Many species that inhabit seasonally ponded wetlands also rely on surrounding upland habitats and nearby aquatic ecosystems for resources to support life stages and to maintain viable populations. Understanding biological connectivity among these habitats is critical to ensure that landscapes are protected at appropriate scales to conserve species and ecosystem function. Biological connectivity occurs across a range of spatial and temporal scales. For example, at annual time scales many organisms move between seasonal wetlands and adjacent terrestrial habitats as they undergo life‐stage transitions; at generational time scales, individuals may disperse among nearby wetlands; and at multigenerational scales, there can be gene flow across large portions of a species’ range. The scale of biological connectivity may also vary among species. Larger bodied or more vagile species can connect a matrix of seasonally ponded wetlands, streams, lakes, and surrounding terrestrial habitats on a seasonal or annual basis. Measuring biological connectivity at different spatial and temporal scales remains a challenge. Here we review environmental and biological factors that drive biological connectivity, discuss implications of biological connectivity for animal populations and ecosystem processes, and provide examples illustrating the range of spatial and temporal scales across which biological connectivity occurs in seasonal wetlands.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12682","usgsCitation":"Smith, L.L., Subalusky, A., Atkinson, C.L., Earl, J.E., Mushet, D.M., Scott, D.E., Lance, S.L., and Johnson, S.A., 2018, Biological connectivity of seasonally ponded wetlands across spatial and temporal scales: Journal of the American Water Resources Association, v. 55, no. 2, p. 334-353, https://doi.org/10.1111/1752-1688.12682.","productDescription":"10 p.","startPage":"334","endPage":"353","ipdsId":"IP-094347","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":488776,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1466083","text":"External Repository"},{"id":356966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-23","publicationStatus":"PW","scienceBaseUri":"5b98a26de4b0702d0e842eaa","contributors":{"authors":[{"text":"Smith, Lora L.","contributorId":207476,"corporation":false,"usgs":false,"family":"Smith","given":"Lora","email":"","middleInitial":"L.","affiliations":[{"id":37541,"text":"Joseph W. Jones Ecological Research Center","active":true,"usgs":false}],"preferred":false,"id":743905,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Subalusky, Amanda","contributorId":207477,"corporation":false,"usgs":false,"family":"Subalusky","given":"Amanda","affiliations":[{"id":36248,"text":"Cary Institute of Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":743906,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Atkinson, Carla L.","contributorId":207478,"corporation":false,"usgs":false,"family":"Atkinson","given":"Carla","email":"","middleInitial":"L.","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":743907,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Earl, Julia E.","contributorId":177320,"corporation":false,"usgs":false,"family":"Earl","given":"Julia","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":743908,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":743904,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, David E. 0000-0002-7925-7452 dscott@usgs.gov","orcid":"https://orcid.org/0000-0002-7925-7452","contributorId":207479,"corporation":false,"usgs":false,"family":"Scott","given":"David","email":"dscott@usgs.gov","middleInitial":"E.","affiliations":[{"id":37542,"text":"Savannah River Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":743909,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lance, Stacey L.","contributorId":207480,"corporation":false,"usgs":false,"family":"Lance","given":"Stacey","email":"","middleInitial":"L.","affiliations":[{"id":37542,"text":"Savannah River Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":743910,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Steve A.","contributorId":205912,"corporation":false,"usgs":false,"family":"Johnson","given":"Steve","email":"","middleInitial":"A.","affiliations":[{"id":37188,"text":"Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32611","active":true,"usgs":false}],"preferred":false,"id":743911,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70198617,"text":"ofr20181131 - 2018 - Research to improve ShakeAlert earthquake early warning products and their utility","interactions":[],"lastModifiedDate":"2018-08-31T09:32:14","indexId":"ofr20181131","displayToPublicDate":"2018-08-30T14:20:57","publicationYear":"2018","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":"2018-1131","title":"Research to improve ShakeAlert earthquake early warning products and their utility","docAbstract":"Earthquake early warning (EEW) is the rapid detection of an earthquake and issuance of an alert or notification to people and vulnerable systems likely to experience potentially damaging ground shaking. The level of ground shaking that is considered damaging is defined by the specific application; for example, manufacturing equipment may experience damage at a lower intensity ground shaking than would cause damage to a building. Along the West Coast of the United States, the warning times for ground shaking could range as high as tens of seconds for moderate levels of ground shaking, or potentially longer, if a lower ground-shaking threshold is used to issue alerts. However, it is not always possible to provide advance warning of ground shaking, particularly for locations close to an earthquake that are most likely to experience very strong ground shaking. EEW alerts may be useful to individuals who can use a few seconds to move to a safe zone and to electromechanical systems that can take automatic actions to reduce damage and injuries. An EEW system, ShakeAlert, has been under development in the United States since 2006. Federal and State governments, as well as the private sector, are now investing in the ShakeAlert prototype system that will, when completed, become an operational public system for the West Coast of the United States.
While the current prototype is delivering alerts to test users, improvements to the accuracy, timeliness, and utility of the alerts are needed. For this reason, it is essential that the ShakeAlert system be continuously improved through targeted research, involving not only the current ShakeAlert partner organizations, but also the broader scientific, engineering, and emergencyresponse communities. To this end, this report describes the opportunities for improvement that can be addressed through research and development over the next 5 years.
Our recommendations are organized into four areas: (1) understand EEW capabilities and user needs, (2) make alerts as fast and accurate as possible, (3) ensure reliability when it counts, and (4) explore the use of new instrumentation.
The first challenge is to understand EEW capabilities and user needs. EEW must deliver actionable information to people and to automated systems to mitigate short- and longterm impacts of damaging ground shaking, so development of EEW must be motivated by the needs of users. Within this challenge, we must study the technical capabilities and limitations of EEW in general, and the ShakeAlert system specifically. This includes development of performance metrics that assess the timeliness and accuracy of alerts to understand the value and utility of the ShakeAlert EEW product(s) for various user groups, including different industry sectors, emergency-management agencies, and the public. Research is needed to define the alerting choices that maximize the utility of the system for users and to determine what the available communication pathways are for providing timely alert information. Additionally, we engage users to assess how alerts will be used by different sectors to mitigate losses and to inform EEW product design. Further, social-science research is needed to develop alert messaging, including what relevant prior and follow-up information are required, to ensure effective use of alerts.
The second challenge is to make alerts as fast and as accurate as possible. The timeliness and accuracy of an EEW alert is important because it will set in motion a series of actions and downstream products. An EEW alert will trigger notification across emergency-alert systems and across multiple communication channels to populations in impacted regions. The EEW alert region may grow as the earthquake fault-rupture length increases, and the EEW system’s characterization of it, evolves. We must continue research into new or improved seismic and geodetic waveform-processing methods necessary to rapidly characterize the expected ground shaking and associated uncertainties. It is important to thoroughly evaluate whether new methods improve alerts through more accurate ground-motion estimates and (or) reduced latencies (that is, longer warning times). New methods could include tracking the extent of a large rupture in real time (known as finite-fault algorithms) and ground-motionbased EEW algorithms. Additionally, ground motion predictions could be optimized for each earthquake as the earthquake fault rupture progresses by using, for example, event terms to shift ground-motion curves for more (or less) energetic ruptures.
The third challenge is to ensure reliability when it counts. This challenge requires us to explore approaches that assess the expected performance of ShakeAlert across the range of earthquake magnitudes, locations, and depths that may occur within the alerting region. Large, damaging earthquakes and their associated aftershock sequences matter most for hazard and for EEW, but these large-earthquake sequences occur infrequently. We expect ShakeAlert to respond robustly to these large-earthquake sequences despite potentially long periods of relative seismic quiescence in the intervening years, and in spite of inevitable communication challenges that arise during and after a large earthquake. We must develop methods to utilize the broadest available datasets to test EEW performance, including ground-motion data recorded in other parts of the world. The observational period for large, damaging earthquakes in any particular region has been short in comparison to estimated large-earthquake recurrence times. Ground-motion records for very large, damaging western United States events and major aftershock sequences do not yet exist, nor do data exist for all potential sources of noise and spurious signals that ShakeAlert must be “tuned” to reject. In addition, robust synthetic data could provide the flexibility to test a wider range of earthquake magnitude, tectonic-setting, and noise scenarios than are covered by existing observational data. Synthetic ground-motion data must be thoroughly vetted against records of smaller magnitude earthquakes to ensure that they accurately capture both the onset and the amplitude of the ground shaking.
