Climate warming poses a serious threat to alpine-restricted species worldwide, yet few studies have empirically documented climate-induced changes in distributions. The rare stonefly, Zapada glacier (Baumann and Gaufin), endemic to alpine streams of Glacier National Park (GNP), Montana, was recently petitioned for listing under the US Endangered Species Act because of climate-change-induced glacier loss, yet little was known about its current status and distribution. We resampled streams throughout the historical distribution of Z. glacier to investigate trends in occurrence associated with changes in temperature and glacial extent. The current geographic distribution of the species was assessed using morphological characteristics of adults and DNA barcoding of nymphs. Bayesian phylogenetic analysis of mtDNA data revealed 8 distinct clades of the genus corresponding with 7 known species from GNP, and one potentially cryptic species. Climate model simulations indicate that average summer air temperature increased (0.67–1.00°C) during the study period (1960–2012), and glacial surface area decreased by ∼35% from 1966 to 2005. We detected Z. glacier in only 1 of the 6 historically occupied streams and at 2 new locations in GNP. These results suggest that an extremely restricted historical distribution of Z. glacierin GNP has been further reduced over the past several decades by an upstream retreat to higher, cooler sites as water temperatures increased and glacial masses decreased. More research is urgently needed to determine the status, distribution, and vulnerability of Z. glacier and other alpine stream invertebrates threatened by climate change in mountainous ecosystems.
","language":"English","publisher":"The Society for Freshwater Science","publisherLocation":"Springfield, IL","doi":"10.1086/679490","usgsCitation":"Giersch, J., Jordan, S., Luikart, G., Jones, L.A., Hauer, F.R., and Muhlfeld, C.C., 2015, Climate-induced range contraction of a rare alpine aquatic invertebrate: Freshwater Science, v. 34, no. 1, p. 53-65, https://doi.org/10.1086/679490.","productDescription":"13 p.","startPage":"53","endPage":"65","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056728","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":319551,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": 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Joseph","email":"jgiersch@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":false,"id":625384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jordan, Steve","contributorId":168297,"corporation":false,"usgs":false,"family":"Jordan","given":"Steve","email":"","affiliations":[{"id":25242,"text":"Department of Biology, Bucknell University, Lewisburg, Pennsylvania 17837, USA","active":true,"usgs":false}],"preferred":false,"id":625385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luikart, Gordon","contributorId":97409,"corporation":false,"usgs":false,"family":"Luikart","given":"Gordon","affiliations":[{"id":6580,"text":"University of Montana, Flathead Lake Biological Station, Polson, Montana 59860, USA","active":true,"usgs":false}],"preferred":false,"id":625386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Leslie A. 0000-0002-4953-7189 lajones@usgs.gov","orcid":"https://orcid.org/0000-0002-4953-7189","contributorId":4599,"corporation":false,"usgs":true,"family":"Jones","given":"Leslie","email":"lajones@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":625389,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hauer, F. Richard","contributorId":76892,"corporation":false,"usgs":true,"family":"Hauer","given":"F.","email":"","middleInitial":"Richard","affiliations":[],"preferred":false,"id":625387,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":625388,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70133844,"text":"70133844 - 2015 - Scale-dependent feedbacks between patch size and plant reproduction in desert grassland","interactions":[],"lastModifiedDate":"2015-02-09T15:32:06","indexId":"70133844","displayToPublicDate":"2014-11-20T14:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Scale-dependent feedbacks between patch size and plant reproduction in desert grassland","docAbstract":"Theoretical models suggest that scale-dependent feedbacks between plant reproductive success and plant patch size govern transitions from highly to sparsely vegetated states in drylands, yet there is scant empirical evidence for these mechanisms. Scale-dependent feedback models suggest that an optimal patch size exists for growth and reproduction of plants and that a threshold patch organization exists below which positive feedbacks between vegetation and resources can break down, leading to critical transitions. We examined the relationship between patch size and plant reproduction using an experiment in a Chihuahuan Desert grassland. We tested the hypothesis that reproductive effort and success of a dominant grass (Bouteloua eriopoda) would vary predictably with patch size. We found that focal plants in medium-sized patches featured higher rates of grass reproductive success than when plants occupied either large patch interiors or small patches. These patterns support the existence of scale-dependent feedbacks in Chihuahuan Desert grasslands and indicate an optimal patch size for reproductive effort and success in B. eriopoda. We discuss the implications of these results for detecting ecological thresholds in desert grasslands.
","language":"English","publisher":"Springer","publisherLocation":"New York, NY","doi":"10.1007/s10021-014-9818-9","usgsCitation":"Svejcar, L.N., Bestelmeyer, B.T., Duniway, M.C., and James, D.K., 2015, Scale-dependent feedbacks between patch size and plant reproduction in desert grassland: Ecosystems, v. 18, no. 1, p. 146-153, https://doi.org/10.1007/s10021-014-9818-9.","productDescription":"8 p.","startPage":"146","endPage":"153","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-048974","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":296238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Chihuahuan Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.89285278320312,\n 32.43445398335842\n ],\n [\n -106.89285278320312,\n 32.786120011700625\n ],\n [\n -106.53030395507812,\n 32.786120011700625\n ],\n [\n -106.53030395507812,\n 32.43445398335842\n ],\n [\n -106.89285278320312,\n 32.43445398335842\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","issue":"1","noUsgsAuthors":false,"publicationDate":"2014-11-18","publicationStatus":"PW","scienceBaseUri":"546f10fee4b057be23d4a7af","contributors":{"authors":[{"text":"Svejcar, Lauren N.","contributorId":127492,"corporation":false,"usgs":false,"family":"Svejcar","given":"Lauren","email":"","middleInitial":"N.","affiliations":[{"id":6973,"text":"USDA-ARS Jornada Experimental Range and Jornada Basin LTER, Las Cruces, NM; New Mexico State University, Dept. of Plant and Environmental Sciences, Las Cruces, NM","active":true,"usgs":false}],"preferred":false,"id":525474,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bestelmeyer, Brandon T.","contributorId":26180,"corporation":false,"usgs":false,"family":"Bestelmeyer","given":"Brandon","email":"","middleInitial":"T.","affiliations":[{"id":6973,"text":"USDA-ARS Jornada Experimental Range and Jornada Basin LTER, Las Cruces, NM; New Mexico State University, Dept. of Plant and Environmental Sciences, Las Cruces, NM","active":true,"usgs":false}],"preferred":false,"id":525475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":525473,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"James, Darren K.","contributorId":58166,"corporation":false,"usgs":false,"family":"James","given":"Darren","email":"","middleInitial":"K.","affiliations":[{"id":6973,"text":"USDA-ARS Jornada Experimental Range and Jornada Basin LTER, Las Cruces, NM; New Mexico State University, Dept. of Plant and Environmental Sciences, Las Cruces, NM","active":true,"usgs":false}],"preferred":false,"id":525476,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70131490,"text":"70131490 - 2015 - Assessing the impacts of climate and land use and land cover change on the freshwater availability in the Brahmaputra River basin","interactions":[],"lastModifiedDate":"2017-01-18T10:09:19","indexId":"70131490","displayToPublicDate":"2014-11-19T10:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"subseriesTitle":"Regional Studies","title":"Assessing the impacts of climate and land use and land cover change on the freshwater availability in the Brahmaputra River basin","docAbstract":"Study Region: Brahmaputra River basin in South Asia.
\n\n
Study Focus: The Soil and Water Assessment Tool was used to evaluate sensitivities and patterns in freshwater availability due to projected climate and land use changes in the Brahmaputra basin. The daily observed discharge at Bahadurabad station in Bangladesh was used to calibrate and validate the model and analyze uncertainties with a sequential uncertainty fitting algorithm. The sensitivities and impacts of projected climate and land use changes on basin hydrological components were simulated for the A1B and A2 scenarios and analyzed relative to a baseline scenario of 1988–2004.
