Semipermanent GPS (SPGPS) is an alternative to conventional campaign or survey-mode GPS (SGPS) and to continuous GPS (CGPS) that offers several advantages for monitoring ground deformation. Unlike CGPS installations, SPGPS stations can be deployed quickly in response to changing volcanic conditions or earthquake activity such as a swarm or aftershock sequence. SPGPS networks can be more focused or more extensive than CGPS installations, because SPGPS equipment can be moved from station to station quickly to increase the total number of stations observed in a given time period. SPGPS networks are less intrusive on the landscape than CGPS installations, which makes it easier to satisfy land-use restrictions in ecologically sensitive areas. SPGPS observations are preferred over SGPS measurements because they provide better precision with only a modest increase in the amount of time, equipment, and personnel required in the field. We describe three applications of the SPGPS method that demonstrate its utility and flexibility. At the Yellowstone caldera, Wyoming, a 9-station SPGPS network serves to densify larger preexisting networks of CGPS and SGPS stations. At the Three Sisters volcanic center, Oregon, a 14-station SPGPS network complements an SGPS network and extends the geographic coverage provided by 3 CGPS stations permitted under wilderness land-use restrictions. In the Basin and Range province in northwest Nevada, a 6-station SPGPS network has been established in response to a prolonged earthquake swarm in an area with only sparse preexisting geodetic coverage. At Three Sisters, the estimated precision of station velocities based on annual ~ 3 month summertime SPGPS occupations from 2009 to 2015 is approximately half that for nearby CGPS stations. Conversely, SPGPS-derived station velocities are about twice as precise as those based on annual ~ 1 week SGPS measurements. After 5 years of SPGPS observations at Three Sisters, the precision of velocity determinations is estimated to be 0.5 mm/yr in longitude, 0.6 mm/yr in latitude, and 0.8 mm/yr in height. We conclude that an optimal approach to monitoring volcano deformation includes complementary CGPS and SPGPS networks, periodic InSAR observations, and measurements from in situ borehole sensors such as tiltmeters or strainmeters. This comprehensive approach provides the spatial and temporal detail necessary to adequately characterize a complex and evolving deformation pattern. Such information is essential to multi-parameter models of magmatic or tectonic processes that can help to guide research efforts, and also to inform hazards assessments and land-use planning decisions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2017.03.007","usgsCitation":"Dzurisin, D., Lisowski, M., and Wicks, C., 2017, Semipermanent GPS (SPGPS) as a volcano monitoring tool: Rationale, method, and applications: Journal of Volcanology and Geothermal Research, v. 344, p. 40-51, https://doi.org/10.1016/j.jvolgeores.2017.03.007.","productDescription":"12 p.","startPage":"40","endPage":"51","ipdsId":"IP-076627","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":469500,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2017.03.007","text":"Publisher Index Page"},{"id":346057,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"344","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ca15a5e4b017cf31404197","contributors":{"authors":[{"text":"Dzurisin, Daniel 0000-0002-0138-5067 dzurisin@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-5067","contributorId":538,"corporation":false,"usgs":true,"family":"Dzurisin","given":"Daniel","email":"dzurisin@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":711125,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lisowski, Michael 0000-0003-4818-2504 mlisowski@usgs.gov","orcid":"https://orcid.org/0000-0003-4818-2504","contributorId":637,"corporation":false,"usgs":true,"family":"Lisowski","given":"Michael","email":"mlisowski@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":711126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wicks, Charles W. Jr. cwicks@usgs.gov","contributorId":3476,"corporation":false,"usgs":true,"family":"Wicks","given":"Charles W.","suffix":"Jr.","email":"cwicks@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":711127,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191595,"text":"70191595 - 2017 - Holocene earthquakes of magnitude 7 during westward escape of the Olympic Mountains, Washington","interactions":[],"lastModifiedDate":"2020-12-21T12:54:10.400554","indexId":"70191595","displayToPublicDate":"2017-09-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Holocene earthquakes of magnitude 7 during westward escape of the Olympic Mountains, Washington","docAbstract":"The Lake Creek–Boundary Creek fault, previously mapped in Miocene bedrock as an oblique thrust on the north flank of the Olympic Mountains, poses a significant earthquake hazard. Mapping using 2015 light detection and ranging (lidar) confirms 2004 lidar mapping of postglacial (
Faulted terrace risers are semi-planar features commonly used to constrain Quaternary slip rates along strike-slip faults. These landforms are difficult to date directly and therefore their ages are commonly bracketed by age estimates of the adjacent upper and lower terrace surfaces. However, substantial differences in the ages of the upper and lower terrace surfaces (a factor of 2.4 difference observed globally) produce large uncertainties in the slip-rate estimate. In this investigation, we explore how the full range of displacements and bounding ages from multiple faulted terrace risers can be combined to yield a more accurate fault slip rate. We use 0.25-m cell size digital terrain models derived from airborne lidar data to analyze three sites where terrace risers are offset right-laterally by the Honey Lake fault in NE California, USA. We use ages for locally extensive subhorizontal surfaces to bracket the time of riser formation: an upper surface is the bed of abandoned Lake Lahontan having an age of 15.8 ± 0.6 ka and a lower surface is a fluvial terrace abandoned at 4.7 ± 0.1 ka. We estimate lateral offsets of the risers ranging between 6.6 and 28.3 m (median values), a greater than fourfold difference in values. The amount of offset corresponds to the riser's position relative to modern stream meanders: the smallest offset is in a meander cutbank position, whereas the larger offsets are in straight channel or meander point-bar positions. Taken in isolation, the individual terrace-riser offsets yield slip rates ranging from 0.3 to 7.1 mm/a. However, when the offset values are collectively assessed in a probabilistic framework, we find that a uniform (linear) slip rate of 1.6 mm/a (1.4–1.9 mm/a at 95% confidence) can satisfy the data, within their respective uncertainties. This investigation demonstrates that integrating observations of multiple offset elements (crest, midpoint, and base) from numerous faulted and dated terrace risers at closely spaced sites can refine slip-rate estimates on strike-slip faults.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2017.08.021","usgsCitation":"Gold, R.D., Briggs, R.W., Crone, A.J., and DuRoss, C., 2017, Refining fault slip rates using multiple displaced terrace risers-An example from the Honey Lake fault, NE California, USA: Earth and Planetary Science Letters, v. 477, p. 134-146, https://doi.org/10.1016/j.epsl.2017.08.021.","productDescription":"13 p.","startPage":"134","endPage":"146","ipdsId":"IP-088635","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":461397,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2017.08.021","text":"Publisher Index Page"},{"id":346047,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Honey Lake Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.1333,\n 40.0417\n ],\n [\n -120.1,\n 40.0417\n ],\n [\n -120.1,\n 40.0583\n ],\n [\n -120.1333,\n 40.0583\n ],\n [\n -120.1333,\n 40.0417\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"477","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ca15aae4b017cf314041b0","contributors":{"authors":[{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":711076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":4136,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":711116,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crone, Anthony J. 0000-0002-3006-406X crone@usgs.gov","orcid":"https://orcid.org/0000-0002-3006-406X","contributorId":790,"corporation":false,"usgs":true,"family":"Crone","given":"Anthony","email":"crone@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":711117,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":711118,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191080,"text":"70191080 - 2017 - Structural equation model of total phosphorus loads in the Red River of the North Basin, USA and Canada","interactions":[],"lastModifiedDate":"2017-10-12T19:51:14","indexId":"70191080","displayToPublicDate":"2017-09-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Structural equation model of total phosphorus loads in the Red River of the North Basin, USA and Canada","docAbstract":"Attribution of the causes of trends in nutrient loading is often limited to correlation, qualitative reasoning, or references to the work of others. This paper represents efforts to improve causal attribution of water-quality changes. The Red River of the North basin provides a regional test case because of international interest in the reduction of total phosphorus loads and the availability of long-term total phosphorus data and ancillary geospatial data with the potential to explain changes in water quality over time. The objectives of the study are to investigate structural equation modeling methods for application to water-quality problems and to test causal hypotheses related to the drivers of total phosphorus loads over the period 1970 to 2012. Multiple working hypotheses that explain total phosphorus loads and methods for estimating missing ancillary data were developed, and water-quality related challenges to structural equation modeling (including skewed data and scaling issues) were addressed. The model indicates that increased precipitation in season 1 (November–February) or season 2 (March–June) would increase total phosphorus loads in the basin. The effect of agricultural practices on total phosphorus loads was significant, although the effect is about one-third of the effect of season 1 precipitation. The structural equation model representing loads at six sites in the basin shows that climate and agricultural practices explain almost 60% of the annual total phosphorus load in the Red River of the North basin. The modeling process and the unexplained variance highlight the need for better ancillary long-term data for causal assessments.
","language":"English","publisher":"ACSESS","doi":"10.2134/jeq2017.04.0131","usgsCitation":"Ryberg, K.R., 2017, Structural equation model of total phosphorus loads in the Red River of the North Basin, USA and Canada: Journal of Environmental Quality, v. 46, no. 5, p. 1072-1080, https://doi.org/10.2134/jeq2017.04.0131.","productDescription":"9 p.","startPage":"1072","endPage":"1080","ipdsId":"IP-075962","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":469503,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2134/jeq2017.04.0131","text":"Publisher Index Page"},{"id":346042,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Red River of the North Basin","volume":"46","issue":"5","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ca15a7e4b017cf314041a4","contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":711096,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70191053,"text":"70191053 - 2017 - Projecting impacts of climate change on water availability using artificial neural network techniques","interactions":[],"lastModifiedDate":"2017-09-25T11:54:31","indexId":"70191053","displayToPublicDate":"2017-09-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2501,"text":"Journal of Water Resources Planning and Management","active":true,"publicationSubtype":{"id":10}},"title":"Projecting impacts of climate change on water availability using artificial neural network techniques","docAbstract":"Lago Loíza reservoir in east-central Puerto Rico is one of the primary sources of public water supply for the San Juan metropolitan area. To evaluate and predict the Lago Loíza water budget, an artificial neural network (ANN) technique is trained to predict river inflows. A method is developed to combine ANN-predicted daily flows with ANN-predicted 30-day cumulative flows to improve flow estimates. The ANN application trains well for representing 2007–2012 and the drier 1994–1997 periods. Rainfall data downscaled from global circulation model (GCM) simulations are used to predict 2050–2055 conditions. Evapotranspiration is estimated with the Hargreaves equation using minimum and maximum air temperatures from the downscaled GCM data. These simulated 2050–2055 river flows are input to a water budget formulation for the Lago Loíza reservoir for comparison with 2007–2012. The ANN scenarios require far less computational effort than a numerical model application, yet produce results with sufficient accuracy to evaluate and compare hydrologic scenarios. This hydrologic tool will be useful for future evaluations of the Lago Loíza reservoir and water supply to the San Juan metropolitan area.
