The “paleo calendar effect” is a common expression for the impact that changes in the length of months or seasons over time, related to changes in the eccentricity of Earth's orbit and precession, have on the analysis or summarization of climate-model output. This effect can have significant implications for paleoclimate analyses. In particular, using a “fixed-length” definition of months (i.e., defined by a fixed number of days), as opposed to a “fixed-angular” definition (i.e., defined by a fixed number of degrees of the Earth's orbit), leads to comparisons of data from different positions along the Earth's orbit when comparing paleo with modern simulations. This effect can impart characteristic spatial patterns or signals in comparisons of time-slice simulations that otherwise might be interpreted in terms of specific paleoclimatic mechanisms, and we provide examples for 6, 97, 116, and 127 ka. The calendar effect is exacerbated in transient climate simulations in which, in addition to spatial or map-pattern effects, it can influence the apparent timing of extrema in individual time series and the characterization of phase relationships among series. We outline an approach for adjusting paleo simulations that have been summarized using a modern fixed-length definition of months and that can also be used for summarizing and comparing data archived as daily data. We describe the implementation of this approach in a set of Fortran 90 programs and modules (PaleoCalAdjust v1.0).
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-12-3889-2019","usgsCitation":"Bartlein, P.J., and Shafer, S., 2019, Paleo calendar-effect adjustments in time-slice and transient climate-model simulations (PaleoCalAdjust v1.0): Impact and strategies for data analysis: Geoscientific Model Development, v. 12, p. 3889-3913, https://doi.org/10.5194/gmd-12-3889-2019.","productDescription":"25 p.","startPage":"3889","endPage":"3913","ipdsId":"IP-101124","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":459948,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-12-3889-2019","text":"Publisher Index Page"},{"id":397667,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Bartlein, Patrick J. 0000-0001-7657-5685","orcid":"https://orcid.org/0000-0001-7657-5685","contributorId":211587,"corporation":false,"usgs":false,"family":"Bartlein","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":33397,"text":"U of Oregon","active":true,"usgs":false}],"preferred":false,"id":838926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shafer, Sarah 0000-0003-3739-2637 sshafer@usgs.gov","orcid":"https://orcid.org/0000-0003-3739-2637","contributorId":149866,"corporation":false,"usgs":true,"family":"Shafer","given":"Sarah","email":"sshafer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838927,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70206676,"text":"70206676 - 2019 - North American Bat Monitoring Program regional protocol for surveying with stationary deployments of echolocation recording devices: Narrative version 1.0, Pacific Northwestern US","interactions":[],"lastModifiedDate":"2019-12-03T07:09:37","indexId":"70206676","displayToPublicDate":"2019-08-31T12:34:18","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/UCBN/NRR—2019/1975","title":"North American Bat Monitoring Program regional protocol for surveying with stationary deployments of echolocation recording devices: Narrative version 1.0, Pacific Northwestern US","docAbstract":"The outbreak of white-nose syndrome (WNS) and the growing awareness of the risks to bats from wind power generating facilities have driven radical changes to North American bat conservation. Over the last decade, formerly common species such as the little brown myotis (Myotis lucifugus) and hoary bat (Lasiurus cinereus) have experienced unprecedented mortality rates and are now facing non-trivial extinction risk.
In response to this change, federal land management agencies such as the US National Park Service, US Fish and Wildlife Service, US Forest Service, US Bureau of Land Management and state wildlife management agencies such as the Oregon Department of Fish and Wildlife and Idaho Fish and Game have invested in collaborative, interagency bat monitoring to close the gap in information about bat welfare and to inform bat conservation strategies.
Bats are notoriously difficult to track and study and there remains a paucity of fundamental information about the seasonal patterns of bat activity and habitat use and population distributions and abundances. Moreover, because bats are so highly mobile and difficult to survey (e.g., nocturnal flight), this information needs to be contextualized at broad regional (e.g., 10,000 km2) and range-wide extents. Delimiting bat populations at local scales (e.g., 100 km2) is very difficult and it is not clear, for example, how a declining trend in local (e.g., a small park unit) patterns of bat activity or relative abundance should be interpreted without broader context.
In recognition of these challenges, a plan for coordinated continental-scale monitoring of bats, the North American Bat Monitoring Program (NABat) was developed (Loeb et al. 2015). The centerpiece of the plan is the use of a spatially-balanced randomized master sample of grid-cell sample units from a grid-based sampling frame to provide the architecture for collaboration and the statistical foundation for making inferences about bat populations across broad regions and entire bat geographic ranges. The plan outlines general goals, survey design, and field methods for both summertime acoustic surveys of bats as well as winter and summer counts of bats in hibernacula and maternity colonies but it does not provide field-level protocol and standard operating procedures for consistent and efficient implementation.