\n\n
New hydrological insights for the region: Basin average annual ET was found to be sensitive to changes in CO2 concentration and temperature, while total water yield, streamflow, and groundwater recharge were sensitive to changes in precipitation. The basin hydrological components were predicted to increase with seasonal variability in response to climate and land use change scenarios. Strong increasing trends were predicted for total water yield, streamflow, and groundwater recharge, indicating exacerbation of flooding potential during August–October, but strong decreasing trends were predicted, indicating exacerbation of drought potential during May–July of the 21st century. The model has potential to facilitate strategic decision making through scenario generation integrating climate change adaptation and hazard mitigation policies to ensure optimized allocation of water resources under a variable and changing climate.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2014.09.003","usgsCitation":"Pervez, M., and Henebry, G.M., 2015, Assessing the impacts of climate and land use and land cover change on the freshwater availability in the Brahmaputra River basin: Journal of Hydrology, v. 3, p. 285-311, https://doi.org/10.1016/j.ejrh.2014.09.003.","productDescription":"27 p.","startPage":"285","endPage":"311","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056504","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472456,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2014.09.003","text":"Publisher Index Page"},{"id":296194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bangladesh","otherGeospatial":"Brahmaputra River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 81.8701171875,\n 20.097206227083888\n ],\n [\n 81.8701171875,\n 32.175612478499325\n ],\n [\n 104.4580078125,\n 32.175612478499325\n ],\n [\n 104.4580078125,\n 20.097206227083888\n ],\n [\n 81.8701171875,\n 20.097206227083888\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"546db11ae4b0fc7976bf1e1d","chorus":{"doi":"10.1016/j.ejrh.2014.09.003","url":"http://dx.doi.org/10.1016/j.ejrh.2014.09.003","publisher":"Elsevier BV","authors":"Pervez Md Shahriar, Henebry Geoffrey M.","journalName":"Journal of Hydrology: Regional Studies","publicationDate":"3/2015","auditedOn":"11/26/2014","publiclyAccessibleDate":"9/27/2014"},"contributors":{"authors":[{"text":"Pervez, Md Shahriar 0000-0003-3417-1871 spervez@usgs.gov","orcid":"https://orcid.org/0000-0003-3417-1871","contributorId":3099,"corporation":false,"usgs":true,"family":"Pervez","given":"Md Shahriar","email":"spervez@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":521265,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henebry, Geoffrey M.","contributorId":124528,"corporation":false,"usgs":false,"family":"Henebry","given":"Geoffrey","email":"","middleInitial":"M.","affiliations":[{"id":5087,"text":"Geographic Information Science Center of Excellence (GIScCE), South Dakota State University, Brookings, USA","active":true,"usgs":false}],"preferred":false,"id":521266,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70131491,"text":"70131491 - 2015 - Spatial and seasonal responses of precipitation in the Ganges and Brahmaputra river basins to ENSO and Indian Ocean dipole modes: Implications for flooding and drought","interactions":[],"lastModifiedDate":"2019-12-10T12:54:52","indexId":"70131491","displayToPublicDate":"2014-11-18T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2824,"text":"Natural Hazards and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and seasonal responses of precipitation in the Ganges and Brahmaputra river basins to ENSO and Indian Ocean dipole modes: Implications for flooding and drought","docAbstract":"We evaluated the spatial and seasonal responses of precipitation in the Ganges and Brahmaputra basins as modulated by the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) modes using Global Precipitation Climatology Centre (GPCC) full data reanalysis of monthly global land-surface precipitation data from 1901 to 2010 with a spatial resolution of 0.5° × 0.5°. The GPCC monthly total precipitation climatology targeting the period 1951–2000 was used to compute gridded monthly anomalies for the entire time period. The gridded monthly anomalies were averaged for the years influenced by combinations of climate modes. Occurrences of El Niño alone significantly reduce (88% of the long-term average (LTA)) precipitation during the monsoon months in the western and southeastern Ganges Basin. In contrast, occurrences of La Niña and co-occurrences of La Niña and negative IOD events significantly enhance (110 and 109% of LTA in the Ganges and Brahmaputra Basin, respectively) precipitation across both basins. When El Niño co-occurs with positive IOD events, the impacts of El Niño on the basins' precipitation diminishes. When there is no active ENSO or IOD events (occurring in 41 out of 110 years), precipitation remains below average (95% of LTA) in the agriculturally intensive areas of Haryana, Uttar Pradesh, Rajasthan, Madhya Pradesh, and Western Nepal in the Ganges Basin, whereas precipitation remains average to above average (104% of LTA) across the Brahmaputra Basin. This pattern implies that a regular water deficit is likely, especially in the Ganges Basin, with implications for the agriculture sector due to its reliance on consistent rainfall for successful production. Historically, major droughts occurred during El Niño and co-occurrences of El Niño and positive IOD events, while major flooding occurred during La Niña and co-occurrences of La Niña and negative IOD events in the basins. This observational analysis will facilitate well-informed decision making in minimizing natural hazard risks and climate impacts on agriculture, and supports development of strategies ensuring optimized use of water resources in best management practice under a changing climate.
Since 1998, cyclic mortality events in common eiders (Somateria mollissima), numbering in the hundreds to thousands of dead birds, have been documented along the coast of Cape Cod, Massachusetts, USA. Although longitudinal disease investigations have uncovered potential contributing factors responsible for these outbreaks, detecting a primary etiological agent has proven enigmatic. Here we identify a novel orthomyxovirus, tentatively named Wellfleet Bay virus (WFBV), as a potential causative agent of these outbreaks. Genomic analysis of WFBV revealed that it is most closely related to members of the Quaranjavirus genus within the family Orthomyxoviridae. Similar to other members of the genus, WFBV contains an alphabaculovirus gp64-like glycoprotein, which was demonstrated to have fusion activity, and also tentatively suggests that ticks (and/or insects) may vector the virus in nature. However, in addition to the six RNA segments encoding the prototypical structural proteins identified in other quaranjaviruses, a previously unknown RNA segment (segment 7) encoding a novel protein designated as VP7 was discovered in WFBV. Although WFBV shows low to moderate levels of sequence similarity to Quaranfil virus and Johnston Atoll virus, the original members of the Quaranjavirus genus, additional antigenic and genetic analyses demonstrated that it is closely related to the recently identified Cygnet River virus (CyRV) from South Australia, suggesting that WFBV and CyRV may be geographic variants of the same virus. Although the identification of WFBV in part may resolve the enigma of these mass mortality events, the details of the ecology and epidemiology of the virus remain to be determined.
\n\n
Importance The emergence or reemergence of viral pathogens resulting in large-scale outbreaks of disease in humans and/or animals is one of the most important challenges facing biomedicine. For example, understanding how orthomyxoviruses such as novel influenza A virus reassortants and/or mutants emerge to cause epidemic or pandemic disease is at the forefront of current global health concerns. Here we describe the emergence of a novel orthomyxovirus, Wellfleet Bay virus (WFBV), which has been associated with cyclic large-scale bird die-offs in the northeastern United States. This initial characterization study provides a foundation for further research into the evolution, epidemiology, and ecology of newly emerging orthomyxoviruses, such as WFBV, and their potential impacts on animal and/or human health.
","language":"English","publisher":"American Society for Microbiology","doi":"10.1128/JVI.02019-14","usgsCitation":"Allison, A.B., Ballard, J.R., Tesh, R.B., Brown, J.D., Ruder, M.G., Keel, M.K., Munk, B.A., Mickley, R.M., Gibbs, S., Ellis, J.C., Travassos da Rosac, A.P., Ip, S., Shearn-Bochsler, V.I., Rogers, M.B., Gheldin, E., Holmes, E., Parrish, C.R., and Dwyer, C.P., 2015, Cyclic avian mass mortality in the northeastern United States is associated with a novel orthomyxovirus: Journal of Virology, v. 89, no. 2, p. 1389-1403, https://doi.org/10.1128/JVI.02019-14.","productDescription":"5 p.","startPage":"1389","endPage":"1403","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055695","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":472460,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1128/jvi.02019-14","text":"Publisher Index 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For the State of Wyoming, elevation data are critical for geologic resource assessment and hazard mitigation, flood risk management, water supply an quality, natural resources conservation, agriculture and precision farming, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data.
\nThe National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios.The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.
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1: Originally posted November 7, 2014; Version 1.1: January 26, 2015","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"545ddf18e4b0ba8303f8b634","contributors":{"authors":[{"text":"Carswell, William J. Jr. carswell@usgs.gov","contributorId":1787,"corporation":false,"usgs":true,"family":"Carswell","given":"William J.","suffix":"Jr.","email":"carswell@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":false,"id":519896,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70128671,"text":"70128671 - 2015 - Testing the use of bulk organic δ13C, δ15N, and Corg:Ntot ratios to estimate subsidence during the 1964 great Alaska earthquake","interactions":[],"lastModifiedDate":"2018-04-04T16:09:39","indexId":"70128671","displayToPublicDate":"2014-11-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Testing the use of bulk organic δ13C, δ15N, and Corg:Ntot ratios to estimate subsidence during the 1964 great Alaska earthquake","docAbstract":"During the Mw 9.2 1964 great Alaska earthquake, Turnagain Arm near Girdwood, Alaska subsided 1.7 ± 0.1 m based on pre- and postearthquake leveling. The coseismic subsidence in 1964 caused equivalent sudden relative sea-level (RSL) rise that is stratigraphically preserved as mud-over-peat contacts where intertidal silt buried peaty marsh surfaces. Changes in intertidal microfossil assemblages across these contacts have been used to estimate subsidence in 1964 by applying quantitative microfossil transfer functions to reconstruct corresponding RSL rise. Here, we review the use of organic stable C and N isotope values and Corg:Ntot ratios as alternative proxies for reconstructing coseismic RSL changes, and report independent estimates of subsidence in 1964 by using δ13C values from intertidal sediment to assess RSL change caused by the earthquake. We observe that surface sediment δ13C values systematically decrease by ∼4‰ over the ∼2.5 m increase in elevation along three 60- to 100-m-long transects extending from intertidal mud flat to upland environments. We use a straightforward linear regression to quantify the relationship between modern sediment δ13C values and elevation (n = 84, R2 = 0.56). The linear regression provides a slope–intercept equation used to reconstruct the paleoelevation of the site before and after the earthquake based on δ13C values in sandy silt above and herbaceous peat below the 1964 contact. The regression standard error (average = ±0.59‰) reflects the modern isotopic variability at sites of similar surface elevation, and is equivalent to an uncertainty of ±0.4 m elevation with respect to Mean Higher High Water. To reduce potential errors in paleoelevation and subsidence estimates, we analyzed multiple sediment δ13C values in nine cores on a shore-perpendicular transect at Bird Point. Our method estimates 1.3 ± 0.4 m of coseismic RSL rise across the 1964 contact by taking the arithmetic mean of the differences (n = 9) between reconstructed elevations for sediment above and below the 1964 earthquake subsidence contact. This estimate compares well with independent subsidence estimates derived from post-earthquake leveling in Turnagain Arm, and from microfossil transfer functions at Girdwood (1.50 ± 0.32 m). While our results support the use of bulk organic δ13C for reconstructing RSL change in southern Alaska, the variability of stable isotope values in modern and buried intertidal sediment required the analysis of multiple samples to reduce error.