Increasing reliance on non-renewable mineral resources reinforces the need for identifying potential supply constraints before they occur. The US National Science and Technology Council recently released a report that outlines a methodology for screening potentially critical minerals based on three indicators: supply risk (R), production growth (G), and market dynamics (M). This early-warning screening was initially applied to 78 minerals across the years 1996 to 2013 and identified a subset of minerals as “potentially critical” based on the geometric average of these indicators—designated as criticality potential (C). In this study, the screening methodology has been updated to include data for 2014, as well as to incorporate revisions and modifications to the data, where applicable. Overall, C declined in 2014 for the majority of minerals examined largely due to decreases in production concentration and price volatility. However, the results vary considerably across minerals, with some minerals, such as gallium, recording increases for all three indicators. In addition to assessing magnitudinal changes, this analysis also examines the significance of the change relative to historical variation for each mineral. For example, although mined nickel’s R declined modestly in 2014 in comparison to that of other minerals, it was by far the largest annual change recorded for mined nickel across all years examined and is attributable to Indonesia’s ban on the export of unprocessed minerals. Based on the 2014 results, 20 minerals with the highest C values have been identified for further study including the rare earths, gallium, germanium, rhodium, tantalum, and tungsten.
","language":"English","publisher":"Springer","doi":"10.1007/s13563-017-0119-6","usgsCitation":"McCullough, E.A., and Nassar, N., 2017, Assessment of critical minerals: Updated application of an early-warning screening methodology: Mineral Economics, v. 30, no. 3, p. 257-272, https://doi.org/10.1007/s13563-017-0119-6.","productDescription":"16 p.","startPage":"257","endPage":"272","ipdsId":"IP-090603","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":469507,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s13563-017-0119-6","text":"External Repository"},{"id":345990,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-19","publicationStatus":"PW","scienceBaseUri":"59c4cf95e4b017cf313d3cab","contributors":{"authors":[{"text":"McCullough, Erin A. 0000-0002-9072-7021 emccullough@usgs.gov","orcid":"https://orcid.org/0000-0002-9072-7021","contributorId":196629,"corporation":false,"usgs":true,"family":"McCullough","given":"Erin","email":"emccullough@usgs.gov","middleInitial":"A.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":false,"id":710953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nassar, Nedal 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":196630,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":false,"id":710954,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191018,"text":"70191018 - 2017 - Do you hear what I see? Vocalization relative to visual detection rates of Hawaiian hoary bats (Lasiurus cinereus semotus)","interactions":[],"lastModifiedDate":"2018-01-04T08:28:18","indexId":"70191018","displayToPublicDate":"2017-09-21T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Do you hear what I see? Vocalization relative to visual detection rates of Hawaiian hoary bats (Lasiurus cinereus semotus)","title":"Do you hear what I see? Vocalization relative to visual detection rates of Hawaiian hoary bats (Lasiurus cinereus semotus)","docAbstract":"Bats vocalize during flight as part of the sensory modality called echolocation, but very little is known about whether flying bats consistently call. Occasional vocal silence during flight when bats approach prey or conspecifics has been documented for relatively few species and situations. Bats flying alone in clutter-free airspace are not known to forgo vocalization, yet prior observations suggested possible silent behavior in certain, unexpected situations. Determining when, why, and where silent behavior occurs in bats will help evaluate major assumptions of a primary monitoring method for bats used in ecological research, management, and conservation. In this study, we recorded flight activity of Hawaiian hoary bats (Lasiurus cinereus semotus) under seminatural conditions using both thermal video cameras and acoustic detectors. Simultaneous video and audio recordings from 20 nights of observation at 10 sites were analyzed for correspondence between detection methods, with a focus on video observations in three distance categories for which accompanying vocalizations were detected. Comparison of video and audio detections revealed that a high proportion of Hawaiian hoary bats “seen” on video were not simultaneously “heard.” On average, only about one in three visual detections within a night had an accompanying call detection, but this varied greatly among nights. Bats flying on curved flight paths and individuals nearer the cameras were more likely to be detected by both methods. Feeding and social calls were detected, but no clear pattern emerged from the small number of observations involving closely interacting bats. These results may indicate that flying Hawaiian hoary bats often forgo echolocation, or do not always vocalize in a way that is detectable with common sampling and monitoring methods. Possible reasons for the low correspondence between visual and acoustic detections range from methodological to biological and include a number of biases associated with the propagation and detection of sound, cryptic foraging strategies, or conspecific presence. Silent flight behavior may be more prevalent in echolocating bats than previously appreciated, has profound implications for ecological research, and deserves further characterization and study.
Prior to the arrival of the Dawn spacecraft at Ceres, the dwarf planet was anticipated to be ice-rich. Searches for morphological features related to ice have been ongoing during Dawn's mission at Ceres. Here we report the identification of pitted terrains associated with fresh Cerean impact craters. The Cerean pitted terrains exhibit strong morphological similarities to pitted materials previously identified on Mars (where ice is implicated in pit development) and Vesta (where the presence of ice is debated). We employ numerical models to investigate the formation of pitted materials on Ceres and discuss the relative importance of water ice and other volatiles in pit development there. We conclude that water ice likely plays an important role in pit development on Ceres. Similar pitted terrains may be common in the asteroid belt and may be of interest to future missions motivated by both astrobiology and in situ resource utilization.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2017GL073970","usgsCitation":"Sizemore, H., Platz, T., Schorghofer, N., Prettyman, T., De Sanctis, M., Crown, D.A., Schmedemann, N., Nessemann, A., Kneissl, T., Marchi, S., Schenk, P.M., Bland, M.T., Schmidt, B., Hughson, K.H., Tosi, F., Zambon, F., Mest, S., Yingst, R., Williams, D., Russell, C., and Raymond, C., 2017, Pitted terrains on (1) Ceres and implications for shallow subsurface volatile distribution: Geophysical Research Letters, v. 44, no. 13, p. 6570-6578, https://doi.org/10.1002/2017GL073970.","productDescription":"9 p.","startPage":"6570","endPage":"6578","ipdsId":"IP-082076","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":469505,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doaj.org/article/9ec60cdecbc24e7faac0fa4f772ddd6c","text":"Publisher Index Page"},{"id":345992,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Ceres","volume":"44","issue":"13","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-15","publicationStatus":"PW","scienceBaseUri":"59c4cf96e4b017cf313d3cb3","contributors":{"authors":[{"text":"Sizemore, H.G.","contributorId":86195,"corporation":false,"usgs":false,"family":"Sizemore","given":"H.G.","email":"","affiliations":[{"id":13179,"text":"Planetary Science Institute","active":true,"usgs":false}],"preferred":false,"id":710937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Platz, Thomas","contributorId":128459,"corporation":false,"usgs":false,"family":"Platz","given":"Thomas","affiliations":[{"id":7175,"text":"Institute of Geological Sciences, Planetary Sciences and Remote Sensing, Freie Universitat Berlin","active":true,"usgs":false},{"id":34668,"text":"Max Planck Institute for Solar System Research, Göttingen, Germany","active":true,"usgs":false}],"preferred":false,"id":710938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schorghofer, Norbert","contributorId":196619,"corporation":false,"usgs":false,"family":"Schorghofer","given":"Norbert","email":"","affiliations":[{"id":24732,"text":"Planetary Science Institute, Tucson","active":true,"usgs":false}],"preferred":false,"id":710939,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prettyman, Thomas","contributorId":196620,"corporation":false,"usgs":false,"family":"Prettyman","given":"Thomas","affiliations":[{"id":13179,"text":"Planetary Science Institute","active":true,"usgs":false}],"preferred":false,"id":710940,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"De Sanctis, Maria Christina","contributorId":196621,"corporation":false,"usgs":false,"family":"De Sanctis","given":"Maria Christina","affiliations":[{"id":34654,"text":"Istituto di Astrofisica e Planetologia Spaziali, INAF","active":true,"usgs":false}],"preferred":false,"id":710941,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crown, David A.","contributorId":196622,"corporation":false,"usgs":false,"family":"Crown","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":24732,"text":"Planetary Science Institute, Tucson","active":true,"usgs":false}],"preferred":false,"id":710942,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmedemann, Nico","contributorId":196623,"corporation":false,"usgs":false,"family":"Schmedemann","given":"Nico","email":"","affiliations":[{"id":34669,"text":"Department of Earth Sciences, Freie Universität Berlin, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":710943,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nessemann, Andeas","contributorId":196624,"corporation":false,"usgs":false,"family":"Nessemann","given":"Andeas","email":"","affiliations":[{"id":34669,"text":"Department of Earth Sciences, Freie Universität Berlin, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":710944,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kneissl, Thomas","contributorId":196625,"corporation":false,"usgs":false,"family":"Kneissl","given":"Thomas","email":"","affiliations":[{"id":34669,"text":"Department of Earth Sciences, Freie Universität Berlin, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":710945,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marchi, Simone","contributorId":172689,"corporation":false,"usgs":false,"family":"Marchi","given":"Simone","email":"","affiliations":[{"id":27081,"text":"Southwest Research Inst.","active":true,"usgs":false}],"preferred":false,"id":710946,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Schenk, Paul M.","contributorId":196626,"corporation":false,"usgs":false,"family":"Schenk","given":"Paul","email":"","middleInitial":"M.","affiliations":[{"id":12445,"text":"Lunar and Planetary Institute","active":true,"usgs":false}],"preferred":false,"id":710947,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Bland, Michael T. 0000-0001-5543-1519 mbland@usgs.gov","orcid":"https://orcid.org/0000-0001-5543-1519","contributorId":146287,"corporation":false,"usgs":true,"family":"Bland","given":"Michael","email":"mbland@usgs.gov","middleInitial":"T.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":710936,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Schmidt, B.E.","contributorId":177354,"corporation":false,"usgs":false,"family":"Schmidt","given":"B.E.","email":"","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":710948,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Hughson, Kynan H.G.","contributorId":192186,"corporation":false,"usgs":false,"family":"Hughson","given":"Kynan","email":"","middleInitial":"H.G.","affiliations":[{"id":32998,"text":"Department of Earth, Planetary, and Space Sciences, University of California, Los Angeles, CA, USA","active":true,"usgs":false}],"preferred":false,"id":710987,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Tosi, F.","contributorId":9472,"corporation":false,"usgs":false,"family":"Tosi","given":"F.","email":"","affiliations":[{"id":34654,"text":"Istituto di Astrofisica e Planetologia Spaziali, INAF","active":true,"usgs":false}],"preferred":false,"id":710988,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Zambon, F","contributorId":145548,"corporation":false,"usgs":false,"family":"Zambon","given":"F","affiliations":[{"id":16145,"text":"Italian Space Agency","active":true,"usgs":false},{"id":34654,"text":"Istituto di Astrofisica e Planetologia Spaziali, INAF","active":true,"usgs":false}],"preferred":false,"id":710989,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Mest, S.C.","contributorId":177355,"corporation":false,"usgs":false,"family":"Mest","given":"S.C.","affiliations":[{"id":24732,"text":"Planetary Science Institute, Tucson","active":true,"usgs":false}],"preferred":false,"id":710990,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Yingst, R.A.","contributorId":101370,"corporation":false,"usgs":false,"family":"Yingst","given":"R.A.","email":"","affiliations":[{"id":24732,"text":"Planetary Science Institute, Tucson","active":true,"usgs":false}],"preferred":false,"id":710991,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Williams, D.A.","contributorId":98048,"corporation":false,"usgs":false,"family":"Williams","given":"D.A.","email":"","affiliations":[{"id":7114,"text":"Arizona State Unviersity","active":true,"usgs":false}],"preferred":false,"id":710992,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Russell, C.T.","contributorId":32275,"corporation":false,"usgs":false,"family":"Russell","given":"C.T.","email":"","affiliations":[{"id":33607,"text":"University of California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":710993,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Raymond, C.A.","contributorId":50301,"corporation":false,"usgs":false,"family":"Raymond","given":"C.A.","email":"","affiliations":[{"id":18954,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA","active":true,"usgs":false}],"preferred":false,"id":710994,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70190099,"text":"sir20175086 - 2017 - Multiple-source tracking: Investigating sources of pathogens, nutrients, and sediment in the Upper Little River Basin, Kentucky, water years 2013–14","interactions":[],"lastModifiedDate":"2017-09-21T14:16:10","indexId":"sir20175086","displayToPublicDate":"2017-09-20T16:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5086","title":"Multiple-source tracking: Investigating sources of pathogens, nutrients, and sediment in the Upper Little River Basin, Kentucky, water years 2013–14","docAbstract":"The South Fork Little River (SFLR) and the North Fork Little River (NFLR) are two major headwater tributaries that flow into the Little River just south of Hopkinsville, Kentucky. Both tributaries are included in those water bodies in Kentucky and across the Nation that have been reported with declining water quality. Each tributary has been listed by the Kentucky Energy and Environment Cabinet—Kentucky Division of Water in the 303(d) List of Waters for Kentucky Report to Congress as impaired by nutrients, pathogens, and sediment for contact recreation from point and nonpoint sources since 2002. In 2009, the Kentucky Energy and Environment Cabinet—Kentucky Division of Water developed a pathogen total maximum daily load (TMDL) for the Little River Basin including the SFLR and NFLR Basins. Future nutrient and suspended-sediment TMDLs are planned once nutrient criteria and suspended-sediment protocols have been developed for Kentucky. In this study, different approaches were used to identify potential sources of fecal-indicator bacteria (FIB), nitrate, and suspended sediment; to inform the TMDL process; and to aid in the implementation of effective watershed-management activities. The main focus of source identification was in the SFLR Basin.