This regional protocol provides these details for one component of NABat, the deployment of stationary acoustic detectors to record bats during summer, as is called for by the NABat plan. This protocol was written specifically to provide guidance and consistency across the Pacific Northwestern US (N. California [California Department of Fish and Wildlife Northern Region], Idaho, Washington, and Oregon; US Fish and Wildlife Service Region 1 and portion of Region 8 [in Northern California and Klamath Basin]; US Forest Service Region 6 and portions of Regions 1 and 5 in Idaho; and the Upper Columbia Basin, North Coast Cascades, and Klamath Networks of the National Park Service). This region has internal cohesion, sharing a distinct bat faunal assemblage of 15 species (with several additional species occurring on the southern periphery of the region), and a long history of collaborative bat monitoring beginning with the interagency Bat Grid Program which operated from 2003-2010 across Oregon and Washington (US Forest Service Region 6).
This protocol will be coordinated and implemented by the Northwestern Bat Hub, on behalf of the collective interagency partnership. The Northwestern Bat Hub is housed on the Oregon State University-Cascades campus and leverages pooled partner funds and resources to maintain a small staff that coordinates and conducts monitoring, provides training and oversight, ensures high-quality data quality and control, and analyzes data and reports on results.
Many efforts have been made to map developed impervious surface from remotely sensed information in the last two decades. The U.S. Geological Survey (USGS) developed the National Land Cover Database (NLCD) to provide consistent land cover and change products for the Nation since 2001. Percent impervious surface area (ISA), one of the products in NLCD as a continuous field and estimated with Landsat imagery, represents the fraction of human-made impervious area in a 30 m resolution grid. ISA is used to map urban land cover types and extents for the United States. However, it is still a challenge to quantify highly heterogeneous features in many urban areas using remotely sensed data with spatial and spectral resolutions similar to Landsat and to determine the impacts of remotely sensed data characteristics on ISA mapping.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2019.100246","usgsCitation":"Xian, G.Z., Shi, H., Dewitz, J., and Wu, Z., 2019, Performances of WorldView-3, Sentinel-2, and Landsat-8 data in mapping impervious surface: Remote Sensing Applications: Society and Environment, v. 15, 100246, 11 p., https://doi.org/10.1016/j.rsase.2019.100246.","productDescription":"100246, 11 p.","ipdsId":"IP-102241","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467328,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rsase.2019.100246","text":"Publisher Index Page"},{"id":377728,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Texas","city":"San Francisco, Dallas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.9150390625,\n 37.47485808497102\n ],\n [\n -121.904296875,\n 37.47485808497102\n ],\n [\n -121.904296875,\n 38.13455657705411\n ],\n [\n -122.9150390625,\n 38.13455657705411\n ],\n [\n -122.9150390625,\n 37.47485808497102\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.94335937499999,\n 32.58384932565662\n ],\n [\n -96.5478515625,\n 32.58384932565662\n ],\n [\n -96.5478515625,\n 32.95336814579932\n ],\n [\n -96.94335937499999,\n 32.95336814579932\n ],\n [\n -96.94335937499999,\n 32.58384932565662\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":796916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":796917,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dewitz, Jon 0000-0002-0458-212X","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":215192,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":796918,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":796919,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205696,"text":"70205696 - 2019 - The effect of resolution on terrain feature extraction","interactions":[],"lastModifiedDate":"2019-10-08T07:06:17","indexId":"70205696","displayToPublicDate":"2019-08-30T12:48:21","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"The effect of resolution on terrain feature extraction","docAbstract":"Recent increase in the production of high-resolution digital elevation models (DEMs) from lidar data has led to interest in their use for terrain mapping. Although the impact of different resolutions has been studied relative to terrain characteristics like roughness, slope and curvature, its relationship to the extraction of terrain features remains unclear. To address this question, this study tests the impact of four resolutions on the capture of glacial cirques from DEMs. Mean curvature was derived from one arc-second, one-third arc-second, one-ninth arc-second and half meter DEMs representing a cirque-covered mountainous region southwest of Lake Tahoe, California. Using a GEOBIA workflow, ridge objects were identified, and three scales - via the multi-resolution scale parameter (SP) - of objects bordering the ridges were classified as cirque objects. The resulting classifications were compared to reference cirques digitized at a scale of ~1:10,000. Results show that the one-third arc-second DEM produces the set of cirque objects most closely resembling the reference cirques. The one-ninth arc-second DEM afforded the second-best classification. These results emphasize the importance in carefully choosing resolution relative to the features extracted, rather than using the highest resolution data available. In the case of GEOBIA workflows, the choice of scale parameter is equally important.","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Geomorphometry 2018","largerWorkSubtype":{"id":19,"text":"Conference Paper"},"conferenceDate":"August 13-17, 2018","language":"English","publisher":"PeerJ","doi":"10.7287/peerj.preprints.27072v1","usgsCitation":"Arundel, S., Li, W., and