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2014.09.031","usgsCitation":"Bender, A.M., Witter, R., and Rogers, M., 2015, Testing the use of bulk organic δ13C, δ15N, and Corg:Ntot ratios to estimate subsidence during the 1964 great Alaska earthquake: Quaternary Science Reviews, v. 113, p. 134-146, https://doi.org/10.1016/j.quascirev.2014.09.031.","productDescription":"13 p.","startPage":"134","endPage":"146","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060286","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":297372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -167.16796875,\n 71.1877539181316\n ],\n [\n -140.2734375,\n 71.13098770917023\n ],\n [\n -141.15234374999997,\n 59.62332522313024\n ],\n [\n -167.51953124999997,\n 51.944264879028765\n ],\n [\n -167.16796875,\n 71.1877539181316\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"113","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54dd2c68e4b08de9379b37aa","contributors":{"authors":[{"text":"Bender, Adrian M. 0000-0001-7469-1957 abender@usgs.gov","orcid":"https://orcid.org/0000-0001-7469-1957","contributorId":4963,"corporation":false,"usgs":true,"family":"Bender","given":"Adrian","email":"abender@usgs.gov","middleInitial":"M.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":519748,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Witter, Robert C. 0000-0002-1721-254X rwitter@usgs.gov","orcid":"https://orcid.org/0000-0002-1721-254X","contributorId":4528,"corporation":false,"usgs":true,"family":"Witter","given":"Robert C.","email":"rwitter@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":519747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rogers, Matthew","contributorId":120088,"corporation":false,"usgs":false,"family":"Rogers","given":"Matthew","affiliations":[],"preferred":false,"id":519749,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70160447,"text":"70160447 - 2015 - Late Holocene sea- and land-level change on the U.S. southeastern Atlantic Coast","interactions":[],"lastModifiedDate":"2015-12-18T15:39:40","indexId":"70160447","displayToPublicDate":"2014-11-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Late Holocene sea- and land-level change on the U.S. southeastern Atlantic Coast","docAbstract":"Late Holocene relative sea-level (RSL) reconstructions can be used to estimate rates of land-level (subsidence or uplift) change and therefore to modify global sea-level projections for regional conditions. These reconstructions also provide the long-term benchmark against which modern trends are compared and an opportunity to understand the response of sea level to past climate variability. To address a spatial absence of late Holocene data in Florida and Georgia, we reconstructed ~ 1.3 m of RSL rise in northeastern Florida (USA) during the past ~ 2600 years using plant remains and foraminifera in a dated core of high salt-marsh sediment. The reconstruction was fused with tide-gauge data from nearby Fernandina Beach, which measured 1.91 ± 0.26 mm/year of RSL rise since 1900 CE. The average rate of RSL rise prior to 1800 CE was 0.41 ± 0.08 mm/year. Assuming negligible change in global mean sea level from meltwater input/removal and thermal expansion/contraction, this sea-level history approximates net land-level (subsidence and geoid) change, principally from glacio-isostatic adjustment. Historic rates of rise commenced at 1850–1890 CE and it is virtually certain (P = 0.99) that the average rate of 20th century RSL rise in northeastern Florida was faster than during any of the preceding 26 centuries. The linearity of RSL rise in Florida is in contrast to the variability reconstructed at sites further north on the U.S. Atlantic coast and may suggest a role for ocean dynamic effects in explaining these more variable RSL reconstructions. Comparison of the difference between reconstructed rates of late Holocene RSL rise and historic trends measured by tide gauges indicates that 20th century sea-level trends along the U.S. Atlantic coast were not dominated by the characteristic spatial fingerprint of melting of the Greenland Ice Sheet.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.margeo.2014.07.010","usgsCitation":"Kemp, A., Bernhardt, C.E., Horton, B.P., Kopp, R., Vane, C.H., Peltier, W.R., Hawkes, A., Donnelly, J., Parnell, A.C., and Cahill, N., 2015, Late Holocene sea- and land-level change on the U.S. southeastern Atlantic Coast: Marine Geology, v. 357, p. 90-100, https://doi.org/10.1016/j.margeo.2014.07.010.","productDescription":"11 p.","startPage":"90","endPage":"100","numberOfPages":"11","ipdsId":"IP-058206","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":472462,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://mural.maynoothuniversity.ie/14567/1/NC_late%20holocene.pdf","text":"External Repository"},{"id":312547,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.68322086334229,\n 30.572090062678555\n ],\n [\n -81.68322086334229,\n 30.597434228713674\n ],\n [\n -81.64442539215088,\n 30.597434228713674\n ],\n [\n -81.64442539215088,\n 30.572090062678555\n ],\n [\n -81.68322086334229,\n 30.572090062678555\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"357","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56753c3ee4b0da412f4f8bf1","contributors":{"authors":[{"text":"Kemp, Andrew C.","contributorId":39674,"corporation":false,"usgs":true,"family":"Kemp","given":"Andrew C.","affiliations":[],"preferred":false,"id":582928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernhardt, Christopher E. 0000-0003-0082-4731 cbernhardt@usgs.gov","orcid":"https://orcid.org/0000-0003-0082-4731","contributorId":2131,"corporation":false,"usgs":true,"family":"Bernhardt","given":"Christopher","email":"cbernhardt@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":582927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horton, Benjamin P.","contributorId":63641,"corporation":false,"usgs":true,"family":"Horton","given":"Benjamin","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":582929,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kopp, Robert E.","contributorId":64570,"corporation":false,"usgs":true,"family":"Kopp","given":"Robert E.","affiliations":[],"preferred":false,"id":582930,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vane, Christopher H.","contributorId":88255,"corporation":false,"usgs":true,"family":"Vane","given":"Christopher","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":582931,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peltier, W. Richard","contributorId":150752,"corporation":false,"usgs":false,"family":"Peltier","given":"W.","email":"","middleInitial":"Richard","affiliations":[{"id":7044,"text":"University of Toronto","active":true,"usgs":false}],"preferred":false,"id":582932,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hawkes, Andrea D.","contributorId":20240,"corporation":false,"usgs":true,"family":"Hawkes","given":"Andrea D.","affiliations":[],"preferred":false,"id":582933,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Donnelly, Jeffrey P.","contributorId":91613,"corporation":false,"usgs":true,"family":"Donnelly","given":"Jeffrey P.","affiliations":[],"preferred":false,"id":582934,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Parnell, Andrew C.","contributorId":150753,"corporation":false,"usgs":false,"family":"Parnell","given":"Andrew","email":"","middleInitial":"C.","affiliations":[{"id":18091,"text":"University College Dublin","active":true,"usgs":false}],"preferred":false,"id":582935,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Cahill, Niamh","contributorId":150754,"corporation":false,"usgs":false,"family":"Cahill","given":"Niamh","email":"","affiliations":[{"id":18091,"text":"University College Dublin","active":true,"usgs":false},{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":582936,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70139357,"text":"70139357 - 2015 - Using scenario planning to evaluate the impacts of climate change on wildlife populations and communities in the Florida Everglades","interactions":[],"lastModifiedDate":"2015-04-01T09:35:08","indexId":"70139357","displayToPublicDate":"2014-11-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Using scenario planning to evaluate the impacts of climate change on wildlife populations and communities in the Florida Everglades","docAbstract":"It is uncertain how climate change will impact hydrologic drivers of wildlife population dynamics in freshwater wetlands of the Florida Everglades, or how to accommodate this uncertainty in restoration decisions. Using projections of climate scenarios for the year 2060, we evaluated how several possible futures could affect wildlife populations (wading birds, fish, alligators, native apple snails, amphibians, threatened and invasive species) across the Everglades landscape and inform planning already underway. We used data collected from prior research and monitoring to parameterize our wildlife population models. Hydrologic data were simulated using a spatially explicit, regional-scale model. Our scenario evaluations show that expected changes in temperature, precipitation, and sea level could significantly alter important ecological functions. All of our wildlife indicators were negatively affected by scenarios with less rainfall and more evapotranspiration. Under such scenarios, habitat suitability was substantially reduced for iconic animals such as wading birds and alligators. Conversely, the increased rainfall scenario benefited aquatic prey productivity and apex predators. Cascading impacts on non-native species is speculative, but increasing temperatures could increase the time between cold events that currently limit expansion and abundance of non-native fishes, amphibians, and reptiles with natural ranges in the tropics. This scenario planning framework underscored the benefits of proceeding with Everglades restoration plans that capture and clean more freshwater with the potential to mitigate rainfall loss and postpone impacts of sea level rise.