To begin understanding the potential sources of fecal contamination, samples were collected at 19 sites for densities of FIB (E. coli) in water and fluvial sediment and at 11 sites for Bacteroidales genetic markers (General AllBac, human HF183, ruminant BoBac, canid BacCan, and waterfowl GFD) during the recreational season (May through October) in 2013 and 2014. Results indicated 34 percent of all E. coli water samples (n=227 samples) did not meet the U.S. Environmental Protection Agency 2012 recommended national criteria for primary recreational waters. No criterion currently exists for E. coli in fluvial sediment. By use of the Spearman’s rank correlation test, densities of FIB in fluvial sediments were observed to have a statistically significant positive correlation with drainage area. As drainage area increased, so did the densities of FIB in the fluvial sediments. There was no statistically significant correlation between drainage area and FIB in water. The human-associated marker (HF183) was found above the detection limit in 26 percent of the samples (n=120 samples); a higher proportion of positive samples was in the NFLR Basin. The ruminant-associated marker (BoBac) was above the detection limit in 65 percent of samples; a higher proportion of positive samples was in the headwaters of the SFLR Basin.
Nutrient yields differed between the SFLR and NFLR Basins. Comparatively, the SFLR Basin produced the largest estimated mean yields of total nitrogen (16,000 pounds per year per square mile (lb/yr/mi2) and nitrite plus nitrate nitrogen (12,500 lb/yr/mi2), and the NFLR Basin produced the largest estimated mean yields of ammonia plus organic nitrogen (4,700 lb/yr/mi2), total phosphorus (1,100 lb/yr/mi2), and orthophosphorus (590 lb/yr/mi2).
Nitrate sources in surface water were assessed in both basins using dual-nitrate isotope (nitrogen and oxygen) ratios. Data from the different land uses in the SFLR Basin showed differences in nitrate concentrations and overlapping, but moderately distinct, isotopic signatures. Predominantly forested sites consistently had low nitrate concentrations (median = 0.233 milligrams per liter) with minimal variability, and agricultural sites had the highest nitrate concentrations (median = 7.55 milligrams per liter) with the greatest variability. The median nitrate concentration for sites with mixed land use was 2.66 milligrams per liter. Dual-isotope data for forested sites plotted within ranges characteristic of soil-derived nitrate with possible but minimal influence from recycled atmospheric nitrate. Ranges of dual-isotope data for sites with agricultural and mixed land uses were characteristic of possible mixtures of chemical fertilizer, soil-derived nitrate, and manure and septic wastes. In the NFLR Basin, a positive linear relation was observed between nitrate concentrations and nitrogen isotope ratios (δ15NNO3) (R2=0.56; p-value <0.001) that potentially suggests the NFLR Basin has a higher proportion of δ15NNO3-enriched sources, such as manure and sewage. However, mixing of other nitrate-derived sources cannot be excluded, because many values of δ15NNO3 and concentrations of nitrate showed minimal variation and plotted within dual-nitrate isotope ranges characteristic of fertilizer and soil-derived nitrate sources.
A sediment-fingerprinting approach was used to quantify the relative contribution of four upland sources in the SFLR Basin (agricultural, pasture, riparian/forest, and streambank) to understand how land management affects suspended-sediment concentration. Carbon isotope ratios (δ13C), together with calcium and carbon concentrations, were the best indicators of sediment source; the uncertainty was less than 11 percent. Fine-sediment samples collected at the SFLR Basin outlet indicated streambanks as the largest source of the fine sediment to the stream followed by cropland and riparian/forest-source areas, respectively; pasture was a minor contributing source. Streambanks and cropland were essentially equal contributors of fine sediment at the NFLR Basin outlet.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175086","collaboration":"Prepared in cooperation with the Little River Water-Quality Consortium ","usgsCitation":"Crain, A.S., Cherry, M.A., Williamson, T.N., and Bunch, A.R., 2017, Multiple-source tracking—Investigating sources of pathogens, nutrients, and sediment in the Upper Little River Basin, Kentucky, water years 2013–14: U.S. Geological Survey Scientific Investigations Report 2017–5086, 60 p., https://doi.org/10.3133/sir20175086.","productDescription":"Report: xi, 60 p; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070254","costCenters":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":345723,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZS2TPW","text":"USGS data release","description":"USGS data release"},{"id":345721,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5086/coverthb.jpg"},{"id":345722,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5086/sir20175086.pdf","text":"Report","size":"6.68 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Kentucky","otherGeospatial":"Upper Little River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.35023498535156,\n 36.96086580957587\n ],\n [\n -87.396240234375,\n 36.96799807635307\n ],\n [\n -87.44361877441406,\n 36.9800665440453\n ],\n [\n -87.46147155761719,\n 36.98390610968992\n ],\n [\n -87.48275756835938,\n 36.97842095659727\n ],\n [\n -87.48619079589842,\n 36.97128966642495\n ],\n [\n -87.48893737792969,\n 36.96306042436515\n ],\n [\n -87.50198364257811,\n 36.953732874654285\n ],\n [\n -87.51983642578125,\n 36.958671131530316\n ],\n [\n -87.5390625,\n 36.96031714599471\n ],\n [\n -87.54798889160156,\n 36.94989178681327\n ],\n [\n -87.53768920898438,\n 36.93507434788673\n ],\n [\n -87.52326965332031,\n 36.91421529335739\n ],\n [\n -87.50404357910156,\n 36.87797284779547\n ],\n [\n -87.53082275390625,\n 36.85874638670163\n ],\n [\n -87.56240844726562,\n 36.81862991458789\n ],\n [\n -87.58163452148438,\n 36.773542488140684\n ],\n [\n -87.68531799316406,\n 36.78839127856239\n ],\n [\n -87.76496887207031,\n 36.835118682834256\n ],\n [\n -87.80479431152344,\n 36.8549005138863\n ],\n [\n -87.7862548828125,\n 36.872480066931246\n ],\n [\n -87.78350830078125,\n 36.88126832670563\n ],\n [\n -87.80136108398438,\n 36.88511287236025\n ],\n [\n -87.84049987792969,\n 36.89005557519409\n ],\n [\n -87.86727905273438,\n 36.88126832670563\n ],\n [\n -87.89199829101561,\n 36.89444882017788\n ],\n [\n -87.91328430175781,\n 36.91256828285194\n ],\n [\n -87.93594360351562,\n 36.92135192790115\n ],\n [\n -87.96272277832031,\n 36.887309668681155\n ],\n [\n -87.97920227050781,\n 36.8614933202301\n ],\n [\n -87.96821594238281,\n 36.84061414925051\n ],\n [\n -87.95173645019531,\n 36.829622821570254\n ],\n [\n -87.87071228027344,\n 36.8021375933119\n ],\n [\n -87.83226013183594,\n 36.793340236044536\n ],\n [\n -87.80616760253905,\n 36.77409249464195\n ],\n [\n -87.79312133789062,\n 36.75153899262323\n ],\n [\n -87.7587890625,\n 36.686041276581925\n ],\n [\n -87.72239685058594,\n 36.679433365517774\n ],\n [\n -87.66952514648438,\n 36.677230602346214\n ],\n [\n -87.61940002441405,\n 36.6843893520368\n ],\n [\n -87.57545471191406,\n 36.70200805115167\n ],\n [\n -87.51846313476562,\n 36.71962271257377\n ],\n [\n -87.47383117675781,\n 36.72457610850295\n ],\n [\n -87.40310668945312,\n 36.73393165140215\n ],\n [\n -87.37564086914062,\n 36.72952918496946\n ],\n [\n -87.37770080566405,\n 36.74988847598359\n ],\n [\n -87.38388061523438,\n 36.76474184748192\n ],\n [\n -87.37632751464844,\n 36.78124222006408\n ],\n [\n -87.35710144042969,\n 36.804886560237236\n ],\n [\n -87.34542846679688,\n 36.8087349481474\n ],\n [\n -87.33100891113281,\n 36.82632511529416\n ],\n [\n -87.3358154296875,\n 36.83127162140714\n ],\n [\n -87.32414245605469,\n 36.84336173438382\n ],\n [\n -87.308349609375,\n 36.85380165753812\n ],\n [\n -87.29736328125,\n 36.86698689106877\n ],\n [\n -87.26715087890625,\n 36.88950640179281\n ],\n [\n -87.2589111328125,\n 36.910372213522535\n ],\n [\n -87.29598999023438,\n 36.9400138143685\n ],\n [\n -87.35023498535156,\n 36.96086580957587\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana-Kentucky Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299
Coastal wetlands are sites of rapid carbon (C) sequestration and contain large soil C stocks. Thus, there is increasing interest in those ecosystems as sites for anthropogenic greenhouse gas emission offset projects (sometimes referred to as “Blue Carbon”), through preservation of existing C stocks or creation of new wetlands to increase future sequestration. Here we show that in the globally-widespread occurrence of diked, impounded, drained and tidally-restricted salt marshes, substantial methane (CH4) and CO2 emission reductions can be achieved through restoration of disconnected saline tidal flows. Modeled climatic forcing indicates that tidal restoration to reduce emissions has a much greater impact per unit area than wetland creation or conservation to enhance sequestration. Given that GHG emissions in tidally-restricted, degraded wetlands are caused by human activity, they are anthropogenic emissions, and reducing them will have an effect on climate that is equivalent to reduced emission of an equal quantity of fossil fuel GHG. Thus, as a landuse-based climate change intervention, reducing CH4 emissions is an entirely distinct concept from biological C sequestration projects to enhance C storage in forest or wetland biomass or soil, and will not suffer from the non-permanence risk that stored C will be returned to the atmosphere.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-017-12138-4","usgsCitation":"Kroeger, K.D., Crooks, S., Moseman-Valtierra, S., and Tang, J., 2017, Restoring tides to reduce methane emissions in impounded wetlands: A new and potent Blue Carbon climate change intervention: Scientific Reports, v. 7, Article 11914; 12 p., https://doi.org/10.1038/s41598-017-12138-4.","productDescription":"Article 11914; 12 p.","ipdsId":"IP-070574","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469515,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-017-12138-4","text":"Publisher Index Page"},{"id":345979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-20","publicationStatus":"PW","scienceBaseUri":"59c37e35e4b091459a6316dc","contributors":{"authors":[{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":710923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crooks, Stephen","contributorId":77243,"corporation":false,"usgs":false,"family":"Crooks","given":"Stephen","affiliations":[{"id":34653,"text":"Silvestrum Climate Associates, LLC, Mill Valley, CA","active":true,"usgs":false}],"preferred":false,"id":710924,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moseman-Valtierra, Serena","contributorId":140087,"corporation":false,"usgs":false,"family":"Moseman-Valtierra","given":"Serena","email":"","affiliations":[{"id":6923,"text":"University of Rhode Island, Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":710925,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tang, Jianwu","contributorId":174890,"corporation":false,"usgs":false,"family":"Tang","given":"Jianwu","email":"","affiliations":[{"id":27818,"text":"The Ecosystems Center, Marine Biological Laboratory. Woods Hole, MA 02543.","active":true,"usgs":false}],"preferred":false,"id":710926,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191003,"text":"70191003 - 2017 - What mediates tree mortality during drought in the southern Sierra Nevada?","interactions":[],"lastModifiedDate":"2017-12-12T12:44:47","indexId":"70191003","displayToPublicDate":"2017-09-20T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"What mediates tree mortality during drought in the southern Sierra Nevada?","docAbstract":"Severe drought has the potential to cause selective mortality within a forest, thereby inducing shifts in forest species composition. The southern Sierra Nevada foothills and mountains of California have experienced extensive forest dieback due to drought stress and insect outbreak. We used high-fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) from the Carnegie Airborne Observatory (CAO) to estimate the effect of forest dieback on species composition in response to drought stress in Sequoia National Park. Our aims were: (1) to quantify site-specific conditions that mediate tree mortality along an elevation gradient in the southern Sierra Nevada Mountains; (2) to assess where mortality events have a greater probability of occurring; and (3) to estimate which tree species have a greater likelihood of mortality along the elevation gradient. A series of statistical models were generated to classify species composition and identify tree mortality, and the influences of different environmental factors were spatially quantified and analyzed to assess where mortality events have a greater likelihood of occurring. A higher probability of mortality was observed in the lower portion of the elevation gradient, on southwest and west-facing slopes, in areas with shallow soils, on shallower slopes, and at greater distances from water. All of these factors are related to site water balance throughout the landscape. Our results also suggest that mortality is species-specific along the elevation gradient, mainly affecting Pinus ponderosa and Pinus lambertiana at lower elevations. Selective mortality within the forest may drive long-term shifts in community composition along the elevation gradient.