The final challenge is to explore the use of new instrumentation. The development of EEW around the world to date has focused on the use of high-quality, scientific-grade seismic and geodetic instrumentation. The use of additional types of instrumentation or information may also improve EEW products by filling gaps in sensor coverage in countries that already have dense seismic networks or enable EEW in countries without such networks. We must keep up with these developments and continuously assess their value in supplementing existing EEW systems, such as ShakeAlert, or enabling EEW where such systems do not exist. Such developments include low-cost instrumentation with microelectromechanical system (MEMS) sensors and global positioning system (GPS)/global navigation satellite system (GNSS) antennas embedded in low-cost consumer electronics, sea-floor seismometers, geodetic instrumentation deployed along the Cascadia and Alaska megathrust margins of western North America, and borehole strainmeters that are already deployed across the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181131","usgsCitation":"Cochran, E.S., Aagaard, B.T., Allen, R.M., Andrews, J., Baltay, A.S., Barbour, A.J., Bodin, P., Brooks, B.A., Chung, A., Crowell, B.W., Given, D.D., Hanks, T.C., Hartog, J.R., Hauksson, E., Heaton, T.H., McBride, S., Meier, M-A., Melgar, D., Minson, S.E., Murray, J.R., Strauss, J.A., and Toomey, D., 2018, Research to improve ShakeAlert earthquake early warning products and their utility: U.S. Geological Survey Open-File Report 2018–1131, 17 p., https://doi.org/10.3133/ofr20181131.","productDescription":"iv, 17 p.","onlineOnly":"Y","ipdsId":"IP-098968","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":356971,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1131/coverthb.jpg"},{"id":356972,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1131/ofr20181131.pdf","text":"Report","size":"600 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2018-1131"}],"contact":"Earthquake Science Center-Pasadena Field Office
U.S. Geological Survey
525 South Wilson Ave.
Pasadena, CA 91106-3212
Great megathrust earthquakes arise from the sudden release of energy accumulated during centuries of interseismic plate convergence. The moment deficit (energy available for future earthquakes) is commonly inferred by integrating the rate of interseismic plate locking over the time since the previous great earthquake. But accurate integration requires knowledge of how interseismic plate locking changes decades after earthquakes, measurements not available for most great earthquakes. Here we reconstruct the post-earthquake history of plate locking at Guafo Island, above the seismogenic zone of the giant 1960 (Mw = 9.5) Chile earthquake, through forward modeling of land-level changes inferred from aerial imagery (since 1974) and measured by GPS (since 1994). We find that interseismic locking increased to ~70% in the decade following the 1960 earthquake and then gradually to 100% by 2005. Our findings illustrate the transient evolution of plate locking in Chile, and suggest a similarly complex evolution elsewhere, with implications for the time- and magnitude-dependent probability of future events.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/s41467-018-05989-6","usgsCitation":"Melnick, D., Li, S., Moreno, M., Cisternas, M., Jara-Munoz, J., Wesson, R.L., Nelson, A.R., Baez, J.C., and Deng, Z., 2018, Back to full interseismic plate locking decades after the giant 1960 Chile earthquake: Nature Communications, v. 9, Article number: 3527; 10 p., https://doi.org/10.1038/s41467-018-05989-6.","productDescription":"Article number: 3527; 10 p.","ipdsId":"IP-098182","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":468462,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-018-05989-6","text":"Publisher Index Page"},{"id":357100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76,\n -48\n ],\n [\n -72,\n -48\n ],\n [\n -72,\n -36\n ],\n [\n -76,\n -36\n ],\n [\n -76,\n -48\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-30","publicationStatus":"PW","scienceBaseUri":"5b98a26de4b0702d0e842eae","contributors":{"authors":[{"text":"Melnick, Daniel","contributorId":195525,"corporation":false,"usgs":false,"family":"Melnick","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":744310,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Li, Shaoyang","contributorId":207597,"corporation":false,"usgs":false,"family":"Li","given":"Shaoyang","email":"","affiliations":[],"preferred":false,"id":744311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moreno, Marcos","contributorId":195527,"corporation":false,"usgs":false,"family":"Moreno","given":"Marcos","email":"","affiliations":[],"preferred":false,"id":744312,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cisternas, Macro","contributorId":198898,"corporation":false,"usgs":false,"family":"Cisternas","given":"Macro","email":"","affiliations":[{"id":34895,"text":"Pontificia Universidad Catolica de Valparaiso","active":true,"usgs":false}],"preferred":false,"id":744313,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jara-Munoz, Julius","contributorId":207598,"corporation":false,"usgs":false,"family":"Jara-Munoz","given":"Julius","email":"","affiliations":[],"preferred":false,"id":744314,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wesson, Robert L. 0000-0003-2702-0012 rwesson@usgs.gov","orcid":"https://orcid.org/0000-0003-2702-0012","contributorId":850,"corporation":false,"usgs":true,"family":"Wesson","given":"Robert","email":"rwesson@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":744315,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nelson, Alan R. 0000-0001-7117-7098 anelson@usgs.gov","orcid":"https://orcid.org/0000-0001-7117-7098","contributorId":812,"corporation":false,"usgs":true,"family":"Nelson","given":"Alan","email":"anelson@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":744316,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Baez, Juan Carlos","contributorId":207599,"corporation":false,"usgs":false,"family":"Baez","given":"Juan","email":"","middleInitial":"Carlos","affiliations":[],"preferred":false,"id":744317,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Deng, Zhiguo","contributorId":207600,"corporation":false,"usgs":false,"family":"Deng","given":"Zhiguo","email":"","affiliations":[],"preferred":false,"id":744318,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70199062,"text":"70199062 - 2018 - Ancient convergent losses of Paraoxonase 1 yield potential risks for modern marine mammals","interactions":[],"lastModifiedDate":"2018-08-30T10:53:58","indexId":"70199062","displayToPublicDate":"2018-08-30T10:53:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Ancient convergent losses of Paraoxonase 1 yield potential risks for modern marine mammals","title":"Ancient convergent losses of Paraoxonase 1 yield potential risks for modern marine mammals","docAbstract":"Mammals diversified by colonizing drastically different environments, with each transition yielding numerous molecular changes, including losses of protein function. Though not initially deleterious, these losses could subsequently carry deleterious pleiotropic consequences. We have used phylogenetic methods to identify convergent functional losses across independent marine mammal lineages. In one extreme case, Paraoxonase 1 (PON1) accrued lesions in all marine lineages, while remaining intact in all terrestrial mammals. These lesions coincide with PON1 enzymatic activity loss in marine species’ blood plasma. This convergent loss is likely explained by parallel shifts in marine ancestors’ lipid metabolism and/or bloodstream oxidative environment affecting PON1’s role in fatty acid oxidation. PON1 loss also eliminates marine mammals’ main defense against neurotoxicity from specific man-made organophosphorus compounds, implying potential risks in modern environments.
Nest attendance during incubation is an important facet of avian nesting behavior, and understanding the number, timing, and duration of incubation recesses can improve our understanding of the factors determining avian reproductive success. Temperature loggers are a low-cost, noninvasive method for studying nest attendance, but processing and interpreting the data present logistical challenges for investigators. We developed an accurate automated method for processing data from temperature loggers to identify incubation recesses. This automated method combines absolute changes in nest temperature over time and changes relative to daily nest-specific variation in temperature to identify incubation recesses. We validated this method through comparison with recesses observed during continuous infrared video monitoring of 3 Mallard (Anas platyrhynchos) and 7 Gadwall (Mareca strepera) nests in northern California, USA. Of 116 recesses observed on camera, we detected 102 (88%) with automated recess detection. After excluding 7 recesses in which nest temperature did not decrease during the recess, and which would therefore have been undetectable without ancillary data, we detected 102 of 109 (94%) recesses with automated recess detection. The time lag in detecting a hen's departure from her nest (i.e. when the recess had begun) was influenced by ambient temperature, although detection of the recess itself was not. The lag in detecting the start of a recess was (mean ± SD) 6.9 ± 2.7 min when ambient temperatures were below 30°C, and 13.7 ± 3.2 min at temperatures above 30°C. The lag in detecting the end of a recess was 1.7 ± 3.2 min and was not affected by ambient temperature. Recesses observed on camera were slightly longer (178.3 ± 122.2 min) than those estimated with automated recess detection (158.7 ± 93.1 min), with the time lag in detecting the start of a recess under warm ambient temperatures contributing the most to the difference. These results demonstrate the accuracy of the automated method that we have developed for identifying the timing and duration of incubation recesses using nest temperature data. This method was developed using data from dabbling ducks but is readily adaptable to other avian taxa with appropriate changes in user-defined criteria for identifying incubation recesses.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-18-6.1","usgsCitation":"Croston, R., Hartman, C.A., Herzog, M.P., Casazza, M.L., and Ackerman, J., 2018, A new approach to automated incubation recess detection using temperature loggers: The Condor, v. 120, no. 4, p. 739-750, https://doi.org/10.1650/CONDOR-18-6.1.","productDescription":"12 p.","startPage":"739","endPage":"750","ipdsId":"IP-097170","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":356948,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"120","issue":"4","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a26ee4b0702d0e842eb2","contributors":{"authors":[{"text":"Croston, Rebecca 0000-0003-4696-0878","orcid":"https://orcid.org/0000-0003-4696-0878","contributorId":206560,"corporation":false,"usgs":true,"family":"Croston","given":"Rebecca","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":743857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hartman, C. Alex 0000-0002-7222-1633 chartman@usgs.gov","orcid":"https://orcid.org/0000-0002-7222-1633","contributorId":131109,"corporation":false,"usgs":true,"family":"Hartman","given":"C.","email":"chartman@usgs.gov","middleInitial":"Alex","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":743858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":743859,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":743860,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":743856,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70199060,"text":"70199060 - 2018 - Contaminants of emerging concern in urban stormwater: Spatiotemporal patterns and removal by iron-enhanced sand filters (IESFs)","interactions":[],"lastModifiedDate":"2018-08-30T10:42:22","indexId":"70199060","displayToPublicDate":"2018-08-30T10:42:19","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Contaminants of emerging concern in urban stormwater: Spatiotemporal patterns and removal by iron-enhanced sand filters (IESFs)","docAbstract":"Numerous