","language":"English","publisher":"Environmental Management","doi":"10.1007/s00267-014-0397-5","usgsCitation":"Catano, C.P., Romañach, S., Beerens, J., Pearlstine, L.G., Brandt, L., Hart, K.M., Mazzotti, F., and Trexler, J.C., 2015, Using scenario planning to evaluate the impacts of climate change on wildlife populations and communities in the Florida Everglades: Environmental Management, v. 55, no. 4, p. 807-823, https://doi.org/10.1007/s00267-014-0397-5.","productDescription":"17 p.","startPage":"807","endPage":"823","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-051166","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":297570,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.28759765625,\n 25.110471486223346\n ],\n [\n -82.28759765625,\n 26.868180902512403\n ],\n [\n -79.8431396484375,\n 26.868180902512403\n ],\n [\n -79.8431396484375,\n 25.110471486223346\n ],\n [\n -82.28759765625,\n 25.110471486223346\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2014-11-05","publicationStatus":"PW","scienceBaseUri":"54dd2c7ee4b08de9379b3840","contributors":{"authors":[{"text":"Catano, Christopher P.","contributorId":138935,"corporation":false,"usgs":false,"family":"Catano","given":"Christopher","email":"","middleInitial":"P.","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":539334,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Romañach, Stephanie S. 0000-0003-0271-7825 sromanach@usgs.gov","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":2331,"corporation":false,"usgs":true,"family":"Romañach","given":"Stephanie S.","email":"sromanach@usgs.gov","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":false,"id":539335,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beerens, James M. 0000-0001-8143-916X","orcid":"https://orcid.org/0000-0001-8143-916X","contributorId":25440,"corporation":false,"usgs":false,"family":"Beerens","given":"James M.","affiliations":[],"preferred":false,"id":539336,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pearlstine, Leonard G.","contributorId":34751,"corporation":false,"usgs":false,"family":"Pearlstine","given":"Leonard","email":"","middleInitial":"G.","affiliations":[{"id":12462,"text":"U.S. Department of the Interior, National Park Service","active":true,"usgs":false}],"preferred":false,"id":539337,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brandt, Laura A.","contributorId":18608,"corporation":false,"usgs":false,"family":"Brandt","given":"Laura A.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":539338,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hart, Kristen M. 0000-0002-5257-7974 kristen_hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":1966,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","email":"kristen_hart@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":539322,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mazzotti, Frank J.","contributorId":100018,"corporation":false,"usgs":false,"family":"Mazzotti","given":"Frank J.","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":539339,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Trexler, Joel C.","contributorId":36267,"corporation":false,"usgs":false,"family":"Trexler","given":"Joel","email":"","middleInitial":"C.","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":539340,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70132472,"text":"70132472 - 2015 - Uncertainty estimates in broadband seismometer sensitivities using microseisms","interactions":[],"lastModifiedDate":"2015-03-19T15:49:02","indexId":"70132472","displayToPublicDate":"2014-10-30T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2453,"text":"Journal of Seismology","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty estimates in broadband seismometer sensitivities using microseisms","docAbstract":"The midband sensitivity of a seismic instrument is one of the fundamental parameters used in published station metadata. Any errors in this value can compromise amplitude estimates in otherwise high-quality data. To estimate an upper bound in the uncertainty of the midband sensitivity for modern broadband instruments, we compare daily microseism (4- to 8-s period) amplitude ratios between the vertical components of colocated broadband sensors across the IRIS/USGS (network code IU) seismic network. We find that the mean of the 145,972 daily ratios used between 2002 and 2013 is 0.9895 with a standard deviation of 0.0231. This suggests that the ratio between instruments shows a small bias and considerable scatter. We also find that these ratios follow a standard normal distribution (R 2 = 0.95442), which suggests that the midband sensitivity of an instrument has an error of no greater than ±6 % with a 99 % confidence interval. This gives an upper bound on the precision to which we know the sensitivity of a fielded instrument.
","language":"English","publisher":"Springer","doi":"10.1007/s10950-014-9467-7","usgsCitation":"Ringler, A.T., Storm, T., Gee, L., Hutt, C.R., and Wilson, D., 2015, Uncertainty estimates in broadband seismometer sensitivities using microseisms: Journal of Seismology, v. 19, no. 2, p. 317-327, https://doi.org/10.1007/s10950-014-9467-7.","productDescription":"11 p.","startPage":"317","endPage":"327","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059886","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":296151,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"2","noUsgsAuthors":false,"publicationDate":"2014-10-30","publicationStatus":"PW","scienceBaseUri":"546c763de4b0f4a3478a61d7","contributors":{"authors":[{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":523246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storm, Tyler L. tstorm@usgs.gov","contributorId":4073,"corporation":false,"usgs":true,"family":"Storm","given":"Tyler L.","email":"tstorm@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":523247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gee, Lind S. lgee@usgs.gov","contributorId":2247,"corporation":false,"usgs":true,"family":"Gee","given":"Lind S.","email":"lgee@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":523248,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hutt, Charles R. 0000-0001-9033-9195 bhutt@usgs.gov","orcid":"https://orcid.org/0000-0001-9033-9195","contributorId":1622,"corporation":false,"usgs":true,"family":"Hutt","given":"Charles","email":"bhutt@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":523249,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, David C. dwilson@usgs.gov","contributorId":4588,"corporation":false,"usgs":true,"family":"Wilson","given":"David C.","email":"dwilson@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":523250,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70131478,"text":"70131478 - 2015 - Permafrost-associated gas hydrate: is it really approximately 1% of the global system?","interactions":[],"lastModifiedDate":"2015-02-23T16:20:09","indexId":"70131478","displayToPublicDate":"2014-10-29T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3821,"text":"Journal of Chemical & Engineering Data","active":true,"publicationSubtype":{"id":10}},"title":"Permafrost-associated gas hydrate: is it really approximately 1% of the global system?","docAbstract":"Permafrost-associated gas hydrates are often assumed to contain ∼1 % of the global gas-in-place in gas hydrates based on a study26 published over three decades ago. As knowledge of permafrost-associated gas hydrates has grown, it has become clear that many permafrost-associated gas hydrates are inextricably linked to an associated conventional petroleum system, and that their formation history (trapping of migrated gas in situ during Pleistocene cooling) is consistent with having been sourced at least partially in nearby thermogenic gas deposits. Using modern data sets that constrain the distribution of continuous permafrost onshore5 and subsea permafrost on circum-Arctic Ocean continental shelves offshore and that estimate undiscovered conventional gas within arctic assessment units,16 the done here reveals where permafrost-associated gas hydrates are most likely to occur, concluding that Arctic Alaska and the West Siberian Basin are the best prospects. A conservative estimate is that 20 Gt C (2.7·1013 kg CH4) may be sequestered in permafrost-associated gas hydrates if methane were the only hydrate-former. This value is slightly more than 1 % of modern estimates (corresponding to 1600 Gt C to 1800 Gt C2,22) for global gas-in-place in methane hydrates and about double the absolute estimate (11.2 Gt C) made in 1981.26
","language":"English","publisher":"American Chemical Society","doi":"10.1021/je500770m","usgsCitation":"Ruppel, C., 2015, Permafrost-associated gas hydrate: is it really approximately 1% of the global system?: Journal of Chemical & Engineering Data, v. 60, no. 2, p. 429-436, https://doi.org/10.1021/je500770m.","productDescription":"8 p.","startPage":"429","endPage":"436","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060410","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":296452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"2","noUsgsAuthors":false,"publicationDate":"2014-10-29","publicationStatus":"PW","scienceBaseUri":"548193bee4b0aa6d778520f0","chorus":{"doi":"10.1021/je500770m","url":"http://dx.doi.org/10.1021/je500770m","publisher":"American Chemical Society (ACS)","authors":"Ruppel C.","journalName":"Journal of Chemical & Engineering Data","publicationDate":"2/12/2015","auditedOn":"12/1/2014"},"contributors":{"authors":[{"text":"Ruppel, Carolyn cruppel@usgs.gov","contributorId":2015,"corporation":false,"usgs":true,"family":"Ruppel","given":"Carolyn","email":"cruppel@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":521234,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70128794,"text":"70128794 - 2015 - Machine learning for predicting soil classes in three semi-arid landscapes","interactions":[],"lastModifiedDate":"2014-10-14T15:20:17","indexId":"70128794","displayToPublicDate":"2014-10-14T15:14:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1760,"text":"Geoderma","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning for predicting soil classes in three semi-arid landscapes","docAbstract":"Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes.
\nOur objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination.
\nOverall, complex models were consistently more accurate than simple or moderately complex models. Random forests (RF) using covariates selected via recursive feature elimination was consistently the most accurate, or was among the most accurate, classifiers between study areas and between covariate sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used.
\nOverall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. Individual subgroup class accuracy was generally dependent upon the number of soil pedon observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil–landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geoderma","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.geoderma.2014.09.019","usgsCitation":"Brungard, C.W., Boettinger, J.L., Duniway, M.C., Wills, S., and Edwards, T.C., 2015, Machine learning for predicting soil classes in three semi-arid landscapes: Geoderma, v. 239-240, p. 68-83, https://doi.org/10.1016/j.geoderma.2014.09.019.","productDescription":"16 p.","startPage":"68","endPage":"83","ipdsId":"IP-055747","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":295328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":295318,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.geoderma.2014.09.019"}],"country":"United States","state":"New Mexico, Utah, Wyoming","volume":"239-240","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"543e2d06e4b0fd76af69cedc","contributors":{"authors":[{"text":"Brungard, Colby W.","contributorId":99488,"corporation":false,"usgs":true,"family":"Brungard","given":"Colby","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":503225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boettinger, Janis L.","contributorId":82239,"corporation":false,"usgs":true,"family":"Boettinger","given":"Janis","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":503223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":503222,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wills, Skye A.","contributorId":92600,"corporation":false,"usgs":true,"family":"Wills","given":"Skye A.","affiliations":[],"preferred":false,"id":503224,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edwards, Thomas C. Jr. 0000-0002-0773-0909 tce@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-0909","contributorId":2061,"corporation":false,"usgs":true,"family":"Edwards","given":"Thomas","suffix":"Jr.","email":"tce@usgs.gov","middleInitial":"C.","affiliations":[],"preferred":false,"id":503221,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70134827,"text":"70134827 - 2015 - Moving beyond too little, too late: managing emerging infectious diseases in wild populations requires international policy and partnerships","interactions":[],"lastModifiedDate":"2015-11-09T10:15:51","indexId":"70134827","displayToPublicDate":"2014-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1443,"text":"EcoHealth","active":true,"publicationSubtype":{"id":10}},"title":"Moving beyond too little, too late: managing emerging infectious diseases in wild populations requires international policy and partnerships","docAbstract":"Emerging infectious diseases (EIDs) are on the rise due to multiple factors, including human facilitated movement of pathogens, broad-scale landscape changes, and perturbations to ecological systems (Jones et al. 2008; Fisher et al. 2012). Epidemics in wildlife are problematic because they can lead to pathogen spillover to new host organisms, erode biodiversity and threaten ecosystems that sustain human societies (Fisher et al. 2012; Kilpatrick 2011). There have been recent calls for large-scale research approaches to combat threats EIDs pose to wildlife (Sleeman 2013). While it is true that developing new analytical models, diagnostic assays and molecular tools will significantly avance outr abilities to respond to disease threats, we also propose that addressing difficult problems in EIDs will require considerable shofts in international health policy and infrastructure. While there are currently international organizations responsbile for rapidly initiating and coordinating preventative measures to control infectious diseases in human, livestock, and arable systems, there are few comparable instiutions that have the authority to implement transnational responses to EIDs in wildlife. This absence of well-developed infastructure hampers the rapid responses necessary to mitigate international spread of EIDs.