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1620","usgsCitation":"Paz-Kagan, T., Brodrick, P., Vaughn, N.R., Das, A.J., Stephenson, N.L., Nydick, K.R., and Asner, G.P., 2017, What mediates tree mortality during drought in the southern Sierra Nevada?: Ecological Applications, v. 27, no. 8, p. 2443-2457, https://doi.org/10.1002/eap.1620.","productDescription":"15 p.","startPage":"2443","endPage":"2457","ipdsId":"IP-085845","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":345927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Sierra Nevada","volume":"27","issue":"8","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-29","publicationStatus":"PW","scienceBaseUri":"59c37e36e4b091459a6316e6","contributors":{"authors":[{"text":"Paz-Kagan, Tarin","contributorId":196597,"corporation":false,"usgs":false,"family":"Paz-Kagan","given":"Tarin","email":"","affiliations":[],"preferred":false,"id":710888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brodrick, Philip","contributorId":196598,"corporation":false,"usgs":false,"family":"Brodrick","given":"Philip","affiliations":[],"preferred":false,"id":710889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vaughn, Nicholas R.","contributorId":196599,"corporation":false,"usgs":false,"family":"Vaughn","given":"Nicholas","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":710890,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Das, Adrian J. 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":196600,"corporation":false,"usgs":true,"family":"Das","given":"Adrian","email":"adas@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":710891,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stephenson, Nathan L. 0000-0003-0208-7229 nstephenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0208-7229","contributorId":2836,"corporation":false,"usgs":true,"family":"Stephenson","given":"Nathan","email":"nstephenson@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":710887,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nydick, Koren R.","contributorId":196601,"corporation":false,"usgs":false,"family":"Nydick","given":"Koren","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":710892,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Asner, Gregory P.","contributorId":25393,"corporation":false,"usgs":false,"family":"Asner","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":710893,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70191005,"text":"70191005 - 2017 - Food abundance, prey morphology, and diet specialization influence individual sea otter tool use","interactions":[],"lastModifiedDate":"2017-09-25T13:38:04","indexId":"70191005","displayToPublicDate":"2017-09-20T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":981,"text":"Behavioral Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Food abundance, prey morphology, and diet specialization influence individual sea otter tool use","docAbstract":"Sea otters are well-known tool users, employing objects such as rocks or shells to break open invertebrate prey. We used a series of generalized linear mixed effect models to examine observational data on prey capture and tool use from 211 tagged individuals from 5 geographically defined study areas throughout the sea otter’s range in California. Our best supported model was able to explain 75% of the variation in the frequency of tool use by individual sea otters with only ecological and demographic variables. In one study area, where sea otter food resources were abundant, all individuals had similar diets focusing on preferred prey items and used tools at low to moderate frequencies (4–38% of prey captures). In the remaining areas, where sea otters were food-limited, individuals specialized on different subsets of the available prey and had a wider range of average tool-use frequency (0–98% of prey captures). The prevalence of difficult-to-access prey in individual diets was a major predictor of tool use and increased the likelihood of using tools on prey that were not difficult to access as well. Age, sex, and feeding habitat also contributed to the probability of tool use but to a smaller extent. We developed a conceptual model illustrating how food abundance, the prevalence of difficult-to-access prey, and individual diet specialization interacted to determine the likelihood that individual sea otters would use tools and considered the model’s relevance to other tool-using species.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/beheco/arx011","usgsCitation":"Fujii, J.A., Ralls, K., and Tinker, M.T., 2017, Food abundance, prey morphology, and diet specialization influence individual sea otter tool use: Behavioral Ecology, v. 28, no. 5, p. 1206-1216, https://doi.org/10.1093/beheco/arx011.","productDescription":"11 p.","startPage":"1206","endPage":"1216","ipdsId":"IP-076085","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":469510,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/beheco/arx011","text":"Publisher Index Page"},{"id":345941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"5","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-21","publicationStatus":"PW","scienceBaseUri":"59c37e36e4b091459a6316e3","contributors":{"authors":[{"text":"Fujii, Jessica A. 0000-0003-4794-479X","orcid":"https://orcid.org/0000-0003-4794-479X","contributorId":196602,"corporation":false,"usgs":false,"family":"Fujii","given":"Jessica","email":"","middleInitial":"A.","affiliations":[],"preferred":true,"id":710896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ralls, Katherine","contributorId":37900,"corporation":false,"usgs":false,"family":"Ralls","given":"Katherine","email":"","affiliations":[{"id":7035,"text":"Smithsonian Conservation Biology Institute, National Zoological Park","active":true,"usgs":false}],"preferred":false,"id":710897,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tinker, M. Tim 0000-0002-3314-839X ttinker@usgs.gov","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":2796,"corporation":false,"usgs":true,"family":"Tinker","given":"M.","email":"ttinker@usgs.gov","middleInitial":"Tim","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":710895,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191012,"text":"70191012 - 2017 - Ancient lakes, Pleistocene climates and river avulsions structure the phylogeography of a large but little-known rock scorpion from the Mojave and Sonoran deserts","interactions":[],"lastModifiedDate":"2017-09-20T17:23:29","indexId":"70191012","displayToPublicDate":"2017-09-20T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1019,"text":"Biological Journal of the Linnean Society","active":true,"publicationSubtype":{"id":10}},"title":"Ancient lakes, Pleistocene climates and river avulsions structure the phylogeography of a large but little-known rock scorpion from the Mojave and Sonoran deserts","docAbstract":"Recent syntheses of phylogeographical data from terrestrial animals in the Mojave and Sonoran deserts have revealed a complex history of geologic and climatic vicariance events. We studied the phylogeography of Smeringurus vachoni to see how vicariance events may have impacted a large, endemic rock scorpion. Additionally, we used the phylogeographical data to examine the validity of two subspecies of S. vachoni that were described using unconventional morphological characters. Phylogenetic, network and SAMOVA analyses indicate that S. vachoni consists of 11 clades mostly endemic to isolated desert mountain ranges. Molecular clock estimates suggest that clades diversified between the Miocene and early Pleistocene. Species distribution models predict a contraction of suitable habitat during the last glacial maximum. Landscape interpolations and Migrate-n analyses highlight areas of gene flow across the Colorado River. Smeringurus vachoni does not comprise two subspecies. Instead, the species represents at least 11 mitochondrial clades that probably diversified by vicariance associated with Pleistocene climate changes and formation of ancient lakes along the Colorado River corridor. Gene flow appears to have occurred from west to east across the Colorado River during periodic river avulsions.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/biolinnean/blx058","usgsCitation":"Graham, M.R., Wood, D.A., Henault, J.A., Valois, Z.J., and Cushing, P.E., 2017, Ancient lakes, Pleistocene climates and river avulsions structure the phylogeography of a large but little-known rock scorpion from the Mojave and Sonoran deserts: Biological Journal of the Linnean Society, v. 122, no. 1, p. 133-146, https://doi.org/10.1093/biolinnean/blx058.","productDescription":"14 p.","startPage":"133","endPage":"146","ipdsId":"IP-083197","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":345978,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mojave Desert, Sonoran Desert","volume":"122","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-17","publicationStatus":"PW","scienceBaseUri":"59c37e36e4b091459a6316df","contributors":{"authors":[{"text":"Graham, Matthew R.","contributorId":196613,"corporation":false,"usgs":false,"family":"Graham","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":34649,"text":"Eastern Connectictut State University","active":true,"usgs":false}],"preferred":false,"id":710918,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":710919,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Henault, Jonathan A.","contributorId":196614,"corporation":false,"usgs":false,"family":"Henault","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[{"id":34649,"text":"Eastern Connectictut State University","active":true,"usgs":false}],"preferred":false,"id":710920,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Valois, Zachary J.","contributorId":196615,"corporation":false,"usgs":false,"family":"Valois","given":"Zachary","email":"","middleInitial":"J.","affiliations":[{"id":34651,"text":"Utah Museum of Natural History","active":true,"usgs":false}],"preferred":false,"id":710921,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cushing, Paula E.","contributorId":196616,"corporation":false,"usgs":false,"family":"Cushing","given":"Paula","email":"","middleInitial":"E.","affiliations":[{"id":27833,"text":"Denver Museum of Nature and Science","active":true,"usgs":false}],"preferred":false,"id":710922,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191899,"text":"70191899 - 2017 - The Great Acceleration and the disappearing surficial geologic record","interactions":[],"lastModifiedDate":"2017-11-10T14:11:56","indexId":"70191899","displayToPublicDate":"2017-09-20T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1728,"text":"GSA Today","active":true,"publicationSubtype":{"id":10}},"title":"The Great Acceleration and the disappearing surficial geologic record","docAbstract":"The surficial geologic record is the relatively thin veneer of young (<~1 Ma) and mostly unconsolidated sediments that cover portions of Earth’s terrestrial surface (Fig. 1). Once largely ignored as “overburden” by geologists, surficial deposits are now studied to address a wide range of issues related to the sustainability of human societies. Geologists use surficial deposits to determine the frequency and severity of past climatic changes, quantify natural and anthropogenic erosion rates, identify hazards, and calculate recurrence intervals associated with earthquakes, landslides, tsunamis, and volcanic eruptions. Increasingly, however, humans are eradicating the surficial geologic record in many key areas through progressive modification of Earth’s surface.