contaminants of emerging concern (CECs) typically occur in urban rivers. Wastewater effluents are a major source of many CECs. Urban runoff (stormwater) is a major urban water budget component and may constitute another major CEC pathway. Yet, stormwater-based CEC field studies are rare. This research investigated 384 CECs in 36 stormwater samples in Minneapolis-St. Paul, Minnesota, USA. Nine sampling sites included three large stormwater conveyances (pipes) and three paired iron-enhanced sand filters (IESFs; untreated inlets and treated outlets). The 123 detected compounds included commercial-consumer compounds, veterinary and human pharmaceuticals, lifestyle and personal care compounds, pesticides, and others. Thirty-one CECs were detected in ≥50% of samples. Individual samples contained a median of 35 targeted CECs (range: 18–54). Overall, median concentrations were ≥10 ng/L for 25 CECs and ≥100 ng/L for 9 CECs. Ranked, hierarchical linear modeling indicated significant seasonal- and site type-based concentration variability for 53 and 30 CECs, respectively, with observed patterns corresponding to CEC type, source, usage, and seasonal hydrology. A primarily warm-weather, diffuse, runoff-based profile included many herbicides. A second profile encompassed winter and/or late summer samples enriched with some recalcitrant, hydrophobic compounds (e.g., PAHs), especially at pipes, suggesting conservative, less runoff-dependent sources (e.g., sediments). A third profile, indicative of mixed conservative/non-runoff, runoff, and/or atmospheric sources and transport that collectively affect a variety of conditions, included various fungicides, lifestyle, non-prescription, and commercial-consumer CECs. Generally, pipe sites had large, diverse land-use catchments, and showed more frequent detections of diverse CECs, but often at lower concentrations; while untreated sites (with smaller, more residential-catchments) demonstrated greater detections of “pseudo-persistent” and other ubiquitous or residentially-associated CECs. Although untreated stormwater transports an array of CECs to receiving waters, IESF treatment significantly removed concentrations of 14 (29%) of the 48 most detected CECs; for these, median removal efficiencies were 26%–100%. Efficient removal of some hydrophobic (e.g., PAHs, bisphenol A) and polar-hydrophilic (e.g., caffeine, nicotine) compounds indicated particulate-bound contaminant filtration and for certain dissolved contaminants, sorption.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2018.08.020","usgsCitation":"Fairbairn, D.J., Elliott, S.M., Kiesling, R.L., Schoenfuss, H.L., Ferrey, M.L., and Westerhoff, B., 2018, Contaminants of emerging concern in urban stormwater: Spatiotemporal patterns and removal by iron-enhanced sand filters (IESFs): Water Research, v. 145, p. 332-345, https://doi.org/10.1016/j.watres.2018.08.020.","productDescription":"14 p.","startPage":"332","endPage":"345","ipdsId":"IP-094557","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":356946,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","city":"Minneapolis, St. Paul","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.44696044921875,\n 44.859275967357476\n ],\n [\n -92.95944213867186,\n 44.859275967357476\n ],\n [\n -92.95944213867186,\n 45.08661163034925\n ],\n [\n -93.44696044921875,\n 45.08661163034925\n ],\n [\n -93.44696044921875,\n 44.859275967357476\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"145","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a26ee4b0702d0e842eb4","contributors":{"authors":[{"text":"Fairbairn, David J.","contributorId":207455,"corporation":false,"usgs":false,"family":"Fairbairn","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":13330,"text":"Minnesota Pollution Control Agency","active":true,"usgs":false}],"preferred":false,"id":743862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elliott, Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":743861,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kiesling, Richard L. 0000-0002-3017-1826 kiesling@usgs.gov","orcid":"https://orcid.org/0000-0002-3017-1826","contributorId":1837,"corporation":false,"usgs":true,"family":"Kiesling","given":"Richard","email":"kiesling@usgs.gov","middleInitial":"L.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":743863,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schoenfuss, Heiko L.","contributorId":76409,"corporation":false,"usgs":false,"family":"Schoenfuss","given":"Heiko","email":"","middleInitial":"L.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":743864,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ferrey, Mark L.","contributorId":207457,"corporation":false,"usgs":false,"family":"Ferrey","given":"Mark","email":"","middleInitial":"L.","affiliations":[{"id":13330,"text":"Minnesota Pollution Control Agency","active":true,"usgs":false}],"preferred":false,"id":743865,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Westerhoff, Benjamin J.","contributorId":207458,"corporation":false,"usgs":false,"family":"Westerhoff","given":"Benjamin J.","affiliations":[{"id":20306,"text":"St. Cloud State University","active":true,"usgs":false}],"preferred":false,"id":743866,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70231756,"text":"70231756 - 2018 - Kinematic, deformational, and thermochronologic conditions along the Gossan Lead and Fries shear zones: Constraining the western-eastern Blue Ridge boundary in northwestern North Carolina","interactions":[],"lastModifiedDate":"2022-06-01T15:39:39.634872","indexId":"70231756","displayToPublicDate":"2018-08-30T10:32:40","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Kinematic, deformational, and thermochronologic conditions along the Gossan Lead and Fries shear zones: Constraining the western-eastern Blue Ridge boundary in northwestern North Carolina","docAbstract":"The fault boundary between the western and eastern Blue Ridge (WBR-EBR) in the southern Appalachians separates Mesoproterozoic basement rocks and their cover from Neoproterozoic to Paleozoic accreted rocks. Several northeast striking faults delineate the boundary, including the Gossan Lead shear zone in northwestern North Carolina. Varying tectonic interpretations of WBR-EBR boundary include a premetamorphic fault, an Acadian dextral strike-slip fault, or an Alleghanian fault. We use field-based, microstructural, and theromochronometric analyses to determine the conditions, kinematics, and timing of deformation, in order to distinguish among competing hypotheses for the Gossan Lead shear zone. This comprehensive approach has allowed us to attribute a number of new and previously observed tectonic fabrics to specific orogenic events; key relationships necessary to the study of multiply deformed tectonic margins. Detailed mapping and microstructural analysis of the Gossan Lead shear zone document a several kilometer-wide mylonitic zone with kinematic indicators that record dominantly top-to-the-NW thrust motion, with local strike-slip and normal sense indicators. Dynamically recrystallized quartz and feldspar constrain a range of deformation conditions from amphibolite to greenschist facies. Two unaltered lineation-forming amphiboles from mylonitic amphibolites record 40Ar/39Ar cooling ages of 347–345 Ma, and a mylonitized metagraywacke records a muscovite 40Ar/39Ar cooling age of 336 Ma. These data are consistent with dominantly NW directed thrusting along the Gossan Lead shear zone at amphibolite to greenschist facies conditions, and rapid cooling in the Middle Mississippian. We suggest these data support overprinting and/or reactivation of an earlier structure along this complexly deformed boundary by 336 Ma.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2017TC004879","usgsCitation":"Levine, J.S., Merschat, A.J., McAleer, R.J., Casale, G., Quillan, K.R., Fraser, K.I., and BeDell, T.G., 2018, Kinematic, deformational, and thermochronologic conditions along the Gossan Lead and Fries shear zones: Constraining the western-eastern Blue Ridge boundary in northwestern North Carolina: Tectonics, v. 37, no. 10, p. 3500-3523, https://doi.org/10.1029/2017TC004879.","productDescription":"24 p.","startPage":"3500","endPage":"3523","ipdsId":"IP-098742","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":468463,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017tc004879","text":"Publisher Index Page"},{"id":401052,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Blue Ridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.5,\n 36.40\n ],\n [\n -81.25,\n 36.40\n ],\n [\n -81.25,\n 36.55\n ],\n [\n -81.5,\n 36.550\n ],\n [\n -81.5,\n 36.40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"10","noUsgsAuthors":false,"publicationDate":"2018-10-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Levine, Jamie S. F.","contributorId":292052,"corporation":false,"usgs":false,"family":"Levine","given":"Jamie","email":"","middleInitial":"S. F.","affiliations":[{"id":36626,"text":"Appalachian State University","active":true,"usgs":false}],"preferred":false,"id":843706,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Merschat, Arthur J. 0000-0002-9314-4067 amerschat@usgs.gov","orcid":"https://orcid.org/0000-0002-9314-4067","contributorId":4556,"corporation":false,"usgs":true,"family":"Merschat","given":"Arthur","email":"amerschat@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":843707,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":843708,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Casale, G.","contributorId":292053,"corporation":false,"usgs":false,"family":"Casale","given":"G.","email":"","affiliations":[{"id":36626,"text":"Appalachian State University","active":true,"usgs":false}],"preferred":false,"id":843709,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Quillan, K. R.","contributorId":292054,"corporation":false,"usgs":false,"family":"Quillan","given":"K.","email":"","middleInitial":"R.","affiliations":[{"id":36626,"text":"Appalachian State University","active":true,"usgs":false}],"preferred":false,"id":843710,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fraser, K. I.","contributorId":292055,"corporation":false,"usgs":false,"family":"Fraser","given":"K.","email":"","middleInitial":"I.","affiliations":[{"id":36626,"text":"Appalachian State University","active":true,"usgs":false}],"preferred":false,"id":843711,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"BeDell, T. G.","contributorId":292056,"corporation":false,"usgs":false,"family":"BeDell","given":"T.","email":"","middleInitial":"G.","affiliations":[{"id":36626,"text":"Appalachian State University","active":true,"usgs":false}],"preferred":false,"id":843712,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70216175,"text":"70216175 - 2018 - Sediment fingerprinting to delineate sources of sediment in the agricultural and forested Smith Creek Watershed, Virginia, USA","interactions":[],"lastModifiedDate":"2020-11-09T15:22:20.957875","indexId":"70216175","displayToPublicDate":"2018-08-30T09:22:06","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Sediment fingerprinting to delineate sources of sediment in the agricultural and forested Smith Creek Watershed, Virginia, USA","docAbstract":"The sediment fingerprinting approach was used to apportion fine‐grained sediment to cropland, pasture, forests, and streambanks in the agricultural and forested Smith Creek, watershed, Virginia. Smith Creek is a showcase study area in the Chesapeake Bay watershed, where management actions to reduce nutrients and sediment are being monitored. Analyses of suspended sediment at the downstream and upstream sampling sites indicated streambanks were the major source of sediment (76% downstream and 70% upstream). Current management strategies proposed to reduce sediment loadings for Smith Creek do not target streambanks as a source of sediment, whereas the results of this study indicate that management strategies to reduce sediment loads in Smith Creek may be effective if directed toward managing streambank erosion. The results of this study also highlight the utility of sediment fingerprinting as a management tool to identify sediment sources.