","language":"English","publisher":"Springer","doi":"10.1007/s10393-014-0980-5","usgsCitation":"Voyles, J., Kilpatrick, A., Collins, J.P., Fisher, M.C., Frick, W., McCallum, H.I., Willis, C.K., Blehert, D., Murray, K.A., Puschendorf, R., Rosenblum, E.B., Bolker, B.M., Cheng, T., Langwig, K.E., Linder, D.L., Toothman, M., Wilber, M.Q., and Briggs, C.J., 2015, Moving beyond too little, too late: managing emerging infectious diseases in wild populations requires international policy and partnerships: EcoHealth, v. 12, no. 3, p. 404-407, https://doi.org/10.1007/s10393-014-0980-5.","productDescription":"4 p.","startPage":"404","endPage":"407","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045980","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":472464,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10393-014-0980-5","text":"Publisher Index Page"},{"id":296467,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Worldwide","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -171.5625,\n -85.1709701284095\n ],\n [\n -171.5625,\n 84.9901001802348\n ],\n [\n 191.25,\n 84.9901001802348\n ],\n [\n 191.25,\n -85.1709701284095\n ],\n [\n -171.5625,\n -85.1709701284095\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2014-10-07","publicationStatus":"PW","scienceBaseUri":"5482e548e4b0aa6d7785300a","contributors":{"authors":[{"text":"Voyles, Jamie","contributorId":127709,"corporation":false,"usgs":false,"family":"Voyles","given":"Jamie","email":"","affiliations":[{"id":7026,"text":"New Mexico Tech","active":true,"usgs":false}],"preferred":false,"id":526605,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kilpatrick, A. Marm","contributorId":59279,"corporation":false,"usgs":false,"family":"Kilpatrick","given":"A. Marm","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":526606,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, James P.","contributorId":127710,"corporation":false,"usgs":false,"family":"Collins","given":"James","email":"","middleInitial":"P.","affiliations":[{"id":7114,"text":"Arizona State Unviersity","active":true,"usgs":false}],"preferred":false,"id":526607,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fisher, Matthew C.","contributorId":127711,"corporation":false,"usgs":false,"family":"Fisher","given":"Matthew","email":"","middleInitial":"C.","affiliations":[{"id":7115,"text":"Imperial College of London","active":true,"usgs":false}],"preferred":false,"id":526608,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frick, Winifred F.","contributorId":127712,"corporation":false,"usgs":false,"family":"Frick","given":"Winifred F.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":526609,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCallum, Hamish I.","contributorId":127713,"corporation":false,"usgs":false,"family":"McCallum","given":"Hamish","email":"","middleInitial":"I.","affiliations":[{"id":7117,"text":"Griffith University","active":true,"usgs":false}],"preferred":false,"id":526610,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Willis, Craig K. R.","contributorId":92551,"corporation":false,"usgs":true,"family":"Willis","given":"Craig","email":"","middleInitial":"K. R.","affiliations":[],"preferred":false,"id":526611,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Blehert, David S. 0000-0002-1065-9760 dblehert@usgs.gov","orcid":"https://orcid.org/0000-0002-1065-9760","contributorId":1816,"corporation":false,"usgs":true,"family":"Blehert","given":"David S.","email":"dblehert@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":526612,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Murray, Kris A.","contributorId":127714,"corporation":false,"usgs":false,"family":"Murray","given":"Kris","email":"","middleInitial":"A.","affiliations":[{"id":7118,"text":"EcoHealth Alliance","active":true,"usgs":false}],"preferred":false,"id":526613,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Puschendorf, Robert","contributorId":127715,"corporation":false,"usgs":false,"family":"Puschendorf","given":"Robert","email":"","affiliations":[{"id":7119,"text":"Plymouth University","active":true,"usgs":false}],"preferred":false,"id":526614,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rosenblum, Erica Bree","contributorId":104330,"corporation":false,"usgs":false,"family":"Rosenblum","given":"Erica","email":"","middleInitial":"Bree","affiliations":[{"id":6643,"text":"University of California - Berkeley","active":true,"usgs":false}],"preferred":false,"id":526615,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Bolker, Benjamin M.","contributorId":34021,"corporation":false,"usgs":false,"family":"Bolker","given":"Benjamin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":526616,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Cheng, Tina L.","contributorId":127716,"corporation":false,"usgs":false,"family":"Cheng","given":"Tina L.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":526617,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Langwig, Kate E.","contributorId":127717,"corporation":false,"usgs":false,"family":"Langwig","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":526618,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Linder, Daniel L.","contributorId":127718,"corporation":false,"usgs":false,"family":"Linder","given":"Daniel","email":"","middleInitial":"L.","affiliations":[{"id":6679,"text":"US Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":526619,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Toothman, Mary","contributorId":127719,"corporation":false,"usgs":false,"family":"Toothman","given":"Mary","email":"","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":526620,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Wilber, Mark Q.","contributorId":127720,"corporation":false,"usgs":false,"family":"Wilber","given":"Mark","email":"","middleInitial":"Q.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":526621,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Briggs, Cheryl J.","contributorId":127721,"corporation":false,"usgs":false,"family":"Briggs","given":"Cheryl","email":"","middleInitial":"J.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":526622,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70142051,"text":"70142051 - 2015 - Rangewide climate vulnerability assessment for threatened Bull Trout","interactions":[],"lastModifiedDate":"2022-10-18T14:25:49.509194","indexId":"70142051","displayToPublicDate":"2014-09-30T09:47:55","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Rangewide climate vulnerability assessment for threatened Bull Trout","docAbstract":"The bull trout, listed as threatened under the Endangered Species Act, is well adapted to the cold waters of the Northwest. Recent changes in climate have caused winter flooding and warmer summer water temperatures in the region, reducing the cold-water habitats that bull trout depend on. The southernmost bull trout populations, found in Oregon, Washington, Idaho, Montana, and Nevada, are currently restricted to small reserves where the coldest waters still exist. These shrinking habitats have created a severed environment being further split by dams, poor water quality, and invasive species.
The goal of this project was to determine how these factors threaten the species regionally by using predictions of stream temperature to map habitat areas that support juvenile bull trout. Results show that maintaining larger areas of cold water habitat had the greatest, positive impact on bull trout habitat conservation. Other conditions that support bull trout include very cold summer water temperatures, fewer winter floods, and fewer human disturbances (such as the building of dams). Based on these results, specific climate adaptation actions that local managers might consider include prioritizing land and water use to foster colder summer water temperatures, controlling invasive species, increasing connectivity between Bull Trout habitats, and continuing monitoring efforts.
To ensure that these results and habitat maps could be incorporated into management actions, researchers met with stakeholders including the U.S. Fish and Wildlife Service (USFWS), the U.S. Forest Service, and the Burns Paiute Tribe. As a result, the maps were used in forest planning for the Lolo National Forest in Montana, the Wenatchee River basin, and in the lower Pend Oreille River during the relicensing process for local dam operations. In addition, the recovery plan proposed by the USFWS incorporated these models into detailed analyses of bull trout habitat loss, which managers can use to prioritize actions in their Recovery Unit Implementation Plans.
","language":"English","publisher":"Northwest Climate Science Center","usgsCitation":"Dunham, J., 2015, Rangewide climate vulnerability assessment for threatened Bull Trout, 47 p.","productDescription":"47 p.","ipdsId":"IP-060209","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":362358,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":298173,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f8c64d2e4b0546c0c397b46/5006f464e4b0abf7ce733f90"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":1808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason B.","email":"jdunham@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":541589,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70127469,"text":"70127469 - 2015 - Threshold-dependent sample sizes for selenium assessment with stream fish tissue","interactions":[],"lastModifiedDate":"2016-12-14T11:58:43","indexId":"70127469","displayToPublicDate":"2014-09-30T09:44:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2006,"text":"Integrated Environmental Assessment and Management","active":true,"publicationSubtype":{"id":10}},"title":"Threshold-dependent sample sizes for selenium assessment with stream fish tissue","docAbstract":"Natural resource managers are developing assessments of selenium (Se) contamination in freshwater ecosystems based on fish tissue concentrations. We evaluated the effects of sample size (i.e., number of fish per site) on the probability of correctly detecting mean whole-body Se values above a range of potential management thresholds. We modeled Se concentrations as gamma distributions with shape and scale parameters fitting an empirical mean-to-variance relationship in data from southwestern West Virginia, USA (63 collections, 382 individuals). We used parametric bootstrapping techniques to calculate statistical power as the probability of detecting true mean concentrations up to 3 mg Se/kg above management thresholds ranging from 4 to 8 mg Se/kg. Sample sizes required to achieve 80% power varied as a function of management thresholds and Type I error tolerance (α). Higher thresholds required more samples than lower thresholds because populations were more heterogeneous at higher mean Se levels. For instance, to assess a management threshold of 4 mg Se/kg, a sample of eight fish could detect an increase of approximately 1 mg Se/kg with 80% power (given α = 0.05), but this sample size would be unable to detect such an increase from a management threshold of 8 mg Se/kg with more than a coin-flip probability. Increasing α decreased sample size requirements to detect above-threshold mean Se concentrations with 80% power. For instance, at an α-level of 0.05, an 8-fish sample could detect an increase of approximately 2 units above a threshold of 8 mg Se/kg with 80% power, but when α was relaxed to 0.2, this sample size was more sensitive to increasing mean Se concentrations, allowing detection of an increase of approximately 1.2 units with equivalent power. Combining individuals into 2- and 4-fish composite samples for laboratory analysis did not decrease power because the reduced number of laboratory samples was compensated for by increased precision of composites for estimating mean conditions. However, low sample sizes (<5 fish) did not achieve 80% power to detect near-threshold values (i.e., <1 mg Se/kg) under any scenario we evaluated. This analysis can assist the sampling design and interpretation of Se assessments from fish tissue by accounting for natural variation in stream fish populations.