","language":"English","publisher":"The Geological Society of America","doi":"10.1130/GSATG341GW.1","usgsCitation":"Rech, J.A., Springer, K.B., and Pigati, J., 2017, The Great Acceleration and the disappearing surficial geologic record: GSA Today, v. 27, no. 11, p. 8-9, https://doi.org/10.1130/GSATG341GW.1.","productDescription":"2 p.","startPage":"8","endPage":"9","ipdsId":"IP-085757","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":346931,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-20","publicationStatus":"PW","scienceBaseUri":"59e86834e4b05fe04cd4d1e4","contributors":{"authors":[{"text":"Rech, Jason A.","contributorId":117323,"corporation":false,"usgs":false,"family":"Rech","given":"Jason","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":713587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Springer, Kathleen B. 0000-0002-2404-0264 kspringer@usgs.gov","orcid":"https://orcid.org/0000-0002-2404-0264","contributorId":149826,"corporation":false,"usgs":true,"family":"Springer","given":"Kathleen","email":"kspringer@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":713586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":149825,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey S.","email":"jpigati@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":false,"id":713588,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190970,"text":"70190970 - 2017 - Fatal attraction? Intraguild facilitation and suppression among predators","interactions":[],"lastModifiedDate":"2017-10-26T10:01:47","indexId":"70190970","displayToPublicDate":"2017-09-19T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5500,"text":"The American Naturalist","onlineIssn":"1537-5323","printIssn":" 0003-014","active":true,"publicationSubtype":{"id":10}},"title":"Fatal attraction? Intraguild facilitation and suppression among predators","docAbstract":"Competition and suppression are recognized as dominant forces that structure predator communities. Facilitation via carrion provisioning, however, is a ubiquitous interaction among predators that could offset the strength of suppression. Understanding the relative importance of these positive and negative interactions is necessary to anticipate community-wide responses to apex predator declines and recoveries worldwide. Using state-sponsored wolf (Canis lupus) control in Alaska as a quasi experiment, we conducted snow track surveys of apex, meso-, and small predators to test for evidence of carnivore cascades (e.g., mesopredator release). We analyzed survey data using an integrative occupancy and structural equation modeling framework to quantify the strengths of hypothesized interaction pathways, and we evaluated fine-scale spatiotemporal responses of nonapex predators to wolf activity clusters identified from radio-collar data. Contrary to the carnivore cascade hypothesis, both meso- and small predator occupancy patterns indicated guild-wide, negative responses of nonapex predators to wolf abundance variations at the landscape scale. At the local scale, however, we observed a near guild-wide, positive response of nonapex predators to localized wolf activity. Local-scale association with apex predators due to scavenging could lead to landscape patterns of mesopredator suppression, suggesting a key link between occupancy patterns and the structure of predator communities at different spatial scales.
","language":"English","publisher":"The University of Chicago Press","doi":"10.1086/693996","usgsCitation":"Sivy, K.J., Pozzanghera, C.B., Grace, J.B., and Prugh, L.R., 2017, Fatal attraction? Intraguild facilitation and suppression among predators: The American Naturalist, v. 190, no. 5, p. 663-679, https://doi.org/10.1086/693996.","productDescription":"17 p.","startPage":"663","endPage":"679","ipdsId":"IP-074486","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":345900,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"190","issue":"5","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59c22cb2e4b091459a61b729","contributors":{"authors":[{"text":"Sivy, Kelly J. 0000-0002-3598-3014","orcid":"https://orcid.org/0000-0002-3598-3014","contributorId":196570,"corporation":false,"usgs":false,"family":"Sivy","given":"Kelly","email":"","middleInitial":"J.","affiliations":[{"id":7211,"text":"University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":710789,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pozzanghera, Casey B.","contributorId":196571,"corporation":false,"usgs":false,"family":"Pozzanghera","given":"Casey","email":"","middleInitial":"B.","affiliations":[{"id":34632,"text":"Boise State University, Idaho","active":true,"usgs":false}],"preferred":false,"id":710790,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":710788,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prugh, Laura R. 0000-0001-9045-3107","orcid":"https://orcid.org/0000-0001-9045-3107","contributorId":196572,"corporation":false,"usgs":false,"family":"Prugh","given":"Laura","email":"","middleInitial":"R.","affiliations":[{"id":13194,"text":"School of Environmental and Forest Sciences, University of Washington","active":true,"usgs":false}],"preferred":false,"id":710791,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190963,"text":"70190963 - 2017 - Geospatial tools effectively estimate nonexceedance probabilities of daily streamflow at ungauged and intermittently gauged locations in Ohio","interactions":[],"lastModifiedDate":"2017-09-19T11:19:32","indexId":"70190963","displayToPublicDate":"2017-09-19T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Geospatial tools effectively estimate nonexceedance probabilities of daily streamflow at ungauged and intermittently gauged locations in Ohio","docAbstract":"Study region
The state of Ohio in the United States, a humid, continental climate.
Study focus
The estimation of nonexceedance probabilities of daily streamflows as an alternative means of establishing the relative magnitudes of streamflows associated with hydrologic and water-quality observations.
New hydrological insights for the region
Several methods for estimating nonexceedance probabilities of daily mean streamflows are explored, including single-index methodologies (nearest-neighboring index) and geospatial tools (kriging and topological kriging). These methods were evaluated by conducting leave-one-out cross-validations based on analyses of nearly 7 years of daily streamflow data from 79 unregulated streamgages in Ohio and neighboring states. The pooled, ordinary kriging model, with a median Nash–Sutcliffe performance of 0.87, was superior to the single-site index methods, though there was some bias in the tails of the probability distribution. Incorporating network structure through topological kriging did not improve performance. The pooled, ordinary kriging model was applied to 118 locations without systematic streamgaging across Ohio where instantaneous streamflow measurements had been made concurrent with water-quality sampling on at least 3 separate days. Spearman rank correlations between estimated nonexceedance probabilities and measured streamflows were high, with a median value of 0.76. In consideration of application, the degree of regulation in a set of sample sites helped to specify the streamgages required to implement kriging approaches successfully.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2017.08.006","usgsCitation":"Farmer, W.H., and Koltun, G.F., 2017, Geospatial tools effectively estimate nonexceedance probabilities of daily streamflow at ungauged and intermittently gauged locations in Ohio: Journal of Hydrology: Regional Studies, v. 13, p. 208-221, https://doi.org/10.1016/j.ejrh.2017.08.006.","productDescription":"14 p.","startPage":"208","endPage":"221","ipdsId":"IP-081013","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":461399,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2017.08.006","text":"Publisher Index Page"},{"id":438211,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TM788T","text":"USGS data release","linkHelpText":"Geospatial Tools Effectively Estimate Nonexceedance Probabilities of Daily Streamflow at Ungaged and 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\"}}]}","volume":"13","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59c22cb3e4b091459a61b730","contributors":{"authors":[{"text":"Farmer, William H. 0000-0002-2865-2196 wfarmer@usgs.gov","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":4374,"corporation":false,"usgs":true,"family":"Farmer","given":"William","email":"wfarmer@usgs.gov","middleInitial":"H.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":710766,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koltun, G. F. 0000-0003-0255-2960 gfkoltun@usgs.gov","orcid":"https://orcid.org/0000-0003-0255-2960","contributorId":140048,"corporation":false,"usgs":true,"family":"Koltun","given":"G.","email":"gfkoltun@usgs.gov","middleInitial":"F.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":710767,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70190788,"text":"ofr20171116 - 2017 - Morphologic evolution of the wilderness area breach at Fire Island, New York—2012–15","interactions":[],"lastModifiedDate":"2024-12-27T15:18:20.876985","indexId":"ofr20171116","displayToPublicDate":"2017-09-18T11:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1116","title":"Morphologic evolution of the wilderness area breach at Fire Island, New York—2012–15","docAbstract":"Hurricane Sandy, which made landfall on October 29, 2012, near Atlantic City, New Jersey, had a significant impact on the coastal system along the south shore of Long Island, New York. A record significant wave height of 9.6 meters (m) was measured at wave buoy 44025, approximately 48 kilometers offshore of Fire Island, New York. Surge and runup during the storm resulted in extensive beach and dune erosion and breaching of the Fire Island barrier island system at two locations, including a breach that formed within the Otis Pike Fire Island High Dune Wilderness area on the eastern side of Fire Island.
The U.S. Geological Survey (USGS) has a long history of conducting morphologic change and processes research at Fire Island. One of the primary objectives of the current research effort is to understand the morphologic evolution of the barrier system on a variety of time scales (from storm scale to decade(s) to century). A number of studies that support the project objectives have been published. Prior to Hurricane Sandy, however, little information was available on specific storm-driven change in this region. The USGS received Hurricane Sandy supplemental funding (project GS2–2B: Linking Coastal Processes and Vulnerability, Fire Island, New York, Regional Study) to enhance existing research efforts at Fire Island. The existing research was greatly expanded to include inner continental shelf mapping and investigations of processes of inner shelf sediment transport; beach and dune response and recovery; and observation, analysis, and modeling of the newly formed breach in the Otis Pike High Dune Wilderness area, herein referred to as the wilderness breach. The breach formed at the site of Old Inlet, which was open from 1763 to 1825. The location of the initial island breaching does not directly correspond with topographic lows of the dunes, but instead the breach formed in the location of a cross-island boardwalk that was destroyed during Hurricane Sandy.
From 2013 to November 2015, bathymetric data were collected by the USGS St. Petersburg Coastal and Marine Science Center during three surveys of the breach channel and tidal shoals, and shoreline positions on each side of the breach (also collected by the National Park Service). Additionally, pre-storm topography/bathymetry EAARL–B light detection and ranging (lidar) data were collected by the USGS the day prior to Hurricane Sandy’s landfall. These data serve as a baseline for change analyses during four subsequent periods: June 2013, June 2014, October 2014, and May 2015. The June 2013 single-beam bathymetry data were collected in collaboration with the U.S. Army Corps of Engineers (USACE), using the Lighter Amphibious Resupply Cargo (LARC) vessel, and included the ebb shoal and breach channel. The USGS collected and processed the three additional bathymetric datasets using personal watercraft equipped with single-beam echo sounders and backpack Global Positioning System (GPS) over shallow flood shoals.