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12680","usgsCitation":"Gellis, A.C., and Gorman Sanisaca, L.E., 2018, Sediment fingerprinting to delineate sources of sediment in the agricultural and forested Smith Creek Watershed, Virginia, USA: Journal of the American Water Resources Association, v. 54, no. 6, p. 1197-1221, https://doi.org/10.1111/1752-1688.12680.","productDescription":"25 p.","startPage":"1197","endPage":"1221","ipdsId":"IP-088475","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":437771,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RN36Q1","text":"USGS data release","linkHelpText":"Sediment-sample and climate data for the agricultural and forested parts of Smith Creek watershed, Virginia (2012-2015)"},{"id":380299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Smith Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.2608642578125,\n 38.40194908237822\n ],\n [\n -78.848876953125,\n 38.25543637637947\n ],\n [\n -78.7060546875,\n 38.212288054388175\n ],\n [\n -77.95898437499999,\n 39.01918369029134\n ],\n [\n -78.134765625,\n 39.21523130910491\n ],\n [\n -78.365478515625,\n 39.20671884491848\n ],\n [\n -79.2608642578125,\n 38.40194908237822\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"6","noUsgsAuthors":false,"publicationDate":"2018-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804357,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gorman Sanisaca, Lillian E. 0000-0003-1711-3864","orcid":"https://orcid.org/0000-0003-1711-3864","contributorId":210381,"corporation":false,"usgs":true,"family":"Gorman Sanisaca","given":"Lillian","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804358,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70198992,"text":"70198992 - 2018 - Molecular systematics of swifts of the genus Chaetura (Aves: Apodiformes: Apodidae)","interactions":[],"lastModifiedDate":"2018-08-29T16:07:24","indexId":"70198992","displayToPublicDate":"2018-08-29T16:07:20","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2779,"text":"Molecular Phylogenetics and Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Molecular systematics of swifts of the genus Chaetura (Aves: Apodiformes: Apodidae)","title":"Molecular systematics of swifts of the genus Chaetura (Aves: Apodiformes: Apodidae)","docAbstract":"Phylogenetic relationships among swifts of the morphologically conservative genus Chaetura were studied using mitochondrial and nuclear DNA sequences. Taxon sampling included all species and 21 of 30 taxa (species and subspecies) within Chaetura. Our results indicate that Chaetura is monophyletic and support the division of the genus into the two subgenera previously identified using plumage characters. However, our genetic data, when considered in combination with phenotypic data, appear to be at odds with the current classification of some species of Chaetura. We recommend that C. viridipennis, currently generally treated as specifically distinct from C. chapmani, be returned to its former status as C. chapmani viridipennis, and that C. andrei, now generally regarded as synonymous with C. vauxi aphanes, again be recognized as a valid species. Widespread Neotropical species C. spinicaudus is paraphyletic with respect to more range-restricted species C. fumosa, C. egregia, and C. martinica. Geographically structured genetic variation within some other species of Chaetura, especially notable in C. cinereiventris, suggests that future study may lead to recognition of additional species in this genus. Biogeographic analysis indicated that Chaetura originated in South America and identified several dispersal events to Middle and North America following the formation of the Isthmus of Panama.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ympev.2018.07.006","usgsCitation":"Chesser, T., Vaseghi, H., Hosner, P., Bergner, L.M., Cortes-Rodriguez, M.N., Welch, A., and Collins, C.T., 2018, Molecular systematics of swifts of the genus Chaetura (Aves: Apodiformes: Apodidae): Molecular Phylogenetics and Evolution, v. 128, p. 162-171, https://doi.org/10.1016/j.ympev.2018.07.006.","productDescription":"10 p.","startPage":"162","endPage":"171","ipdsId":"IP-093490","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468464,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ympev.2018.07.006","text":"Publisher Index Page"},{"id":356938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"128","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a26ee4b0702d0e842eb6","contributors":{"authors":[{"text":"Chesser, Terry 0000-0003-4389-7092 tchesser@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-7092","contributorId":177781,"corporation":false,"usgs":true,"family":"Chesser","given":"Terry","email":"tchesser@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":743680,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vaseghi, Haley","contributorId":207387,"corporation":false,"usgs":false,"family":"Vaseghi","given":"Haley","email":"","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":743684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hosner, Peter A.","contributorId":207389,"corporation":false,"usgs":false,"family":"Hosner","given":"Peter A.","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":743686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergner, Laura M.","contributorId":207385,"corporation":false,"usgs":false,"family":"Bergner","given":"Laura","email":"","middleInitial":"M.","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":743681,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cortes-Rodriguez, M. Nandadevi","contributorId":207386,"corporation":false,"usgs":false,"family":"Cortes-Rodriguez","given":"M.","email":"","middleInitial":"Nandadevi","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":743682,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Welch, Andreanna J.","contributorId":79313,"corporation":false,"usgs":false,"family":"Welch","given":"Andreanna J.","affiliations":[],"preferred":false,"id":743683,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Collins, Charles T.","contributorId":207388,"corporation":false,"usgs":false,"family":"Collins","given":"Charles","email":"","middleInitial":"T.","affiliations":[{"id":36956,"text":"California State University","active":true,"usgs":false}],"preferred":false,"id":743685,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70198999,"text":"70198999 - 2018 - Elevated aeolian sediment transport on the Colorado Plateau, USA: The role of grazing, vehicle disturbance, and increasing aridity","interactions":[],"lastModifiedDate":"2018-11-14T09:27:13","indexId":"70198999","displayToPublicDate":"2018-08-29T16:05:09","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Elevated aeolian sediment transport on the Colorado Plateau, USA: The role of grazing, vehicle disturbance, and increasing aridity","docAbstract":"Dryland wind transport of sediment can accelerate soil erosion, degrade air quality, mobilize dunes, decrease water supply, and damage infrastructure. We measured aeolian sediment horizontal mass flux (q) at 100 cm height using passive aspirated sediment traps to better understand q variability on the Colorado Plateau. Measured q‘hot spots’ rival the highest ever recorded including 7,460 g m−2 day−1 in an off‐highway vehicle (OHV) area, but were more commonly 50‐2,000 g m−2 day−1. Overall mean q on rangeland sites was 5.14 g m−2 day−1, considerably lower than areas with concentrated livestock use (9‐19 g m−2 day−1), OHV use (414 g m−2 day−1), and downwind of unpaved roads (13.14 g m−2 day−1), but were higher than areas with minimal soil disturbance (1.60 g m−2 day−1). Rangeland q increased with increasing annual temperature, increased winds, and decreasing precipitation. Spatial modeling suggests that ~92‐93% of regional q occurs in rangelands versus ~7‐8% along unpaved roads. Four of the five largest road qvalues (n=33) measured were along roads used primarily for oil or gas wells. Our findings indicate that predicted future mega‐droughts will increase q disproportionately in disturbed rangelands, and potentially further compromise air quality, hydrologic cycles, and other ecosystem services.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.4457","usgsCitation":"Nauman, T.W., Duniway, M.C., Webb, N.P., and Belnap, J., 2018, Elevated aeolian sediment transport on the Colorado Plateau, USA: The role of grazing, vehicle disturbance, and increasing aridity: Earth Surface Processes and Landforms, v. 43, no. 14, p. 2897-2914, https://doi.org/10.1002/esp.4457.","productDescription":"18 p.","startPage":"2897","endPage":"2914","ipdsId":"IP-091070","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":356937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.23406982421875,\n 37.76202988573211\n ],\n [\n -109.05853271484374,\n 37.76202988573211\n ],\n [\n -109.05853271484374,\n 39.37889504706486\n ],\n [\n -110.23406982421875,\n 39.37889504706486\n ],\n [\n -110.23406982421875,\n 37.76202988573211\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"14","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-13","publicationStatus":"PW","scienceBaseUri":"5b98a26ee4b0702d0e842eb8","contributors":{"authors":[{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":743714,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":743715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Webb, Nichloas P.","contributorId":207393,"corporation":false,"usgs":false,"family":"Webb","given":"Nichloas","email":"","middleInitial":"P.","affiliations":[{"id":37528,"text":"USDA-ARS Jornada Experimental Range, PO Box 30003, MSC 3JER, NMSU, Las Cruces, NM 88003, USA","active":true,"usgs":false}],"preferred":false,"id":743717,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":743716,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197875,"text":"ofr20121024N - 2018 - Geologic framework for the national assessment of carbon dioxide storage resources—Atlantic Coastal Plain and Eastern Mesozoic Rift Basins","interactions":[{"subject":{"id":70197875,"text":"ofr20121024N - 2018 - Geologic framework for the national assessment of carbon dioxide storage resources—Atlantic Coastal Plain and Eastern Mesozoic Rift Basins","indexId":"ofr20121024N","publicationYear":"2018","noYear":false,"chapter":"N","title":"Geologic framework for the national assessment of carbon dioxide storage resources—Atlantic Coastal Plain and Eastern Mesozoic Rift Basins"},"predicate":"IS_PART_OF","object":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"id":1}],"isPartOf":{"id":70093199,"text":"ofr20121024 - 2012 - Geologic framework for the national assessment of carbon dioxide storage resources","indexId":"ofr20121024","publicationYear":"2012","noYear":false,"title":"Geologic framework for the national assessment of carbon dioxide storage resources"},"lastModifiedDate":"2019-02-21T10:54:07","indexId":"ofr20121024N","displayToPublicDate":"2018-08-29T15:45:00","publicationYear":"2018","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":"2012-1024","chapter":"N","title":"Geologic framework for the national assessment of carbon dioxide storage resources—Atlantic Coastal Plain and Eastern Mesozoic Rift Basins","docAbstract":"This chapter presents information pertinent to the geologic carbon dioxide (CO2) sequestration potential within saline aquifers located in the Atlantic Coastal Plain and Eastern Mesozoic Rift Basins of the Eastern United States. The Atlantic Coastal Plain is underlain by a Jurassic to Quaternary succession of sedimentary strata that onlap westward onto strata of the Appalachian Piedmont physiographic province and generally thicken eastward toward the present-day Atlantic coastline and onto the present-day continental shelf. Although no significant petroleum discoveries have been made on the coastal plain, the deep saline aquifers of the region appear to contain porous strata (potential reservoirs, or “storage formations”) that are overlain by fine-grained, laterally continuous strata (potential seals), which are prospective CO2 sequestration targets. For the Atlantic Coastal Plain, we identify two storage assessment units (SAUs), both of which consist of Cretaceous strata. The two SAUs are the Lower Cretaceous Composite SAU C50700101 and the Upper Cretaceous Composite SAU C50700102.