","language":"English","publisher":"SETAC","publisherLocation":"Pensacola, FL","doi":"10.1002/ieam.1579","usgsCitation":"Hitt, N.P., and Smith, D., 2015, Threshold-dependent sample sizes for selenium assessment with stream fish tissue: Integrated Environmental Assessment and Management, v. 11, no. 1, p. 143-149, https://doi.org/10.1002/ieam.1579.","productDescription":"7 p.","startPage":"143","endPage":"149","ipdsId":"IP-053353","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":472465,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ieam.1579","text":"Publisher Index Page"},{"id":294605,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294604,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/ieam.1579"}],"volume":"11","issue":"1","noUsgsAuthors":false,"publicationDate":"2014-09-01","publicationStatus":"PW","scienceBaseUri":"542bb80ee4b0abfb4c8096b3","contributors":{"authors":[{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568 nhitt@usgs.gov","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":4435,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"nhitt@usgs.gov","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":502326,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, David R.","contributorId":173756,"corporation":false,"usgs":false,"family":"Smith","given":"David R.","affiliations":[],"preferred":false,"id":502325,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70126913,"text":"70126913 - 2015 - Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States","interactions":[],"lastModifiedDate":"2015-06-02T11:03:52","indexId":"70126913","displayToPublicDate":"2014-09-25T10:10:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3035,"text":"Pest Management Science","active":true,"publicationSubtype":{"id":10}},"title":"Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States","docAbstract":"Complex environmental models are frequently extrapolated to overcome data limitations in space and time, but quantifying data worth to such models is rarely attempted. The authors determined which field observations most informed the parameters of agricultural system models applied to field sites in Nebraska (NE) and Maryland (MD), and identified parameters and observations that most influenced prediction uncertainty.
\nThe standard error of regression of the calibrated models was about the same at both NE (0.59) and MD (0.58), and overall reductions in prediction uncertainties of metolachlor and metolachlor ethane sulfonic acid concentrations were 98.0 and 98.6% respectively. Observation data groups reduced the prediction uncertainty by 55–90% at NE and by 28–96% at MD. Soil hydraulic parameters were well informed by the observed data at both sites, but pesticide and macropore properties had comparatively larger contributions after model calibration.
\nAlthough the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well-drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty.
\n\n
RESULTS: The standard error of regression of the calibrated models was about the same at both NE (0.59) and MD (0.58), and overall reductions in prediction uncertainties of metolachlor and metolachlor ethane sulfonic acid concentrations were 98.0 and 98.6% respectively. Observation data groups reduced the prediction uncertainty by 55–90% at NE and by 28–96% at MD. Soil hydraulic parameters were well informed by the observed data at both sites, but pesticide and macropore properties had comparatively larger contributions after model calibration.
\n\n
CONCLUSIONS: Although the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well-drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty.
","language":"English","publisher":"Wiley","doi":"10.1002/ps.3875","usgsCitation":"Nolan, B.T., Malone, R.W., Doherty, J.E., Barbash, J.E., Ma, L., and Shaner, D.L., 2015, Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States: Pest Management Science, v. 71, no. 7, p. 972-985, https://doi.org/10.1002/ps.3875.","productDescription":"14 p.","startPage":"972","endPage":"985","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-044123","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":294476,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294473,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/ps.3875"}],"country":"United States","state":"Maryl;Nebraska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -104.0535,37.8886 ], [ -104.0535,43.0017 ], [ -75.0492,43.0017 ], [ -75.0492,37.8886 ], [ -104.0535,37.8886 ] ] ] } } ] }","volume":"71","issue":"7","noUsgsAuthors":false,"publicationDate":"2014-09-11","publicationStatus":"PW","scienceBaseUri":"54252087e4b0e641df8a6d92","contributors":{"authors":[{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":502182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Malone, Robert W.","contributorId":10347,"corporation":false,"usgs":false,"family":"Malone","given":"Robert","email":"","middleInitial":"W.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":502185,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doherty, John E.","contributorId":8817,"corporation":false,"usgs":false,"family":"Doherty","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":7046,"text":"Watermark Numerical Computing","active":true,"usgs":false}],"preferred":false,"id":502184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barbash, Jack E. 0000-0001-9854-8880 jbarbash@usgs.gov","orcid":"https://orcid.org/0000-0001-9854-8880","contributorId":1003,"corporation":false,"usgs":true,"family":"Barbash","given":"Jack","email":"jbarbash@usgs.gov","middleInitial":"E.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":502181,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ma, Liwang","contributorId":6751,"corporation":false,"usgs":false,"family":"Ma","given":"Liwang","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":502183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shaner, Dale L.","contributorId":100766,"corporation":false,"usgs":true,"family":"Shaner","given":"Dale","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":502186,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70108458,"text":"70108458 - 2015 - Understanding heat and groundwater flow through continental flood basalt provinces: insights gained from alternative models of permeability/depth relationships for the Columbia Plateau, USA","interactions":[],"lastModifiedDate":"2019-07-22T12:54:07","indexId":"70108458","displayToPublicDate":"2014-09-19T14:32:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1765,"text":"Geofluids","active":true,"publicationSubtype":{"id":10}},"title":"Understanding heat and groundwater flow through continental flood basalt provinces: insights gained from alternative models of permeability/depth relationships for the Columbia Plateau, USA","docAbstract":"Heat-flow mapping of the western USA has identified an apparent low-heat-flow anomaly coincident with the Columbia Plateau Regional Aquifer System, a thick sequence of basalt aquifers within the Columbia River Basalt Group (CRBG). A heat and mass transport model (SUTRA) was used to evaluate the potential impact of groundwater flow on heat flow along two different regional groundwater flow paths. Limited in situ permeability (k) data from the CRBG are compatible with a steep permeability decrease (approximately 3.5 orders of magnitude) at 600–900 m depth and approximately 40°C. Numerical simulations incorporating this permeability decrease demonstrate that regional groundwater flow can explain lower-than-expected heat flow in these highly anisotropic (kx/kz ~ 104) continental flood basalts. Simulation results indicate that the abrupt reduction in permeability at approximately 600 m depth results in an equivalently abrupt transition from a shallow region where heat flow is affected by groundwater flow to a deeper region of conduction-dominated heat flow. Most existing heat-flow measurements within the CRBG are from shallower than 600 m depth or near regional groundwater discharge zones, so that heat-flow maps generated using these data are likely influenced by groundwater flow. Substantial k decreases at similar temperatures have also been observed in the volcanic rocks of the adjacent Cascade Range volcanic arc and at Kilauea Volcano, Hawaii, where they result from low-temperature hydrothermal alteration.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geofluids","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/gfl.12095","usgsCitation":"Burns, E., Williams, C.F., Ingebritsen, S.E., Voss, C.I., Spane, F.A., and DeAngelo, J., 2015, Understanding heat and groundwater flow through continental flood basalt provinces: insights gained from alternative models of permeability/depth relationships for the Columbia Plateau, USA: Geofluids, v. 15, no. 1-2, p. 120-138, https://doi.org/10.1111/gfl.12095.","productDescription":"19 p.","startPage":"120","endPage":"138","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-053358","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":472466,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gfl.12095","text":"Publisher Index Page"},{"id":294238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294237,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/gfl.12095"},{"id":294239,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1111/gfl.12095/abstract"}],"country":"United States","state":"Idaho;Oregon;Washington","otherGeospatial":"Columbia River Plateau","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122,44.5 ], [ -122,48.5 ], [ -116.5,48.5 ], [ -116.5,44.5 ], [ -122,44.5 ] ] ] } } ] }","volume":"15","issue":"1-2","noUsgsAuthors":false,"publicationDate":"2014-09-19","publicationStatus":"PW","scienceBaseUri":"541d3790e4b0f68901ebd9d4","contributors":{"authors":[{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":84802,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":310,"text":"Geology, Minerals, Energy and Geophysics Science Center","active":false,"usgs":true}],"preferred":false,"id":494028,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Colin F. 0000-0003-2196-5496 colin@usgs.gov","orcid":"https://orcid.org/0000-0003-2196-5496","contributorId":274,"corporation":false,"usgs":true,"family":"Williams","given":"Colin","email":"colin@usgs.gov","middleInitial":"F.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":494023,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ingebritsen, Steven E. 0000-0001-6917-9369 seingebr@usgs.gov","orcid":"https://orcid.org/0000-0001-6917-9369","contributorId":818,"corporation":false,"usgs":true,"family":"Ingebritsen","given":"Steven","email":"seingebr@usgs.gov","middleInitial":"E.","affiliations":[{"id":438,"text":"National Research Program - 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2015 - Development of ten microsatellite loci in the invasive giant African land snail, Achatina (=Lissachatina) fulica Bowdich, 1822","interactions":[],"lastModifiedDate":"2015-02-23T16:15:24","indexId":"70127956","displayToPublicDate":"2014-09-19T09:46:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1325,"text":"Conservation Genetics Resources","active":true,"publicationSubtype":{"id":10}},"title":"Development of ten microsatellite loci in the invasive giant African land snail, Achatina (=Lissachatina) fulica Bowdich, 1822","docAbstract":"A suite of tetra-nucleotide microsatellite loci were developed for the invasive giant African land snail, Achatina (=Lissachatina) fulica Bowdich, 1822, from Ion Torrent next-generation sequencing data. Ten of the 96 primer sets tested amplified consistently in 30 snails from Miami, Florida, plus 12 individuals representative of their native East Africa, Indian and Pacific Ocean regions. The loci displayed moderate levels of allelic diversity (average 5.6 alleles/locus) and heterozygosity (average 42 %). Levels of genetic diversity were sufficient to produce unique multi-locus genotypes and detect phylogeographic structuring among regional samples. The invasive A. fulica can cause extensive damage to important food crops and natural resources, including native flora and fauna. The loci characterized here will be useful for determining the origins and tracking the spread of invasions, detecting fine-scale spatial structuring and estimating demographic parameters.