Eastern and western breach shorelines were surveyed weekly to monthly beginning on November 6, 2012 (by the National Park Service [NPS], and USGS St. Petersburg Coastal and Marine Science Center), with measurements made every few weeks for the first year and every few months after October 2013. The NPS and researchers from Stony Brook University monitored the breach by collecting field data of the breach channel bathymetry, conducting aerial photographic overflights, and performing water-quality analyses (see http://po.msrc.sunysb.edu/GSB/). The aerial photography collected and rectified by Stony Brook University is used extensively in our morphologic change description to examine changes to breach shorelines (supplementing shoreline data collected in the field), channel width, and orientation. Due to the uncertainties and the variation in survey methods, a rigorous quantitative analysis was not performed. However, average calculations of various breach metrics allow a qualitative analysis of breach development and evolution.
This report presents an overview of the data collected and a summary discussion of the observed changes to the breach system and the seasonal wave climatology associated with the breach morphodynamic response.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171116","usgsCitation":"Hapke, C.J., Nelson, T.R., Henderson, R.E., Brenner, O.T., and Miselis, J.L., 2017, Morphologic evolution of the wilderness area breach at Fire Island, New York—2012–15: U.S. Geological Survey Open-File Report 2017–1116, 17 p., https://doi.org/10.3133/ofr20171116.","productDescription":"Report: vi, 17 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-086286","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":345809,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds1049","text":"Data Series 1049","description":"Data Series 1049","linkHelpText":"- Coastal bathymetry data collected in May 2015 from Fire Island, New York—Wilderness breach and shoreface"},{"id":345808,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds1034","text":"Data Series 1034","description":"Data Series 1034","linkHelpText":"Bathymetry data collected in October 2014 from Fire Island, New York—The wilderness breach, shoreface, and bay"},{"id":345810,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds1007","text":"Data Series 1007","description":"Data Series 1007","linkHelpText":"- Coastal bathymetry data collected in June 2014 from Fire Island, New York—The wilderness breach and shoreface"},{"id":345750,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1116/coverthb.jpg"},{"id":345805,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1116/ofr20171116.pdf","text":"Report","size":"21.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1116"},{"id":345806,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds914","text":"Data Series 914","description":"Data Series 914","linkHelpText":"- Bathymetry of Wilderness Breach at Fire Island, New York from June 2013"},{"id":345807,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7G15Z17","text":"USGS data release","description":"USGS data release","linkHelpText":"Hurricane Sandy Beach Response and Recovery at Fire Island, New York—Shoreline and Beach Profile Data, October 2012 to June 2016"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.32000732421875,\n 40.6113461833302\n ],\n [\n -72.87574768066406,\n 40.6113461833302\n ],\n [\n -72.87574768066406,\n 40.73581157695217\n ],\n [\n -73.32000732421875,\n 40.73581157695217\n ],\n [\n -73.32000732421875,\n 40.6113461833302\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
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600 4th Street South
St. Petersburg, FL 33701
Gross primary production (GPP) is the Earth’s largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0184466","usgsCitation":"Kimball, H.L., Selmants, P., Moreno, A., W, R.S., and Giardina, C.P., 2017, Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems: PLoS ONE, v. 12, no. 9, e0184466; 14 p., https://doi.org/10.1371/journal.pone.0184466.","productDescription":"e0184466; 14 p.","ipdsId":"IP-090329","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":461401,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0184466","text":"Publisher Index Page"},{"id":345862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"12","issue":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-08","publicationStatus":"PW","scienceBaseUri":"59c0db1ce4b091459a5f472a","contributors":{"authors":[{"text":"Kimball, Heather L.","contributorId":196550,"corporation":false,"usgs":false,"family":"Kimball","given":"Heather","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":710710,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Selmants, Paul 0000-0001-6211-3957 pselmants@usgs.gov","orcid":"https://orcid.org/0000-0001-6211-3957","contributorId":192591,"corporation":false,"usgs":true,"family":"Selmants","given":"Paul","email":"pselmants@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":710709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moreno, Alvaro","contributorId":196551,"corporation":false,"usgs":false,"family":"Moreno","given":"Alvaro","email":"","affiliations":[],"preferred":false,"id":710711,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"W, Running Steve","contributorId":196552,"corporation":false,"usgs":false,"family":"W","given":"Running","email":"","middleInitial":"Steve","affiliations":[],"preferred":false,"id":710712,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Giardina, Christian P. 0000-0002-3431-5073","orcid":"https://orcid.org/0000-0002-3431-5073","contributorId":182695,"corporation":false,"usgs":false,"family":"Giardina","given":"Christian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":710713,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70190856,"text":"70190856 - 2017 - Sediment unmixing using detrital geochronology","interactions":[],"lastModifiedDate":"2017-09-17T11:23:16","indexId":"70190856","displayToPublicDate":"2017-09-17T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Sediment unmixing using detrital geochronology","docAbstract":"Sediment mixing within sediment routing systems can exert a strong influence on the preservation of provenance signals that yield insight into the influence of environmental forcings (e.g., tectonism, climate) on the earth’s surface. Here we discuss two approaches to unmixing detrital geochronologic data in an effort to characterize complex changes in the sedimentary record. First we summarize ‘top-down’ mixing, which has been successfully employed in the past to characterize the different fractions of prescribed source distributions (‘parents’) that characterize a derived sample or set of samples (‘daughters’). Second we propose the use of ‘bottom-up’ methods, previously used primarily for grain size distributions, to model parent distributions and the abundances of these parents within a set of daughters. We demonstrate the utility of both top-down and bottom-up approaches to unmixing detrital geochronologic data within a well-constrained sediment routing system in central California. Use of a variety of goodness-of-fit metrics in top-down modeling reveals the importance of considering the range of allowable mixtures over any single best-fit mixture calculation. Bottom-up modeling of 12 daughter samples from beaches and submarine canyons yields modeled parent distributions that are remarkably similar to those expected from the geologic context of the sediment-routing system. In general, mixture modeling has potential to supplement more widely applied approaches in comparing detrital geochronologic data by casting differences between samples as differing proportions of geologically meaningful end-member provenance categories.","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2017.07.044","usgsCitation":"Sharman, G.R., and Johnstone, S., 2017, Sediment unmixing using detrital geochronology: Earth and Planetary Science Letters, v. 477, p. 183-194, https://doi.org/10.1016/j.epsl.2017.07.044.","productDescription":"8 p.","startPage":"183","endPage":"194","ipdsId":"IP-086349","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":345831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"477","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59bf8993e4b091459a5e0873","contributors":{"authors":[{"text":"Sharman, Glenn R.","contributorId":196537,"corporation":false,"usgs":false,"family":"Sharman","given":"Glenn","email":"","middleInitial":"R.","affiliations":[{"id":34621,"text":"Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA","active":true,"usgs":false}],"preferred":false,"id":710647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnstone, Samuel 0000-0002-3945-2499 sjohnstone@usgs.gov","orcid":"https://orcid.org/0000-0002-3945-2499","contributorId":196536,"corporation":false,"usgs":true,"family":"Johnstone","given":"Samuel","email":"sjohnstone@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":710646,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70179092,"text":"cir1425 - 2017 - Investigating the landscape of Arroyo Seco—Decoding the past—A teaching guide to climate-controlled landscape evolution in a tectonically active region","interactions":[],"lastModifiedDate":"2017-09-18T10:35:51","indexId":"cir1425","displayToPublicDate":"2017-09-15T17:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1425","title":"Investigating the landscape of Arroyo Seco—Decoding the past—A teaching guide to climate-controlled landscape evolution in a tectonically active region","docAbstract":"Arroyo Seco is a river that flows eastward out of the Santa Lucia Range in Monterey County, California. The Santa Lucia Range is considered part of the central California Coast Range. Arroyo Seco flows out of the Santa Lucia Range into the Salinas River valley, near the town of Greenfield, where it joins the Salinas River. The Salinas River flows north into Monterey Bay about 40 miles from where it merges with Arroyo Seco. In the mountain range, Arroyo Seco has cut or eroded a broad and deep valley. This valley preserves a geologic story in the landscape that is influenced by both fault-controlled mountain building (tectonics) and sea level fluctuations (regional climate).
Broad flat surfaces called river terraces, once eroded by Arroyo Seco, can be observed along the modern drainage. In the valley, terraces are also preserved like climbing stairs up to 1,800 feet above Arroyo Seco today. These terraces mark where Arroyo Seco once flowed.The terraces were formed by the river because no matter how high they are, the terraces are covered by gravel deposits exactly like those that can be observed in the river today. The Santa Lucia Range, Arroyo Seco, and the Salinas River valley must have looked very different when the highest and oldest terraces were forming. The Santa Lucia Range may have been lower, the Arroyo Seco may have been steeper and wider, and the Salinas River valley may have been much smaller.
Arroyo Seco, like all rivers, is always changing. Some-times rivers flow very straight, and sometimes they are curvy. Sometimes rivers are cutting down or eroding the landscape, and sometimes they are not eroding but depositing material. Sometimes rivers are neither eroding nor transporting material. The influences that change the behavior of Arroyo Seco are mountain uplift caused by fault moment and sea level changes driven by regional climate change. When a stream is affected by one or both of these influences, the stream accommodates the change by eroding, depositing, and (or) changing its shape.
In the vicinity of Arroyo Seco, the geologically young faulting history is relatively well understood. Geologists have some sense of the most recent faulting event and of the faulting in the recent geologic past. The timing of regional climate changes is also well accepted. In this area, warm climate cycles tend to cause the sea level to rise, and cool climate cycles tend to cause the sea level to fall. If we understand the way the terraces form and their ages in Arroyo Seco, we can draw conclusions about whether faulting and (or) climate contributed to their formation.