The Eastern Mesozoic Rift Basins are a chain of generally southwest- to northeast-trending, elongate sedimentary basins that either underlie the Atlantic Coastal Plain or crop out within adjacent geologic provinces to the west. Similar to the Atlantic Coastal Plain, there has been no significant oil and gas production from any of the basins, although there is a proven petroleum system in several of them. At least three of these basins appear to contain potential storage formations overlain by potential seal units. Most of the other basins were not assessed because a storage and (or) seal formation could not be established in the timeframe of the assessment, often because of the paucity of subsurface data for these basins in comparison to other petroliferous basins of the United States. Thus, we present information supporting one quantitative assessment in the Newark basin, as well as information supporting two nonquantitative assessments, one for strata in the Gettysburg basin and the other for strata in the Culpeper basin. We briefly discuss six other basins within the Eastern Mesozoic Rift Basins that were not assessed.
For all SAUs, we discuss the areal distribution of suitable CO2 reservoir rock. We also describe the overlying sealing unit and the geologic characteristics that influence the potential CO2 storage volume and reservoir characteristics. These characteristics include storage formation depth, gross thickness, net thickness, porosity, permeability, and groundwater salinity. Case-by-case strategies for estimating the pore volume existing within structurally and (or) stratigraphically closed traps are presented. Although assessment results are not contained in this chapter, the geologic information included herein was used to calculate the potential storage space in the SAUs.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Geologic framework for the national assessment of carbon dioxide storage resources","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121024N","usgsCitation":"Craddock, W.H., Merrill, M.D., Roberts-Ashby, T.L., Brennan, S.T., Buursink, M.L., Drake, R.M., II, Warwick, P.D., Cahan, S.M., DeVera, C.A., Freeman, P.A., Gosai, M.A., and Lohr, C.D., 2018, Geologic framework for the national assessment of carbon dioxide storage resources—Atlantic Coastal Plain and Eastern Mesozoic Rift Basins, chap. N of Warwick, P.D., and Corum, M.D., eds., Geologic framework for the national assessment of carbon dioxide storage resources: U.S. Geological Survey Open-File Report 2012–1024, 32 p., https://doi.org/10.3133/ofr20121024N.","productDescription":"Report: vi, 32 p.; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-082323","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"links":[{"id":356831,"rank":4,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2012/1024/n/ofr20121024n_acp-cell-c5070.zip","text":"Atlantic Coastal Plain Well Density","size":"1.77 GB","linkFileType":{"id":6,"text":"zip"}},{"id":356832,"rank":5,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2012/1024/n/ofr20121024n_emrb_sau-c5068.zip","text":"Eastern Mesozoic Rift Basins Storage Assessment Units","size":"1.77 GB","linkFileType":{"id":6,"text":"zip"}},{"id":356833,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2012/1024/n/ofr20121024n_emrb-cell-c5068.zip","text":"Eastern Mesozoic Rift Basins Well Density","size":"1.77 GB","linkFileType":{"id":6,"text":"zip"}},{"id":356830,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2012/1024/n/ofr20121024n_acp-sau-c5070.zip","text":"Atlantic Coastal Plain Storage Assessment Units","size":"1.77 GB","linkFileType":{"id":6,"text":"zip"}},{"id":356407,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2012/1024/n/coverthb.jpg"},{"id":356408,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1024/n/ofr20121024n.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2012 1024 N"}],"country":"United States","otherGeospatial":"Atlantic Coastal Plain and Eastern Mesozoic Rift Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.72607421875,\n 27.235094607795503\n ],\n [\n -71.34521484375,\n 27.235094607795503\n ],\n [\n -71.34521484375,\n 42.71473218539458\n ],\n [\n -86.72607421875,\n 42.71473218539458\n ],\n [\n -86.72607421875,\n 27.235094607795503\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Energy Resources Program
12201 Sunrise Valley Drive
913 National Center
Reston, VA 20192
Email: gd-energyprogram@usgs.gov
Tree mortality is an important outcome of many forest fires. Extensive tree injuries from fire may lead directly to mortality, but environmental and biological stressors may also contribute to tree death. However, there is little evidence showing how the combined effects of two common stressors, drought and competition, influence post‐fire mortality. Geographically broad observations of three common western coniferous trees subjected to prescribed fire showed the likelihood of post‐fire mortality was related to intermediate‐term (10 yr) pre‐fire average radial growth, an important component of tree vigor. Path analysis showed that indices of competition and drought stress prior to fire can be described in terms of joint effects on growth, indirectly affecting post‐fire mortality. Our results suggest that the conditions that govern the relationship between growth and mortality in unburned stands may also apply to post‐fire environments. Thus, biotic and abiotic changes that affect growth negatively (e.g., drought stress) or positively (e.g., growth releases following thinning treatments) prior to fire may influence expressed fire severity, independent of fire intensity (e.g., heat flux, residence time). These relationships suggest that tree mortality may increase under stressful climatic or stand conditions even if fire behavior remains constant.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1778","usgsCitation":"van Mantgem, P.J., Falk, D.A., Williams, E.C., Das, A., and Stephenson, N.L., 2018, Pre‐fire drought and competition mediate post‐fire conifer mortality in western U.S. National Parks: Ecological Applications, v. 28, no. 7, p. 1730-1739, https://doi.org/10.1002/eap.1778.","productDescription":"10 p.","startPage":"1730","endPage":"1739","ipdsId":"IP-089928","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":498951,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.1778","text":"Publisher Index Page"},{"id":437772,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JH3JXM","text":"USGS data release","linkHelpText":"Fire Caused Tree Mortality in Western US National Parks (2018)(ver. 2.0, February 2020)"},{"id":356932,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"7","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-27","publicationStatus":"PW","scienceBaseUri":"5b98a26fe4b0702d0e842ebc","contributors":{"authors":[{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422 pvanmantgem@usgs.gov","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":2838,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip","email":"pvanmantgem@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":743737,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falk, Donald A.","contributorId":197570,"corporation":false,"usgs":false,"family":"Falk","given":"Donald","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":743738,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Emma C.","contributorId":207401,"corporation":false,"usgs":false,"family":"Williams","given":"Emma","email":"","middleInitial":"C.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":743739,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Das, Adrian J. 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":3842,"corporation":false,"usgs":true,"family":"Das","given":"Adrian J.","email":"adas@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":743740,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stephenson, Nathan L. 0000-0003-0208-7229 nstephenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0208-7229","contributorId":2836,"corporation":false,"usgs":true,"family":"Stephenson","given":"Nathan","email":"nstephenson@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":743741,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227930,"text":"70227930 - 2018 - Survival of whirling disease resistant rainbow trout fry in the wild: A comparison of two strains","interactions":[],"lastModifiedDate":"2022-02-02T21:48:04.937987","indexId":"70227930","displayToPublicDate":"2018-08-29T15:38:32","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2177,"text":"Journal of Aquatic Animal Health","active":true,"publicationSubtype":{"id":10}},"title":"Survival of whirling disease resistant rainbow trout fry in the wild: A comparison of two strains","docAbstract":"Introduced pathogens can affect fish populations, and three main factors affect disease occurrence: the environment, host, and pathogen. Manipulating at least one of these factors is necessary for controlling disease. Myxobolus cerebralis, the parasite responsible for salmonid whirling disease, became established in Colorado during the 1990s and caused significant declines in wild Rainbow Trout Oncorhynchus mykiss populations. Attempts to re-establish Rainbow Trout have focused on manipulating salmonid host resistance. A Rainbow Trout strain known as GR × CRR was developed for stocking in Colorado by crossing a whirling-disease-resistant strain known as the German Rainbow Trout (GR) with the Colorado River Rainbow Trout (CRR). The GR × CRR fish exhibit resistance similar to that shown by GR, and survival and reproduction were expected to be similar to those of CRR. One disadvantage of stocking GR × CRR is that outcrossing and backcrossing could decrease resistance, and laboratory studies have indicated that this can occur. A potential disadvantage of stocking pure GR is lower survival due to domestication. To compare fry survival between the strains, a field experiment was conducted in 1.6-km reaches of nine Colorado streams. Each stream was stocked in August 2014 with 5,000 GR × CRR and 5,000 GR individuals. In October 2014, April 2015, and August 2015, apparent survival was assessed. Two laboratory predation experiments were also conducted. The field experiment revealed that short-term apparent survival was influenced by stream, and growth rate was influenced by strain and stream. However, after 12 months, there was no difference in apparent survival or growth rate between the GR and GR × CRR strains. Laboratory experiments showed that survival did not differ between the strains when confronted with Brown Trout Salmo trutta predation. Our results indicate that the GR strain is a viable option for stocking in streams where M. cerebralis is enzootic. Further evaluation is needed to determine whether GR fish will survive to maturity and reproduce.