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","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Groundwater","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/gwat.12216","usgsCitation":"Hughes, J.D., Langevin, C.D., and White, J., 2015, MODFLOW-based coupled surface water routing and groundwater-flow simulation: Groundwater, v. 53, no. 3, p. 452-463, https://doi.org/10.1111/gwat.12216.","productDescription":"12 p.","startPage":"452","endPage":"463","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-053378","costCenters":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"links":[{"id":294073,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294069,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/gwat.12216"}],"volume":"53","issue":"3","noUsgsAuthors":false,"publicationDate":"2014-06-05","publicationStatus":"PW","scienceBaseUri":"541a9491e4b01571b3d4cc5a","contributors":{"authors":[{"text":"Hughes, Joseph D. 0000-0003-1311-2354 jdhughes@usgs.gov","orcid":"https://orcid.org/0000-0003-1311-2354","contributorId":2492,"corporation":false,"usgs":true,"family":"Hughes","given":"Joseph","email":"jdhughes@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":501635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Langevin, Christian D. 0000-0001-5610-9759 langevin@usgs.gov","orcid":"https://orcid.org/0000-0001-5610-9759","contributorId":1030,"corporation":false,"usgs":true,"family":"Langevin","given":"Christian","email":"langevin@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":501634,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Jeremy T. jwhite@usgs.gov","contributorId":3930,"corporation":false,"usgs":true,"family":"White","given":"Jeremy T.","email":"jwhite@usgs.gov","affiliations":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"preferred":false,"id":501636,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70125494,"text":"70125494 - 2015 - Demography of the Pacific walrus (Odobenus rosmarus divergens): 1974-2006","interactions":[],"lastModifiedDate":"2015-01-05T11:05:29","indexId":"70125494","displayToPublicDate":"2014-09-17T09:47:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2671,"text":"Marine Mammal Science","active":true,"publicationSubtype":{"id":10}},"title":"Demography of the Pacific walrus (Odobenus rosmarus divergens): 1974-2006","docAbstract":"Global climate change may fundamentally alter population dynamics of many species for which baseline population parameter estimates are imprecise or lacking. Historically, the Pacific walrus is thought to have been limited by harvest, but it may become limited by global warming-induced reductions in sea ice. Loss of sea ice, on which walruses rest between foraging bouts, may reduce access to food, thus lowering vital rates. Rigorous walrus survival rate estimates do not exist, and other population parameter estimates are out of date or have well-documented bias and imprecision. To provide useful population parameter estimates we developed a Bayesian, hidden process demographic model of walrus population dynamics from 1974 through 2006 that combined annual age-specific harvest estimates with five population size estimates, six standing age structure estimates, and two reproductive rate estimates. Median density independent natural survival was high for juveniles (0.97) and adults (0.99), and annual density dependent vital rates rose from 0.06 to 0.11 for reproduction, 0.31 to 0.59 for survival of neonatal calves, and 0.39 to 0.85 for survival of older calves, concomitant with a population decline. This integrated population model provides a baseline for estimating changing population dynamics resulting from changing harvests or sea ice.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Marine Mammal Science","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/mms.12156","usgsCitation":"Taylor, R.L., and Udevitz, M.S., 2015, Demography of the Pacific walrus (Odobenus rosmarus divergens): 1974-2006: Marine Mammal Science, v. 31, no. 1, p. 231-254, https://doi.org/10.1111/mms.12156.","productDescription":"24 p.","startPage":"231","endPage":"254","numberOfPages":"24","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-050957","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":294017,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/mms.12156"},{"id":294021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"1","noUsgsAuthors":false,"publicationDate":"2014-09-05","publicationStatus":"PW","scienceBaseUri":"541a948be4b01571b3d4cc17","chorus":{"doi":"10.1111/mms.12156","url":"http://dx.doi.org/10.1111/mms.12156","publisher":"Wiley-Blackwell","authors":"Taylor Rebecca L., Udevitz Mark S.","journalName":"Marine Mammal Science","publicationDate":"9/5/2014","auditedOn":"11/1/2014"},"contributors":{"authors":[{"text":"Taylor, Rebecca L. 0000-0001-8459-7614 rebeccataylor@usgs.gov","orcid":"https://orcid.org/0000-0001-8459-7614","contributorId":5112,"corporation":false,"usgs":true,"family":"Taylor","given":"Rebecca","email":"rebeccataylor@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":501516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Udevitz, Mark S. 0000-0003-4659-138X mudevitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4659-138X","contributorId":3189,"corporation":false,"usgs":true,"family":"Udevitz","given":"Mark","email":"mudevitz@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":501515,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70169224,"text":"70169224 - 2015 - The unseen iceberg: Plant roots in arctic tundra","interactions":[],"lastModifiedDate":"2016-03-24T13:59:12","indexId":"70169224","displayToPublicDate":"2014-09-10T15:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2863,"text":"New Phytologist","active":true,"publicationSubtype":{"id":10}},"title":"The unseen iceberg: Plant roots in arctic tundra","docAbstract":"Plant roots play a critical role in ecosystem function in arctic tundra, but root dynamics in these ecosystems are poorly understood. To address this knowledge gap, we synthesized available literature on tundra roots, including their distribution, dynamics and contribution to ecosystem carbon and nutrient fluxes, and highlighted key aspects of their representation in terrestrial biosphere models. Across all tundra ecosystems, belowground plant biomass exceeded aboveground biomass, with the exception of polar desert tundra. Roots were shallowly distributed in the thin layer of soil that thaws annually, and were often found in surface organic soil horizons. Root traits – including distribution, chemistry, anatomy and resource partitioning – play an important role in controlling plant species competition, and therefore ecosystem carbon and nutrient fluxes, under changing climatic conditions, but have only been quantified for a small fraction of tundra plants. Further, the annual production and mortality of fine roots are key components of ecosystem processes in tundra, but extant data are sparse. Tundra root traits and dynamics should be the focus of future research efforts. Better representation of the dynamics and characteristics of tundra roots will improve the utility of models for the evaluation of the responses of tundra ecosystems to changing environmental conditions.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"New Phytologist","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Blackwell Publishing","publisherLocation":"London","doi":"10.1111/nph.13003","usgsCitation":"Iverson, C.M., Sloan, V.L., Sullivan, P.F., Euskirchen, E., McGuire, A.D., Norby, R.J., Walker, A.P., Warren, J.M., and Wullschleger, S.D., 2015, The unseen iceberg: Plant roots in arctic tundra: New Phytologist, v. 205, no. 1, p. 34-58, https://doi.org/10.1111/nph.13003.","productDescription":"25 p.","startPage":"34","endPage":"58","numberOfPages":"25","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-053189","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":472471,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/nph.13003","text":"Publisher Index Page"},{"id":319373,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Arctic","volume":"205","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2014-09-10","publicationStatus":"PW","scienceBaseUri":"56f50fd4e4b0f59b85e1ebde","contributors":{"authors":[{"text":"Iverson, Colleen M.","contributorId":167834,"corporation":false,"usgs":false,"family":"Iverson","given":"Colleen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":623777,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sloan, Victoria L.","contributorId":167839,"corporation":false,"usgs":false,"family":"Sloan","given":"Victoria","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":623778,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sullivan, Patrick F.","contributorId":49225,"corporation":false,"usgs":true,"family":"Sullivan","given":"Patrick","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":623779,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Euskirchen, E.S.","contributorId":44737,"corporation":false,"usgs":true,"family":"Euskirchen","given":"E.S.","affiliations":[],"preferred":false,"id":623780,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGuire, A. David 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":166708,"corporation":false,"usgs":true,"family":"McGuire","given":"A.","email":"ffadm@usgs.gov","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":623361,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Norby, Richard J. 0000-0002-0238-9828","orcid":"https://orcid.org/0000-0002-0238-9828","contributorId":167836,"corporation":false,"usgs":false,"family":"Norby","given":"Richard","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":623781,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Walker, Anthony P. 0000-0003-0557-5594","orcid":"https://orcid.org/0000-0003-0557-5594","contributorId":167843,"corporation":false,"usgs":false,"family":"Walker","given":"Anthony","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":623782,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Warren, Jeffrey M.","contributorId":16297,"corporation":false,"usgs":true,"family":"Warren","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":623783,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wullschleger, Stan D.","contributorId":167343,"corporation":false,"usgs":false,"family":"Wullschleger","given":"Stan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":623784,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70154804,"text":"70154804 - 2015 - Comparing methods for estimating larval sea lamprey (Petromyzon marinus) density in the St. Marys River for the purposes of control","interactions":[],"lastModifiedDate":"2015-07-08T13:49:54","indexId":"70154804","displayToPublicDate":"2014-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Comparing methods for estimating larval sea lamprey (Petromyzon marinus) density in the St. Marys River for the purposes of control","docAbstract":"The St. Marys River is a major producer of parasitic sea lampreys (Petromyzon marinus) to Lake Huron making it an important area for larval control. Bayluscide treatments are conducted in areas of high larval density requiring density estimation at fine spatial scales to inform treatment decisions. We evaluated six methods of estimating spatially specific density including the currently used sampling-based estimates, a generalized linear model (GLM) based on mean larval density per plot, a GLM based on larval density per sample, a generalized additive model based on mean larval density per plot, a spatial age-structured population model, and a hybrid approach, which averaged the best performing sampling- and model-based methods. Methods were evaluated based on accuracy in matching independent validation data. Specifically, the methods were evaluated based on their ability to project plot-level larval density, identify high density plots for treatment, and rank plots in order based on density resulting in high numbers of sea lampreys killed per hectare treated. Performance was variable, and no single method outperformed the others for all metrics. Although the sampling-based estimation method and the GLM based on catch data performed adequately for estimating density and identifying high density plots, the hybrid approach was identified as the best method to inform sea lamprey control decisions in the St. Marys River due to its consistent performance. Incorporating model-based approaches should lead to a more efficient and effective treatment program in the St. Marys River and aid in making decisions about the allocation of control resources.