This publication serves as a descriptive companion to the formal geologic map of Arroyo Seco (Taylor and Sweetkind, 2014) and is intended for use by nonscientists and students. Included is a discussion of the processes that controlled the evolution of the drainage and the formation of the terraces in Arroyo Seco. The reader is guided to well-exposed landscape features in an easily accessible environment that will help nonscientists gain an understanding of how features on a geologic map are interpreted in terms of earth processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1425","usgsCitation":"Taylor, E.M., Sweetkind, D.S., and Havens, J.C., 2017, Investigating the landscape of Arroyo Seco—Decoding the past—A teaching guide to climate-controlled landscape evolution in a tectonically active region: U.S. Geological Survey Circular 1425, 44 p., https://doi.org/10.3133/c1425.","productDescription":"v, 45 p","numberOfPages":"56","onlineOnly":"N","ipdsId":"IP-074496","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":341359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1425/coverthb2.jpg"},{"id":341360,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1425/c1425.pdf","text":"Report","size":"27.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1425"},{"id":345815,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/circ/1425/versionHist.txt","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"Circular 1425 Version History"}],"country":"United States","state":"California","county":"Monterey County","otherGeospatial":"Arroyo Seco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.0855712890625,\n 36.89719446989036\n ],\n [\n -121.95373535156249,\n 36.91915611148194\n ],\n [\n -121.8988037109375,\n 36.89280138293983\n ],\n [\n -121.88232421875,\n 36.848856608486905\n ],\n [\n -121.84936523437499,\n 36.74768773190056\n ],\n [\n -121.86584472656251,\n 36.686041276581925\n ],\n [\n -121.8878173828125,\n 36.641977814705946\n ],\n [\n -121.9482421875,\n 36.66841891894786\n ],\n [\n -122.01416015625,\n 36.61552763134925\n ],\n [\n -122.00317382812499,\n 36.5670120564234\n ],\n [\n -121.9482421875,\n 36.26199220445664\n ],\n [\n -121.5911865234375,\n 36.00911716117325\n ],\n [\n -121.3385009765625,\n 35.652832827451654\n ],\n [\n -121.19567871093751,\n 35.60818490437746\n ],\n [\n -121.0308837890625,\n 35.40696093270201\n ],\n [\n -120.94299316406249,\n 35.42486791930558\n ],\n [\n -120.61889648437501,\n 35.303918565311704\n ],\n [\n -118.28979492187499,\n 35.25459097465022\n ],\n [\n -119.13574218749999,\n 36.619936625629215\n ],\n [\n -119.5037841796875,\n 36.91915611148194\n ],\n [\n -119.7015380859375,\n 37.155938651244625\n ],\n [\n -119.84985351562499,\n 37.24782120155428\n ],\n [\n -119.981689453125,\n 37.33522435930639\n ],\n [\n -121.06933593749999,\n 37.208456662000195\n ],\n [\n -121.57470703125,\n 37.077093191754436\n ],\n [\n -122.0855712890625,\n 36.89719446989036\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted May 19, 2017; Version 1.1: September 15, 2017","contact":"Geosciences and Environmental Change Science Center
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Several streams used for recreational activities, such as fishing, swimming, and boating, in Chester County, Pennsylvania, are known to have periodic elevated concentrations of fecal coliform bacteria, a type of bacteria used to indicate the potential presence of fecally related pathogens that may pose health risks to humans exposed through water contact. The availability of near real-time continuous stream discharge, turbidity, and other water-quality data for some streams in the county presents an opportunity to use surrogates to estimate near real-time concentrations of fecal coliform (FC) bacteria and thus provide some information about associated potential health risks during recreational use of streams.
The U.S. Geological Survey (USGS), in cooperation with the Chester County Health Department (CCHD) and the Chester County Water Resources Authority (CCWRA), has collected discrete stream samples for analysis of FC concentrations during March–October annually at or near five gaging stations where near real-time continuous data on stream discharge, turbidity, and water temperature have been collected since 2007 (or since 2012 at 2 of the 5 stations). In 2014, the USGS, in cooperation with the CCWRA and CCHD, began to develop regression equations to estimate FC concentrations using available near real-time continuous data. Regression equations included possible explanatory variables of stream discharge, turbidity, water temperature, and seasonal factors calculated using Julian Day with base-10 logarithmic (log) transformations of selected variables.
The regression equations were developed using the data from 2007 to 2015 (101–106 discrete bacteria samples per site) for three gaging stations on Brandywine Creek (West Branch Brandywine Creek at Modena, East Branch Brandywine Creek below Downingtown, and Brandywine Creek at Chadds Ford) and from 2012 to 2015 (37–38 discrete bacteria samples per site) for one station each on French Creek near Phoenixville and White Clay Creek near Strickersville. Fecal coliform bacteria data collected by USGS in 2016 (about nine samples per site) were used to validate the equations. The best-fit regression equations included log turbidity and seasonality factors computed using Julian Day as explanatory variables to estimate log FC concentrations at all five stream sites. The adjusted coefficient of determination for the equations ranged from 0.61 to 0.76, with the strength of the regression equations likely affected in part by the limited amount and variability of FC bacteria data. During summer months, the estimated and measured FC concentrations commonly were greater than the Pennsylvania Department of Environmental Protection established standards of 200 and 400 colonies per 100 milliliters for water contact from May through September at the 5 stream sites, with concentrations typically higher at 2 sites (White Clay Creek and West Branch Brandywine Creek at Modena) than at the other 3 sites. The estimated concentrations of FC bacteria during the summer months commonly were higher than measured concentrations and therefore could be considered cautious estimates of potential human-health risk. Additional water-quality data are needed to maintain and (or) improve the ability of regression equations to estimate FC concentrations by use of surrogate data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175075","collaboration":"Prepared in cooperation with the Chester County Health Department and Chester County Water Resources Authority","usgsCitation":"Senior, L.A., 2017, Estimated fecal coliform bacteria concentrations using near real-time continuous water-quality and streamflow data from five stream sites in Chester County, Pennsylvania, 2007–16 (ver. 1.2, March 2024): U.S. Geological Survey Scientific Investigations Report 2017–5075, 46 p., https://doi.org/10.3133/sir20175075.","productDescription":"Report: x, 46 p.; Appendix 1-5; Data Release","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-084822","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":499300,"rank":10,"type":{"id":36,"text":"NGMDB Index 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Strickersville, Pennsylvania"},{"id":345655,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5075/sir20175075_appendix4.pdf","text":"Appendix 4","size":"348 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Model Archive Summary for Best-Fit Regression Developed to Estimate Fecal Coliform Concentration at Station 01472157; French Creek near Phoenixville, Pennsylvania"},{"id":345654,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5075/sir20175075_appendix3.pdf","text":"Appendix 3","size":"429 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Model Archive Summary for Best-Fit Regression Developed to Estimate Fecal Coliform Concentration at Station 01481000; Brandywine Creek at Chadds Ford, Pennsylvania"},{"id":345653,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5075/sir20175075_appendix2.pdf","text":"Appendix 2","size":"434 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Model Archive Summary for Best-Fit Regression Developed to Estimate Fecal Coliform Concentration at Station 01480870; East Branch Brandywine Creek below Downingtown, Pennsylvania"},{"id":345650,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5075/coverthb4.jpg"},{"id":345652,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5075/sir20175075_appendix1.pdf","text":"Appendix 1","size":"505 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Model Archive Summary for Best-Fit Regression Developed to Estimate Fecal Coliform Concentration at Station 01480617; West Branch Brandywine Creek at Modena, Pennsylvania"},{"id":345651,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5075/sir20175075.pdf","text":"Report","size":"12.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5075"}],"country":"United 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215 Limekiln Road
New Cumberland, PA 17070-2424
A module for simulation of daily mean water temperature in a network of stream segments has been developed as an enhancement to the U.S. Geological Survey Precipitation Runoff Modeling System (PRMS). This new module is based on the U.S. Fish and Wildlife Service Stream Network Temperature model, a mechanistic, one-dimensional heat transport model. The new module is integrated in PRMS. Stream-water temperature simulation is activated by selection of the appropriate input flags in the PRMS Control File and by providing the necessary additional inputs in standard PRMS input files.This report includes a comprehensive discussion of the methods relevant to the stream temperature calculations and detailed instructions for model input preparation.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section D: Groundwater/surface-water interactions in Book 6: Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6D4","usgsCitation":"Sanders, M.J., Markstrom, S.L., Regan, R.S., and Atkinson, R.D., 2017, Documentation of a daily mean stream temperature module—An enhancement to the Precipitation-Runoff Modeling System: U.S. Geological Survey Techniques and Methods, book 6, chap. D4, 18 p., https://doi.org/10.3133/tm6D4.","productDescription":"v, 18 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-081476","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":345706,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/d04/coverthb.jpg"},{"id":345707,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/d04/tm6d4.pdf","text":"Report","size":"796 kB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6-D4"}],"publicComments":"This report in Chapter 4 of Section D: Groundwater/surface-water interactions in Book 6: Modeling techniques.","contact":"Chief, Branch of Regional Research, Central Region
U.S. Geological Survey
Box 25046, MS–418
Denver, CO 80225–0046
The Colorado Plateau hosts several large accumulations of naturally occurring, non-hydrocarbon gases, including CO2, N2, and the noble gases, making it a good field location to study the fluxes of these gases within the crust and to the atmosphere. In this study, we present a compilation of 1252 published gas-composition measurements. The data reveal at least three natural gas associations in the field area, which are dominated by hydrocarbons, CO2, and N2 + He + Ar, respectively. Most gas accumulations of the region exhibit compositions that are intermediate between the three end members. The first non-hydrocarbon gas association is characterized by very high-purity CO2, in excess of 75 mol% (hereafter, %). Many of these high-purity CO2 fields have recently been well described and interpreted as magmatic in origin. The second non-hydrocarbon gas association is less well described on the Colorado Plateau. It exhibits He concentrations on the order of 1–10%, and centered log ratio biplots show that He occurs proportionally to both N2 and Ar. Overall ratios of N2 to He to Ar are ≈100:10:1 and correlation in concentrations of these gases suggests that they have been sourced from the same reservoir and/or by a common process. To complement the analysis of the gas-composition data, stable isotope and noble-gas isotope measurements are compiled or newly reported from 11 representative fields (previously published data from 4 fields and new data from 7 fields). Gas sampled from the Harley Dome gas field in Utah contains nearly pure N2 + He + Ar. The various compositional and stable and noble gas isotopic data for this gas indicate that noble gas molecule/isotope ratios are near crustal radiogenic production values and also suggest a crustal N2 source. Across the field area, most of the high-purity N2 + He + Ar gas accumulations are associated with the mapped surface trace of structures or sutures in the Precambrian basement and are often accumulated in lower parts of the overlying Phanerozoic sedimentary cover. The high-purity gas association mostly occurs in areas interior to the plateau that are characterized by a narrow range of elevated, moderate heat flow values (53–74 mW/m2) in the ancient (1.8–1.6 Ga) basement terranes of the region. Collectively, the geochemical and geological data suggest that (1) the N2 + He + Ar gas association is sourced from a crustal reservoir, (2) the gas association migrates preferentially along structures in the Precambrian basement, and (3) the sourcing process relates to heating of the crust. Prospecting for noble-gas accumulations may target areas with elevated Cenozoic heat flow, ancient crust, and deep crustal structures that focus gas migration. High-purity CO2 gas may also migrate through regional basement structures, however, there is not always a clear spatial association. Rather, CO2 accumulations are more clearly associated with zones of high heat flow (>63 mW/m2) that sit above hot upper mantle and are proximal to Cenozoic volcanic rocksnear the plateau margins. These observations are consistent with previous interpretations of a magmatic gas source, which were based on geochemical measurements.