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/aah.10040","usgsCitation":"Avila, B.W., Winkelman, D.L., and Fetherman, E.R., 2018, Survival of whirling disease resistant rainbow trout fry in the wild: A comparison of two strains: Journal of Aquatic Animal Health, v. 30, no. 4, p. 280-290, https://doi.org/10.1002/aah.10040.","productDescription":"11 p.","startPage":"280","endPage":"290","ipdsId":"IP-094925","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395315,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"4","noUsgsAuthors":false,"publicationDate":"2018-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Avila, Brian W.","contributorId":172211,"corporation":false,"usgs":false,"family":"Avila","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":17860,"text":"Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":832762,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winkelman, Dana L. 0000-0002-5247-0114 danaw@usgs.gov","orcid":"https://orcid.org/0000-0002-5247-0114","contributorId":4141,"corporation":false,"usgs":true,"family":"Winkelman","given":"Dana","email":"danaw@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fetherman, Eric R.","contributorId":15096,"corporation":false,"usgs":true,"family":"Fetherman","given":"Eric","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":832763,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199008,"text":"70199008 - 2018 - Juvenile Chinook salmon and forage fish use of eelgrass habitats in a diked and channelized Puget Sound River Delta","interactions":[],"lastModifiedDate":"2018-08-29T15:31:23","indexId":"70199008","displayToPublicDate":"2018-08-29T15:31:18","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"title":"Juvenile Chinook salmon and forage fish use of eelgrass habitats in a diked and channelized Puget Sound River Delta","docAbstract":"Eelgrass Zostera marina can form extensive meadows on Puget Sound river deltas. The extent to which these meadows provide critical rearing habitat for local estuarine fishes, especially out‐migrating juvenile salmon, is not well understood. Further, delta eelgrass has been impacted by diking and river channelization with unknown consequences for fish. We sampled fish in the Skagit River delta, Washington, during April–September with a lampara net, which is well suited to capturing fish in eelgrass. We compared abundance and body size of Chinook Salmon Oncorhynchus tshawytscha and three forage fish species between eelgrass and nearby unvegetated habitat. We also assessed combined effects of eelgrass characteristics (meadow size and morphology) and oceanographic conditions (temperature and salinity), which covaried according to proximity and orientation to channelized distributary outlets, diked shorelines, and a jetty. Chinook Salmon were more abundant in eelgrass than in unvegetated habitat in June–July and were relatively more abundant in eelgrass compared with unvegetated habitat in regions with intact eelgrass than offshore from a channelized distributary outlet. Abundances of Pacific Herring Clupea pallasii and Shiner Perch Cymatogaster aggregata were consistently severalfold higher in eelgrass than in unvegetated habitat. Surf Smelt Hypomesus pretiosus were more abundant in eelgrass than in unvegetated habitat at some locations, but never less abundant in eelgrass. Our results suggest that conservation and restoration of delta eelgrass would benefit these species and help to identify the settings in which these actions would be most beneficial.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10035","usgsCitation":"Rubin, S., Hayes, M.C., and Grossman, E., 2018, Juvenile Chinook salmon and forage fish use of eelgrass habitats in a diked and channelized Puget Sound River Delta: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 10, no. 4, p. 435-451, https://doi.org/10.1002/mcf2.10035.","productDescription":"17 p.","startPage":"435","endPage":"451","ipdsId":"IP-092537","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":468465,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10035","text":"Publisher Index Page"},{"id":437773,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YMXJAF","text":"USGS data release","linkHelpText":"Data collected in 2008-2010 to evaluate juvenile salmon and forage fish use of eelgrass on the Skagit River Delta, Washington State, USA"},{"id":356929,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Skagit Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.6,\n 48.25\n ],\n [\n -122.3,\n 48.25\n ],\n [\n -122.3,\n 48.45\n ],\n [\n -122.6,\n 48.45\n ],\n [\n -122.6,\n 48.25\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-09","publicationStatus":"PW","scienceBaseUri":"5b98a26fe4b0702d0e842ebe","contributors":{"authors":[{"text":"Rubin, Stephen P. 0000-0003-3054-7173 srubin@usgs.gov","orcid":"https://orcid.org/0000-0003-3054-7173","contributorId":189436,"corporation":false,"usgs":true,"family":"Rubin","given":"Stephen P.","email":"srubin@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":743754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayes, Michael C. 0000-0002-9060-0565 mhayes@usgs.gov","orcid":"https://orcid.org/0000-0002-9060-0565","contributorId":3017,"corporation":false,"usgs":true,"family":"Hayes","given":"Michael","email":"mhayes@usgs.gov","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":743755,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grossman, Eric E. 0000-0003-0269-6307 egrossman@usgs.gov","orcid":"https://orcid.org/0000-0003-0269-6307","contributorId":2334,"corporation":false,"usgs":true,"family":"Grossman","given":"Eric E.","email":"egrossman@usgs.gov","affiliations":[],"preferred":false,"id":743756,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70200889,"text":"70200889 - 2018 - Using United States Geological Survey stream gages to predict flow and temperature conditions to maintain freshwater mussel habitat","interactions":[],"lastModifiedDate":"2018-11-14T15:22:08","indexId":"70200889","displayToPublicDate":"2018-08-29T15:23:25","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Using United States Geological Survey stream gages to predict flow and temperature conditions to maintain freshwater mussel habitat","docAbstract":"Habitat conditions necessary to support freshwater mussels can be difficult to characterize and predict, particularly for rare or endangered species such as the federally endangered dwarf wedgemussel, Alasmidonta heterodon. In this study, we evaluate flow and temperature conditions in three areas of the mainstem Delaware River known to consistently support A. heterodon, and we develop predictive models using the U.S. Geological Survey (USGS) stream gages and thermal stations in order to identify conditions under which habitat alteration could threaten the species. Flow and temperature prediction models based on nearby existing USGS gage and thermal stations were predictive for all three sites. Both discharge prediction and water depth profile models indicate one location (Site 3) was the most vulnerable to low‐flow conditions as it requires the highest discharge rate (26.3 cms) at the USGS Callicoon gage to maintain both the full wetted perimeter (Pfull) and minimal wetted perimeter (Pmin) and prevent occlusion of areas that contain A. heterodon. Flow management targets aimed at protecting Site 3 should also protect Sites 1 and 2. Although analyses indicated significant benthic habitat available in all three sites even under low discharge rates, specific mussel locations could be vulnerable to dewatering and thermal stress if only Pmin values were maintained. Results indicate the magnitude of site temperature deviations from thermal stations varied by site and river temperature. In general, our results suggest that existing temperature and stream gage infrastructure may be used predictively to evaluate the effects of different flow targets on mainstem Delaware River A. heterodon habitat.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3326","usgsCitation":"Cole, J.C., Townsend, P.A., Eshleman, K.N., St. John White, B., Galbraith, H.S., and Lellis, W.A., 2018, Using United States Geological Survey stream gages to predict flow and temperature conditions to maintain freshwater mussel habitat: River Research and Applications, v. 34, no. 8, p. 977-992, https://doi.org/10.1002/rra.3326.","productDescription":"15 p.","startPage":"977","endPage":"992","ipdsId":"IP-086457","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":437774,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WS8S6V","text":"USGS data release","linkHelpText":"Site bathymetry, water temperature and rating curve 2004 and 2005 data for 3 sites in the Delaware River mainstem"},{"id":359432,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Delaware River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.5255126953125,\n 41.3500103516271\n ],\n [\n -74.5477294921875,\n 41.3500103516271\n ],\n [\n -74.5477294921875,\n 42.429538632268276\n ],\n [\n -75.5255126953125,\n 42.429538632268276\n ],\n [\n -75.5255126953125,\n 41.3500103516271\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"8","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-29","publicationStatus":"PW","scienceBaseUri":"5bed4274e4b0b3fc5cf91c8e","contributors":{"authors":[{"text":"Cole, Jeffrey C. 0000-0002-2477-7231 jccole@usgs.gov","orcid":"https://orcid.org/0000-0002-2477-7231","contributorId":5585,"corporation":false,"usgs":true,"family":"Cole","given":"Jeffrey","email":"jccole@usgs.gov","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":751069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Townsend, Phillip A. 0000-0001-7003-8774","orcid":"https://orcid.org/0000-0001-7003-8774","contributorId":210594,"corporation":false,"usgs":false,"family":"Townsend","given":"Phillip","email":"","middleInitial":"A.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":751070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eshleman, Keith N.","contributorId":210596,"corporation":false,"usgs":false,"family":"Eshleman","given":"Keith","email":"","middleInitial":"N.","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":751071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"St. John White, Barbara 0000-0001-8131-0534 bwhite@usgs.gov","orcid":"https://orcid.org/0000-0001-8131-0534","contributorId":141183,"corporation":false,"usgs":false,"family":"St. John White","given":"Barbara","email":"bwhite@usgs.gov","affiliations":[],"preferred":false,"id":751072,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Galbraith, Heather S. 0000-0003-3704-3517","orcid":"https://orcid.org/0000-0003-3704-3517","contributorId":204518,"corporation":false,"usgs":true,"family":"Galbraith","given":"Heather","email":"","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":751073,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lellis, William A. 0000-0001-7806-2904 wlellis@usgs.gov","orcid":"https://orcid.org/0000-0001-7806-2904","contributorId":2369,"corporation":false,"usgs":true,"family":"Lellis","given":"William","email":"wlellis@usgs.gov","middleInitial":"A.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":751074,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70199020,"text":"70199020 - 2018 - Sensitivity of mangrove range limits to climate variability","interactions":[],"lastModifiedDate":"2018-08-29T15:22:30","indexId":"70199020","displayToPublicDate":"2018-08-29T15:22:07","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of mangrove range limits to climate variability","docAbstract":"Aim
Correlative distribution models have been used to identify potential climatic controls of mangrove range limits, but there is still uncertainty about the relative importance of these factors across different regions. To provide insights into the strength of climatic control of different mangrove range limits, we tested whether temporal variability in mangrove abundance increases near range limits and whether this variability is correlated with climatic factors thought to control large‐scale mangrove distributions.