\n\n
Groundwater temperature measurements in a shallow coastal aquifer in Virginia Beach, Virginia, USA, suggest groundwater warming of +4.1 °C relative to deeper geothermal gradients. Observed warming is related to timing and depth of influence of two potential thermal drivers—atmospheric temperature increases and urbanization. Results indicate that up to 30 % of groundwater warming at the water table can be attributed to atmospheric warming while up to 70 % of warming can be attributed to urbanization. Groundwater temperature readings to 30-m depth correlate positively with percentage of impervious cover and negatively with percentage of tree canopy cover; thus, these two land-use metrics explain up to 70 % of warming at the water table. Analytical and numerical modeling results indicate that an average vertical groundwater temperature profile for the study area, constructed from repeat measurement at 11 locations over 15 months, is consistent with the timing of land-use change over the past century in Virginia Beach. The magnitude of human-induced warming at the water table (+4.1 °C) is twice the current seasonal temperature variation, indicating the potential for ecological impacts on wetlands and estuaries receiving groundwater discharge from shallow aquifers.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-014-1189-y","usgsCitation":"Eggleston, J.R., and McCoy, K.J., 2015, Assessing the magnitude and timing of anthropogenic warming of a shallow aquifer: example from Virginia Beach, USA: Hydrogeology Journal, v. 23, no. 1, p. 105-120, https://doi.org/10.1007/s10040-014-1189-y.","productDescription":"16 p.","startPage":"105","endPage":"120","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-053847","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":297301,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"projection":"Universal Transverse Mercator, Zone 18 North","datum":"North American Datum 1983","country":"United States","state":"Virginia","city":"Virginia Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.34536743164061,\n 36.54936246839778\n ],\n [\n -76.34536743164061,\n 36.97732387852746\n ],\n [\n -75.85578918457031,\n 36.97732387852746\n ],\n [\n -75.85578918457031,\n 36.54936246839778\n ],\n [\n -76.34536743164061,\n 36.54936246839778\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2014-09-19","publicationStatus":"PW","scienceBaseUri":"54dd2b3ce4b08de9379b32bd","contributors":{"authors":[{"text":"Eggleston, John R. 0000-0001-6633-3041 jegglest@usgs.gov","orcid":"https://orcid.org/0000-0001-6633-3041","contributorId":3068,"corporation":false,"usgs":true,"family":"Eggleston","given":"John","email":"jegglest@usgs.gov","middleInitial":"R.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":538616,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCoy, Kurt J. 0000-0002-9756-8238 kjmccoy@usgs.gov","orcid":"https://orcid.org/0000-0002-9756-8238","contributorId":1391,"corporation":false,"usgs":true,"family":"McCoy","given":"Kurt","email":"kjmccoy@usgs.gov","middleInitial":"J.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":538617,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70137563,"text":"70137563 - 2015 - Variations in population vulnerability to tectonic and landslide-related tsunami hazards in Alaska","interactions":[],"lastModifiedDate":"2015-01-09T15:56:46","indexId":"70137563","displayToPublicDate":"2014-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Variations in population vulnerability to tectonic and landslide-related tsunami hazards in Alaska","docAbstract":"Effective tsunami risk reduction requires an understanding of how at-risk populations are specifically vulnerable to tsunami threats. Vulnerability assessments primarily have been based on single hazard zones, even though a coastal community may be threatened by multiple tsunami sources that vary locally in terms of inundation extents and wave arrival times. We use the Alaskan coastal communities of Cordova, Kodiak, Seward, Valdez, and Whittier (USA), as a case study to explore population vulnerability to multiple tsunami threats. We use anisotropic pedestrian evacuation models to assess variations in population exposure as a function of travel time out of hazard zones associated with tectonic and landslide-related tsunamis (based on scenarios similar to the 1964 M w9.2 Good Friday earthquake and tsunami disaster). Results demonstrate that there are thousands of residents, employees, and business customers in tsunami hazard zones associated with tectonically generated waves, but that at-risk individuals will likely have sufficient time to evacuate to high ground before waves are estimated to arrive 30–60 min after generation. Tsunami hazard zones associated with submarine landslides initiated by a subduction zone earthquake are smaller and contain fewer people, but many at-risk individuals may not have enough time to evacuate as waves are estimated to arrive in 1–2 min and evacuations may need to occur during earthquake ground shaking. For all hazard zones, employees and customers at businesses far outnumber residents at their homes and evacuation travel times are highest on docks and along waterfronts. Results suggest that population vulnerability studies related to tsunami hazards should recognize non-residential populations and differences in wave arrival times if emergency managers are to develop realistic preparedness and outreach efforts.
","language":"English","publisher":"Springer","doi":"10.1007/s11069-014-1399-6","usgsCitation":"Wood, N.J., and Peters, J., 2015, Variations in population vulnerability to tectonic and landslide-related tsunami hazards in Alaska: Natural Hazards, v. 75, no. 2, p. 1811-1831, https://doi.org/10.1007/s11069-014-1399-6.","productDescription":"21 p.","startPage":"1811","endPage":"1831","numberOfPages":"21","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057130","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":472474,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11069-014-1399-6","text":"Publisher Index Page"},{"id":297117,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.09375,\n 57.42129439209407\n ],\n [\n -156.09375,\n 62.08331486294795\n ],\n [\n -144.4482421875,\n 62.08331486294795\n ],\n [\n -144.4482421875,\n 57.42129439209407\n ],\n [\n -156.09375,\n 57.42129439209407\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2014-09-07","publicationStatus":"PW","scienceBaseUri":"54dd2c82e4b08de9379b3849","contributors":{"authors":[{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":537976,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peters, Jeff 0000-0003-4312-0590 jpeters@usgs.gov","orcid":"https://orcid.org/0000-0003-4312-0590","contributorId":4711,"corporation":false,"usgs":true,"family":"Peters","given":"Jeff","email":"jpeters@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":537977,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70137735,"text":"70137735 - 2015 - Age and growth of round gobies in Lake Michigan, with preliminary mortality estimation","interactions":[],"lastModifiedDate":"2015-01-12T09:56:26","indexId":"70137735","displayToPublicDate":"2014-09-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Age and growth of round gobies in Lake Michigan, with preliminary mortality estimation","docAbstract":"The round goby (Neogobius melanostomus) is a prevalent invasive species throughout Lake Michigan, as well as other Laurentian Great Lakes, yet little information is available on spatial variation in round goby growth within one body of water. Age and growth of round goby at three areas of Lake Michigan were studied by otolith analysis from a sample of 659 specimens collected from 2008 to 2012. Total length (TL) ranged from 48 to 131 mm for Sturgeon Bay, from 50 to 125 mm for Waukegan, and from 54 to 129 mm for Sleeping Bear Dunes. Ages ranged from 2 to 7 years for Sturgeon Bay, from 2 to 5 years for Waukegan, and from 2 to 6 years for Sleeping Bear Dunes. Area-specific and sex-specific body–otolith relationships were used to back-calculate estimates of total length at age, which were fitted to von Bertalanffy models to estimate growth rates. For both sexes, round gobies at Sleeping Bear Dunes and Waukegan grew significantly faster than those at Sturgeon Bay. However, round goby growth did not significantly differ between Sleeping Bear Dunes and Waukegan for either sex. At all three areas of Lake Michigan, males grew significantly faster than females. Based on catch curve analysis, estimates of annual mortality rates ranged from 0.79 to 0.84. These relatively high mortality rates suggested that round gobies may be under predatory control in Lake Michigan.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2014.07.003","usgsCitation":"Huo, B., Madenjian, C.P., Xie, C., Zhao, Y., O’Brien, T.P., and Czesny, S.J., 2015, Age and growth of round gobies in Lake Michigan, with preliminary mortality estimation: Journal of Great Lakes Research, v. 40, no. 3, p. 712-720, https://doi.org/10.1016/j.jglr.2014.07.003.","productDescription":"9 p.","startPage":"712","endPage":"720","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-053856","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":297122,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","volume":"40","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54dd2b2be4b08de9379b3284","contributors":{"authors":[{"text":"Huo, Bin","contributorId":127463,"corporation":false,"usgs":false,"family":"Huo","given":"Bin","email":"","affiliations":[{"id":6955,"text":"College of Fisheries, Huazhong Agricultural University","active":true,"usgs":false}],"preferred":false,"id":538000,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":537999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xie, Cong X.","contributorId":138597,"corporation":false,"usgs":false,"family":"Xie","given":"Cong X.","affiliations":[{"id":12457,"text":"Huazhong Agricultural University, College of Fisheries","active":true,"usgs":false}],"preferred":false,"id":538001,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhao, Yingming","contributorId":49752,"corporation":false,"usgs":true,"family":"Zhao","given":"Yingming","affiliations":[],"preferred":false,"id":538002,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O’Brien, Timothy P. 0000-0003-4502-5204 tiobrien@usgs.gov","orcid":"https://orcid.org/0000-0003-4502-5204","contributorId":2662,"corporation":false,"usgs":true,"family":"O’Brien","given":"Timothy","email":"tiobrien@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":538003,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Czesny, Sergiusz J.","contributorId":138598,"corporation":false,"usgs":false,"family":"Czesny","given":"Sergiusz","email":"","middleInitial":"J.","affiliations":[{"id":12458,"text":"Illinois Natural History Survey, Lake Michigan Biological Station","active":true,"usgs":false}],"preferred":false,"id":538004,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}