Dam removals with unmanaged sediment releases are good opportunities to learn about channel response to abruptly increased bed material supply. Understanding these events is important because they affect aquatic habitats and human uses of floodplains. A longstanding paradigm in geomorphology holds that response rates to landscape disturbance exponentially decay through time. However, a previous study of the Merrimack Village Dam (MVD) removal on the Souhegan River in New Hampshire, USA, showed that an exponential function poorly described the early geomorphic response. Erosion of impounded sediments there was two-phased. We had an opportunity to quantitatively test the two-phase response model proposed for MVD by extending the record there and comparing it with data from the Simkins Dam removal on the Patapsco River in Maryland, USA. The watershed sizes are the same order of magnitude (102 km2), and at both sites low-head dams were removed (~3–4 m) and ~65 000 m3 of sand-sized sediments were discharged to low-gradient reaches. Analyzing four years of repeat morphometry and sediment surveys at the Simkins site, as well as continuous discharge and turbidity data, we observed the two-phase erosion response described for MVD. In the early phase, approximately 50% of the impounded sediment at Simkins was eroded rapidly during modest flows. After incision to base level and widening, a second phase began when further erosion depended on floods large enough to go over bank and access impounded sediments more distant from the newly-formed channel. Fitting functional forms to the data for both sites, we found that two-phase exponential models with changing decay constants fit the erosion data better than single-phase models. Valley width influences the two-phase erosion responses upstream, but downstream responses appear more closely related to local gradient, sediment re-supply from the upstream impoundments, and base flows.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.4108","usgsCitation":"Collins, M.J., Snyder, N.P., Boardman, G., Banks, W.S., Andrews, M., Baker, M.E., Conlon, M., Gellis, A.C., McClain, S., Miller, A., and Wilcock, P., 2017, Channel response to sediment release: insights from a paired analysis of dam removal: Earth Surface Processes and Landforms, v. 42, no. 11, p. 1636-1651, https://doi.org/10.1002/esp.4108.","productDescription":"16 p.","startPage":"1636","endPage":"1651","ipdsId":"IP-072066","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":347228,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Simkins Dam ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.79872512817383,\n 39.264689574787724\n ],\n [\n -76.70053482055663,\n 39.20671884491848\n ],\n [\n -76.69126510620117,\n 39.215630305545304\n ],\n [\n -76.78979873657227,\n 39.27213188522936\n ],\n [\n -76.79872512817383,\n 39.264689574787724\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"11","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-14","publicationStatus":"PW","scienceBaseUri":"59f05121e4b0220bbd9a1d83","contributors":{"authors":[{"text":"Collins, Mathias J.","contributorId":181551,"corporation":false,"usgs":false,"family":"Collins","given":"Mathias","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":714847,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Snyder, Noah P.","contributorId":198029,"corporation":false,"usgs":false,"family":"Snyder","given":"Noah","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":714848,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boardman, Graham","contributorId":198030,"corporation":false,"usgs":false,"family":"Boardman","given":"Graham","email":"","affiliations":[],"preferred":false,"id":714849,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Banks, William S. 0000-0002-2090-8708 wsbanks@usgs.gov","orcid":"https://orcid.org/0000-0002-2090-8708","contributorId":2349,"corporation":false,"usgs":true,"family":"Banks","given":"William","email":"wsbanks@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714850,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andrews, Mary","contributorId":198031,"corporation":false,"usgs":false,"family":"Andrews","given":"Mary","email":"","affiliations":[],"preferred":false,"id":714851,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baker, Matthew E.","contributorId":42889,"corporation":false,"usgs":true,"family":"Baker","given":"Matthew","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":715151,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Conlon, Maricate","contributorId":198032,"corporation":false,"usgs":false,"family":"Conlon","given":"Maricate","email":"","affiliations":[],"preferred":false,"id":714852,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714846,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McClain, Serena","contributorId":198033,"corporation":false,"usgs":false,"family":"McClain","given":"Serena","email":"","affiliations":[],"preferred":false,"id":714853,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Miller, Andrew","contributorId":196361,"corporation":false,"usgs":false,"family":"Miller","given":"Andrew","affiliations":[],"preferred":false,"id":714854,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wilcock, Peter","contributorId":198034,"corporation":false,"usgs":false,"family":"Wilcock","given":"Peter","affiliations":[],"preferred":false,"id":714855,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70190802,"text":"70190802 - 2017 - Assessing coastal wetland vulnerability to sea-level rise along the northern Gulf of Mexico coast: Gaps and opportunities for developing a coordinated regional sampling network","interactions":[],"lastModifiedDate":"2017-09-14T15:55:52","indexId":"70190802","displayToPublicDate":"2017-09-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Assessing coastal wetland vulnerability to sea-level rise along the northern Gulf of Mexico coast: Gaps and opportunities for developing a coordinated regional sampling network","docAbstract":"Coastal wetland responses to sea-level rise are greatly influenced by biogeomorphic processes that affect wetland surface elevation. Small changes in elevation relative to sea level can lead to comparatively large changes in ecosystem structure, function, and stability. The surface elevation table-marker horizon (SET-MH) approach is being used globally to quantify the relative contributions of processes affecting wetland elevation change. Historically, SET-MH measurements have been obtained at local scales to address site-specific research questions. However, in the face of accelerated sea-level rise, there is an increasing need for elevation change network data that can be incorporated into regional ecological models and vulnerability assessments. In particular, there is a need for long-term, high-temporal resolution data that are strategically distributed across ecologically-relevant abiotic gradients. Here, we quantify the distribution of SET-MH stations along the northern Gulf of Mexico coast (USA) across political boundaries (states), wetland habitats, and ecologically-relevant abiotic gradients (i.e., gradients in temperature, precipitation, elevation, and relative sea-level rise). Our analyses identify areas with high SET-MH station densities as well as areas with notable gaps. Salt marshes, intermediate elevations, and colder areas with high rainfall have a high number of stations, while salt flat ecosystems, certain elevation zones, the mangrove-marsh ecotone, and hypersaline coastal areas with low rainfall have fewer stations. Due to rapid rates of wetland loss and relative sea-level rise, the state of Louisiana has the most extensive SET-MH station network in the region, and we provide several recent examples where data from Louisiana’s network have been used to assess and compare wetland vulnerability to sea-level rise. Our findings represent the first attempt to examine spatial gaps in SET-MH coverage across abiotic gradients. Our analyses can be used to transform a broadly disseminated and unplanned collection of SET-MH stations into a coordinated and strategic regional network. This regional network would provide data for predicting and preparing for the responses of coastal wetlands to accelerated sea-level rise and other aspects of global change.
","language":"English","publisher":"PLoS ONE","doi":"10.1371/journal.pone.0183431","usgsCitation":"Osland, M.J., Griffith, K.T., Larriviere, J., Feher, L.C., Cahoon, D.R., Enwright, N.M., Oster, D.A., Tirpak, J.M., Woodrey, M.S., Collini, R.C., Baustian, J.J., Breithaupt, J.L., Cherry, J., Conrad, J.R., Cormier, N., Coronado-Molina, C.A., Donoghue, J.F., Graham, S.A., Harper, J.W., Hester, M.W., Howard, R.J., Krauss, K.W., Kroes, D., Lane, R.R., McKee, K.L., Mendelssohn, I.A., Middleton, B.A., Moon, J.A., Piazza, S., Rankin, N.M., Sklar, F.H., Steyer, G.D., Swanson, K.M., Swarzenski, C.M., Vervaeke, W., Willis, J.M., and Van Wilson, K., 2017, Assessing coastal wetland vulnerability to sea-level rise along the northern Gulf of Mexico coast: Gaps and opportunities for developing a coordinated regional sampling network: PLoS ONE, v. 12, no. 9, Article e0183431; 23 p., https://doi.org/10.1371/journal.pone.0183431.","productDescription":"Article e0183431; 23 p.","ipdsId":"IP-084947","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":461407,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0183431","text":"Publisher Index Page"},{"id":438215,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79S1PJ5","text":"USGS data release","linkHelpText":"Assessing coastal wetland vulnerability to sea-level rise along the northern Gulf of Mexico coast: gaps and opportunities for developing a coordinated regional sampling network"},{"id":345775,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n 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ktgriffith@usgs.gov","contributorId":196471,"corporation":false,"usgs":false,"family":"Griffith","given":"Kereen","email":"ktgriffith@usgs.gov","middleInitial":"T.","affiliations":[{"id":17706,"text":"Griffith Consulting Services at U.S. Geological Survey, National Wetlands Research Center","active":true,"usgs":false}],"preferred":false,"id":710438,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Larriviere, Jack C.","contributorId":195562,"corporation":false,"usgs":false,"family":"Larriviere","given":"Jack C.","affiliations":[{"id":34306,"text":"Five Rivers Services, Lafayette, LA, USA","active":true,"usgs":false}],"preferred":false,"id":710439,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feher, Laura C. 0000-0002-5983-6190 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USA","active":true,"usgs":false}],"preferred":false,"id":710446,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Baustian, Joseph J.","contributorId":195568,"corporation":false,"usgs":false,"family":"Baustian","given":"Joseph","email":"","middleInitial":"J.","affiliations":[{"id":34312,"text":"The Nature Conservancy, Baton Rouge, LA, USA","active":true,"usgs":false}],"preferred":false,"id":710447,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Breithaupt, Joshua L.","contributorId":195504,"corporation":false,"usgs":false,"family":"Breithaupt","given":"Joshua","email":"","middleInitial":"L.","affiliations":[{"id":17733,"text":"University of South Florida, St. Petersburg, FL","active":true,"usgs":false}],"preferred":false,"id":710448,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Cherry, Julia A","contributorId":150554,"corporation":false,"usgs":false,"family":"Cherry","given":"Julia A","affiliations":[{"id":33913,"text":"Univ. of Alabama, Tuscaloosa, AL","active":true,"usgs":false}],"preferred":false,"id":710449,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Conrad, Jeremy R.","contributorId":149347,"corporation":false,"usgs":false,"family":"Conrad","given":"Jeremy","email":"","middleInitial":"R.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":710450,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Cormier, Nicole 0000-0003-2453-9900 cormiern@usgs.gov","orcid":"https://orcid.org/0000-0003-2453-9900","contributorId":4262,"corporation":false,"usgs":true,"family":"Cormier","given":"Nicole","email":"cormiern@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":710451,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Coronado-Molina, Carlos A.","contributorId":195566,"corporation":false,"usgs":false,"family":"Coronado-Molina","given":"Carlos","email":"","middleInitial":"A.","affiliations":[{"id":27553,"text":"South Florida Water Management District, West Palm Beach, FL","active":true,"usgs":false}],"preferred":false,"id":710452,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Donoghue, Joseph F.","contributorId":195569,"corporation":false,"usgs":false,"family":"Donoghue","given":"Joseph","email":"","middleInitial":"F.","affiliations":[{"id":34313,"text":"University of Central Florida, Orlando, FL, USA","active":true,"usgs":false}],"preferred":false,"id":710453,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Graham, Sean A.","contributorId":195570,"corporation":false,"usgs":false,"family":"Graham","given":"Sean","email":"","middleInitial":"A.","affiliations":[{"id":33463,"text":"Nicholls State University, Thibodaux, LA","active":true,"usgs":false}],"preferred":false,"id":710454,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Harper, Jennifer W.","contributorId":195571,"corporation":false,"usgs":false,"family":"Harper","given":"Jennifer","email":"","middleInitial":"W.","affiliations":[{"id":34315,"text":"Apalachicola National Estuarine Research Reserve, Eastpoint, FL, USA","active":true,"usgs":false}],"preferred":false,"id":710455,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Hester, Mark W.","contributorId":195572,"corporation":false,"usgs":false,"family":"Hester","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":34316,"text":"University of Louisiana at Lafayette, Lafayette, LA, USA","active":true,"usgs":false}],"preferred":false,"id":710456,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Howard, Rebecca J. 0000-0001-7264-4364 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