Location
North and South America.
Time period
1984–2011.
Major taxa studied
Avicennia germinans, Avicennia schuaeriana, Rhizophora mangle, Laguncularia racemosa.
Methods
We characterized temporal variability in the enhanced vegetation index (EVI) at mangrove range limits using Landsat satellite imagery collected between 1984–2011. We characterized greening trends at each range limit, examined variability in EVI along latitudinal gradients near each range limit, and assessed correlations between changes in EVI and temperature and precipitation.
Results
Spatial variability in mean EVI was generally correlated with temperature and precipitation, but the relationships were region specific. Greening trends were most pronounced at range limits in eastern North America. In these regions variability in EVI increased toward the range limit and was sensitive to climatic factors. In contrast, EVI at range limits on the Pacific coast of North America and both coasts of South America was relatively stable and less sensitive to climatic variability.
Main conclusions
Our results suggest that range limits in eastern North America are strongly controlled by climate factors. Mangrove expansion in response to future warming is expected to be rapid in regions that are highly sensitive to climate variability (e.g. eastern North America), but the response in other range limits (e.g. South America) is likely to be more complex and modulated by additional factors such as dispersal limitation, habitat constraints, and/or changing climatic means rather than just extremes.
David A. Johnston Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court, Building 10, Suite 100
Vancouver, Washington, 98683-9589
The tamarisk leaf beetle (Diorhabda carinulata), introduced from Eurasia in 2001 as a biological control agent for the invasive plant Tamarix ramosissima, has spread widely throughout the western USA. With D. carinulata now very abundant, scientists and restoration managers have questioned what influence this introduced arthropod might have upon the avian component of riparian ecosystems. From 2009 through 2012 we studied the consequences of biological invasions of the introduced tamarisk shrub and tamarisk leaf beetles on the diets of native birds along the Dolores River in southwestern Colorado, USA. We examined avian foraging behavior, sampled the arthropod community, documented bird diets and the use of invasive tamarisk shrubs and tamarisk leaf beetles by birds. We documented D. carinulataabundance, on what plants the beetles occurred, and to what degree they were consumed by birds as compared to other arthropods. We hypothesized that if D. carinulata is an important new avian food source, birds should consume beetles at least in proportion to their abundance. We also hypothesized that birds should forage more in tamarisk in the late summer when tamarisk leaf beetle larvae are more abundant than in early summer, and that birds should select beetle-damaged tamarisk shrubs. We found that D. carinulata composed 24.0 percent (± 19.9–27.4%) and 35.4% biomass of all collected arthropods. From the gut contents of 188 birds (25 passerine species), only four species (n = 11 birds) contained tamarisk leaf beetle parts. Although D. carinulata comprised one-quarter of total insect abundance, frequency of occurrence in bird gut contents was only 2.1% by abundance and 3.4% biomass. Birds used tamarisk shrubs for foraging in proportion to their availability, but foraging frequency did not increase during the late summer when more tamarisk leaf beetles were present and birds avoided beetle-damaged tamarisk shrubs. Despite D. carinulata being the most abundant arthropod in the environment, these invasive beetles were not frequently consumed by birds and seem not to provide a significant contribution to avian diets.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-018-1764-6","usgsCitation":"van Riper, C., Puckett, S.L., and Darrah, A.J., 2018, Influences of the invasive tamarisk leaf beetle (Diorhabda carinulata) on avian diets along the Dolores River in Southwestern Colorado USA: Biological Invasions, v. 20, no. 11, p. 3145-3159, https://doi.org/10.1007/s10530-018-1764-6.","productDescription":"15 p.","startPage":"3145","endPage":"3159","ipdsId":"IP-068939","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":356926,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Dolores River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.017333984375,\n 38.016722066763116\n ],\n [\n -108.81271362304688,\n 38.016722066763116\n ],\n [\n -108.81271362304688,\n 38.34165619279595\n ],\n [\n -109.017333984375,\n 38.34165619279595\n ],\n [\n -109.017333984375,\n 38.016722066763116\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-05","publicationStatus":"PW","scienceBaseUri":"5b98a270e4b0702d0e842ec4","contributors":{"authors":[{"text":"van Riper, Charles III 0000-0003-1084-5843 charles_van_riper@usgs.gov","orcid":"https://orcid.org/0000-0003-1084-5843","contributorId":169488,"corporation":false,"usgs":true,"family":"van Riper","given":"Charles","suffix":"III","email":"charles_van_riper@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":743827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Puckett, Sarah L.","contributorId":207425,"corporation":false,"usgs":false,"family":"Puckett","given":"Sarah","email":"","middleInitial":"L.","affiliations":[{"id":36671,"text":"School of Natural Resources and the Environment, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":743828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Darrah, Abigail J.","contributorId":207426,"corporation":false,"usgs":false,"family":"Darrah","given":"Abigail","email":"","middleInitial":"J.","affiliations":[{"id":37538,"text":"Audubon Mississippi, 5009 Main Street, Moss Point, MS 39563","active":true,"usgs":false}],"preferred":false,"id":743829,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70198877,"text":"ofr20181138 - 2018 - A bioassay assessment of a zebra mussel (Dreissena polymorpha) eradication treatment","interactions":[],"lastModifiedDate":"2018-08-30T09:44:32","indexId":"ofr20181138","displayToPublicDate":"2018-08-29T13:45:00","publicationYear":"2018","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":"2018-1138","displayTitle":"A bioassay assessment of a zebra mussel (Dreissena polymorpha) eradication treatment","title":"A bioassay assessment of a zebra mussel (Dreissena polymorpha) eradication treatment","docAbstract":"Zebra mussels (Dreissena polymorpha, Pallas, 1771) are an aquatic invasive species in the
United States, and new infestations of zebra mussels can rapidly expand into dense colonies. Zebra
mussels were first reported in Marion Lake, Dakota County, Minnesota, in September 2017, and
surveys indicated the infestation was likely isolated near a public boat access. A 2.4-hectare area
containing the known zebra mussel infestation was enclosed and treated by area resource managers for
9 days with EarthTec QZ (target concentration: 0.5 milligrams per liter as copper), a copper-based
molluscicide, to eradicate the zebra mussels. Researchers led an onsite bioassay to provide an estimate
of the treatment efficacy within the enclosure. The bioassay was conducted in a mobile assay trailer that
received a continuous flow of treated lake water. Bioassay tanks (n=9; 350 liters) within the trailer were
stocked with zebra mussels (25 mussels per containment bag; 7 bags per tank) collected from White
Bear Lake, Ramsey County, Minn. Mortality in the treated bioassay tanks reached a mean of 99 percent
(95-percent confidence interval: 98–100 percent), there were no mortalities in the control tanks.
However, a predictive model produced for timely delivery to area resource managers indicated zebra
mussel mortality within the treated enclosure may have been as low as 85 percent. Onsite bioassays are
a viable and important tool for treatment evaluation particularly in newly infested waterbodies with low
zebra mussel densities.
Director, Upper Midwest Environmental Science Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
Mercury (Hg) analyses were conducted on samples of water and sport fish collected from selected sampling sites in the Boise and Snake Rivers and Brownlee Reservoir, in Idaho and Oregon, to meet National Pollution Discharge and Elimination System permit requirements for the City of Boise, Idaho, from 2013 to 2017. City of Boise personnel collected water samples from six sites in October and November of 2013, 2015 and 2017, and sampled one site in 2014 and 2016. Total Hg concentrations in unfiltered water samples ranged from 0.41 to 8.78 nanograms per liter (ng/L), with the highest value (8.78 ng/L) observed in Brownlee Reservoir in 2013. All samples were less than the U.S. Environmental Protection Agency aquatic life criterion of 12 ng/L.
Individual fillets of mountain whitefish (Prosopium williamsoni), rainbow trout (Oncorhynchus mykiss), smallmouth bass (Micropterus dolomieu), and channel catfish (Ictalurus punctatus) were collected and analyzed for Hg. The tissue Hg concentrations were compared with regulatory or advisory values for wet-weight methylmercury in fish tissue. In this report, methylmercury concentrations in fish tissue are considered similar to total Hg in fish muscle tissue and are simply referred to as Hg. The 2013 average Hg concentration for smallmouth bass (0.32 mg/kg) collected at Brownlee Reservoir and for channel catfish (0.33 mg/kg) collected at the Boise River mouth, exceeded the Idaho water quality criterion (>0.3 mg/kg). The 2017 Hg concentrations in smallmouth bass from Brownlee Reservoir (geometric mean of 0.22 mg/kg) was at the Idaho Fish Consumption Advisory Program action level.
Selenium (Se) interacts with Hg to reduce the health risks of Hg, such that tissues with Se-to-Hg molar ratios greater than 1 are considered to present less potential health risks for a given Hg concentration than are tissues with lower Se-to-Hg ratios. One composite fish tissue sample per site was analyzed for Se. Selenium-to-Hg molar ratios in the fish tissue samples ranged from 0.99 to 24.7.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181122","collaboration":"Prepared in cooperation with the City of Boise, Idaho","usgsCitation":"MacCoy, D.E., and Mebane, C.A., 2018, Mercury concentrations in water and mercury and selenium concentrations in fish from Brownlee Reservoir and selected sites in the Boise and Snake Rivers, Idaho and Oregon, 2013-17: U.S. Geological Survey Open-File Report 2018-1122, 37 p., https://doi.org/10.3133/ofr20181122.","productDescription":"iv, 37 p.","onlineOnly":"Y","ipdsId":"IP-091972","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":356923,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1122/coverthb.jpg"},{"id":356924,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1122/ofr20181122.pdf","text":"Report","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1122"}],"country":"United States","state":"Idaho, Oregon","otherGeospatial":"Boise River, Brownlee Reservoir, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.27081298828125,\n 43.241201214257885\n ],\n [\n -116.0540771484375,\n 43.241201214257885\n ],\n [\n -116.0540771484375,\n 44.40827836571936\n ],\n [\n -117.27081298828125,\n 44.40827836571936\n ],\n [\n -117.27081298828125,\n 43.241201214257885\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
An understanding of riverine water-quality dynamics in regional mixed-land use watersheds is the foundation for advances in landscape biogeochemistry and informed land management. A differential implementation of the statistical/process-based model SPAtially Referenced Regressions on Watershed attributes (SPARROW; Smith et al., https://doi.org/10.1029/97wr02171) is proposed to empirically relate a regional pattern of changes in flow-normalized constituent flux, over a multiyear period, to contemporaneous changes in spatially referenced explanatory variables. In a pilot application, the differential model, called Spatiotemporal Watershed Accumulation of Net effects (SWAN), is used to explore factors influencing changes in flow-normalized flux of total nitrogen over the period 1990–2010 at 43 sites in the nontidal Chesapeake Bay watershed. A seven-parameter model explains 80% of the transformed variability in independently estimated flux changes, indicating that storage effects having characteristic time scales greater than 20 years had a small influence, relative to changes in inputs, on regional water-quality response. Results suggest that 1990–2010 changes in total-nitrogen flux are largely the outcome of increased nonpoint-source pollution associated with urban and suburban development, modulated to the point of negation by terrestrial losses stemming from widespread increases in air temperature and precipitation. The loss mechanism is qualitatively consistent with denitrification; however, increases in aboveground biomass, agricultural nitrogen exports, or hydrologic flushing are also plausible contributors. Although qualified by a small sample size and constraints on explanatory data availability, the pilot suggests that SWAN is a promising approach for broadening scientific understanding of factors driving regional water-quality change and for supporting evidence-based land-management decisions.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2017WR022403","usgsCitation":"Chanat, J.G., and Yang, G., 2018, Exploring drivers of regional water-quality change using differential spatially referenced regression – A pilot study in the Chesapeake Bay watershed: Water Resources Research, v. 54, no. 10, p. 8120-8145, https://doi.org/10.1029/2017WR022403.","productDescription":"26 p.","startPage":"8120","endPage":"8145","ipdsId":"IP-093045","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":468466,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017wr022403","text":"Publisher Index Page"},{"id":390388,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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0000-0001-5587-3683","orcid":"https://orcid.org/0000-0001-5587-3683","contributorId":267279,"corporation":false,"usgs":false,"family":"Yang","given":"Guoxiang","affiliations":[{"id":55459,"text":"NSA Contractor to USGS VA and WV WSC","active":true,"usgs":false}],"preferred":false,"id":824898,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70198358,"text":"ofr20181123 - 2018 - Effects of proposed navigation channel improvements on sediment transport in Mobile Harbor, Alabama","interactions":[],"lastModifiedDate":"2018-08-29T14:58:16","indexId":"ofr20181123","displayToPublicDate":"2018-08-29T08:30:00","publicationYear":"2018","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":"2018-1123","title":"Effects of proposed navigation channel improvements on sediment transport in Mobile Harbor, Alabama","docAbstract":"A Delft3D model was developed to evaluate the potential effects of proposed navigation
channel deepening and widening in Mobile Harbor, Alabama. The model performance was
assessed through comparisons of modeled and observed data of water levels, velocities, and bed
level changes; the model captured hydrodynamic and sediment transport patterns in the study
area with skill. The validated model was used to simulate changes in sediment transport for existing
conditions and with the proposed modifications to the navigational channel (with-project),
with and without accounting for 0.5 meter (m) of sea level rise (SLR). Each scenario was simulated
for 1 year with a wave climatology representative of the year 2010 as well as for 10 years
with a longer-term wave climatology spanning from 1988 to 2016. Bed level differences for the
existing and with-project 2010 simulations were minimal, ranging from −0.11 to 0.11 m offshore
of Pelican Island and −0.81 to 0.22 m offshore of the Fort Morgan Peninsula. For the simulations
accounting for 0.5 m of SLR, differences in bed levels from −0.20 to 0.32 m near Pelican
Island and −0.38 to 0.34 m offshore of the Fort Morgan Peninsula. The proposed modifications
reduced the channel shoaling volume by 4.77 and 8.09 percent for the 2010 simulations without
and with 0.5 m of SLR, respectively. For the 10-year simulations, bed level differences for the
existing and with-project simulations ranged from −3.17 to 3.94 m for the simulation without
SLR and −1.92 to 1.47 m for the simulation with 0.5 m of SLR. The with-project condition reduced
the entrance channel shoaling volume by 5.54 percent for the simulation without SLR and
14.98 percent for the simulation with 0.5 m of SLR.
Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
In forested landscapes, creation of habitat for early-successional shrubland birds is controversial because of perceived conflicts with the conservation of mature-forest birds. Nonetheless, many mature-forest birds, especially fledglings, readily use early-successional stands during the post-breeding period. This suggests that for mature-forest birds, creating habitat for early-successional birds could involve a tradeoff: reduced abundance and nest survival due to the loss of nesting habitat versus enhanced fledgling survival in early-successional stands. Our research addressed the effects of the creation of early-successional habitat for shrubland birds on wood thrushes (Hylocichla mustelina) in western Massachusetts, USA. We compared wood thrush abundance, nest success, fecundity, and post-fledging survival in landscapes with high (∼20%) or low (∼1%) cover of early-successional stands suitable for shrubland birds. We found no differences in nest success, fecundity, and post-fledging survival between the 2 types of landscapes. Abundance of breeders, however, was significantly greater on the sites with high cover of early-successional habitat. We conclude that in forested landscapes, creation of early-successional habitat at levels recommended for the conservation of shrubland birds is compatible with viable wood thrush populations. © 2018 The Wildlife Society.
Sea-Bird Scientific’s HydroCycle-PO4 phosphate sensor is a single-analyte wet-chemistry sensor designed for in situ environmental monitoring. The unit was evaluated at the U.S. Geological Survey Hydrologic Instrumentation Facility to assess the accuracy of the sensor in solutions with known phosphorous concentration and to test the effects of chromophoric (colored) dissolved organic matter (CDOM) and natural water matrixes on sensor accuracy. Accuracy was tested with three standards: 0.110, 0.174, and 0.260 milligram per liter, as phosphorous (mg/L as P). The 0.110- and 0.260-mg/L standards were made from a dilution of a National Institute of Standards and Technology-traceable phosphate-phosphorous standard with Type I deionized water (DIW). Average measured phosphate concentrations of the tested standards (0.110, 0.174, and 0.260 mg/L as P) in DIW were 0.132, 0.181, and 0.310 mg/L as P, for differences of 20, 4, and 19 percent, respectively.
Measured phosphate concentration of a tested standard was biased by the addition of tea water filtered through a 0.45-micrometer pore size filter (filtered tea water [FTW]) simulating the effect of CDOM. An aliquot of the filtered tea solution was sent to a certified environmental laboratory, which reported a less than reporting level (<0.004 mg/L as P) phosphate concentration. True color of the FTW was measured at 380 platinum-cobalt units by using Standard Methods 8025. For this FTW test, the measured phosphate concentration for the clear 0.260 mg/L as P standard averaged 0.366 mg/L as P. This concentration increased to an average of 0.653 mg/L as P with the addition of 10 percent FTW, and to an average of 0.859 mg/L as P with the addition of 20 percent FTW. These test results indicate a positive bias of up to 40 percent of the concentrations of the measured phosphate concentrations when CDOM is present and indicate a proportional increase in an apparent concentration of phosphorus instrument response as CDOM concentration increases.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181120","usgsCitation":"Snazelle, T.T., 2018, Laboratory evaluation of the Sea-Bird Scientific HydroCycle-PO4 phosphate sensor: U.S. Geological Survey Open-File Report 2018–1120, 10 p., https://doi.org/ofr20181120.","productDescription":"vi, 10 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-090916","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":356807,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1120/coverthb5.jpg"},{"id":356711,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1120/ofr20181120.pdf","text":"Report","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1120"}],"contact":"Chief, Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
Stennis Space Center, MS 39529
On October 21, 1868, a magnitude 6.8 earthquake struck the San Francisco Bay area. Although the region was sparsely populated, the quake on the Hayward Fault was one of the most destructive in California’s history. U.S. Geological Survey (USGS) studies show that similar Hayward Fault quakes have repeatedly jolted the region in the past and that the fault may be ready to produce another magnitude 6.8 to 7.0 earthquake. Such an earthquake could unexpectedly change people’s lives and impact the Bay Area’s infrastructure and economy, but updated building codes and retrofits, as well as planning, community training, and preparedness, will help reduce the effects of a future Hayward Fault earthquake.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183052","usgsCitation":"Brocher, T.M., Boatwright, J., Lienkaemper, J.J., Prentice, C.S., Schwartz, D.P., and Bundock, H.P., 2018, The Hayward Fault—Is it due for a repeat of the powerful 1868 earthquake?: U.S. Geological Survey Fact Sheet 2018–3052, 4 p., https://doi.org/10.3133/fs20183052.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-099862","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":356811,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3052/fs20183052.pdf","text":"Report","size":"12.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2018-3052"},{"id":356810,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3052/coverthb_.jpg"}],"country":"United States","state":"California","otherGeospatial":"Hayward Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.72552490234375,\n 36.848856608486905\n ],\n [\n -121.48406982421875,\n 36.848856608486905\n ],\n [\n -121.48406982421875,\n 38.25112269630296\n ],\n [\n -122.72552490234375,\n 38.25112269630296\n ],\n [\n -122.72552490234375,\n 36.848856608486905\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information
Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025