Zhou, X., 2019, The effect of resolution on terrain feature extraction, in Geomorphometry 2018, August 13-17, 2018, 4 p., https://doi.org/10.7287/peerj.preprints.27072v1.","productDescription":"4 p.","ipdsId":"IP-094644","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":460303,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.7287/peerj.preprints.27072v1","text":"External Repository"},{"id":368033,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada mountain range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.23574829101562,\n 38.82981255949193\n ],\n [\n -120.02632141113283,\n 38.82981255949193\n ],\n [\n -120.02632141113283,\n 39.0047782882536\n ],\n [\n -120.23574829101562,\n 39.0047782882536\n ],\n [\n -120.23574829101562,\n 38.82981255949193\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":772079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Li, Wenwen 0000-0003-2237-9499","orcid":"https://orcid.org/0000-0003-2237-9499","contributorId":219356,"corporation":false,"usgs":false,"family":"Li","given":"Wenwen","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":772080,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhou, Xiran","contributorId":219357,"corporation":false,"usgs":false,"family":"Zhou","given":"Xiran","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":772081,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223413,"text":"70223413 - 2019 - An importance–satisfaction analysis of trout license holders in Georgia","interactions":[],"lastModifiedDate":"2021-08-26T16:13:39.597322","indexId":"70223413","displayToPublicDate":"2019-08-30T11:10:40","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"An importance–satisfaction analysis of trout license holders in Georgia","docAbstract":"As anglers become increasingly diverse, fisheries managers are challenged to find ways to satisfy users with divergent preferences while conserving a limited resource on a limited budget. With this management challenge in mind, this study combines previous angler specialization research with an importance–satisfaction analysis (ISA) to aid fisheries managers in better understanding users with divergent preferences. An ISA involves asking stakeholders to simultaneously evaluate the importance of salient attributes of their fishing experience and their satisfaction with those attributes, which allows for graphical depiction of each attribute in one of four quadrants with managerial implications: Concentrate Here, Keep up the Good Work, Possible Overkill, and Low Priority. The data were collected via a mail-administered survey that was sent to 4,000 licensed trout anglers who were residents of Georgia. The anglers were asked to evaluate 10 catch and 7 noncatch attributes as well as answer questions that were used to measure levels of angler specialization. A K-means cluster analysis was used to segment the 469 respondents into three groups based on their specialization level. Analysis of variance (ANOVA) was used to determine whether anglers’ preferences differed by level of specialization. The results revealed that specialization groups differed in the level of importance that they placed on 6 catch and 4 noncatch attributes and in their levels of satisfaction with 5 catch and 2 noncatch attributes. There were also differences among the groups in quadrant placement for both catch and noncatch attributes. The respondents agreed that habitat improvement initiatives, adding new fishable water, and clear signage of regulations were of relatively high importance, with habitat improvement initiatives as the only attribute to fall in the Concentrate Here quadrant for all three clusters. The average responses for importance and satisfaction were highest for the most specialized group and lowest for the least specialized group. Combining segmenting techniques, like cluster analysis with ISA, offers fisheries managers the opportunity to gauge different user groups’ perceptions of salient fishery attributes.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10353","usgsCitation":"TenHarmsel, H.J., Boley, B.B., Irwin, B.J., and Jennings, C.A., 2019, An importance–satisfaction analysis of trout license holders in Georgia: North American Journal of Fisheries Management, v. 39, no. 6, p. 1227-1241, https://doi.org/10.1002/nafm.10353.","productDescription":"15 p.","startPage":"1227","endPage":"1241","ipdsId":"IP-102950","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":467329,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10353","text":"Publisher Index Page"},{"id":388549,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"39","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"TenHarmsel, H. J.","contributorId":264803,"corporation":false,"usgs":false,"family":"TenHarmsel","given":"H.","email":"","middleInitial":"J.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":821992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boley, B. B.","contributorId":264805,"corporation":false,"usgs":false,"family":"Boley","given":"B.","email":"","middleInitial":"B.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":821993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin, Brian J. 0000-0002-0666-2641 bjirwin@usgs.gov","orcid":"https://orcid.org/0000-0002-0666-2641","contributorId":4037,"corporation":false,"usgs":true,"family":"Irwin","given":"Brian","email":"bjirwin@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":821994,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jennings, Cecil A. 0000-0002-6159-6026 jennings@usgs.gov","orcid":"https://orcid.org/0000-0002-6159-6026","contributorId":874,"corporation":false,"usgs":true,"family":"Jennings","given":"Cecil","email":"jennings@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":821995,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205217,"text":"70205217 - 2019 - Black Carp in North America: A description of range, habitats, time of year, and methods of reported captures","interactions":[],"lastModifiedDate":"2019-10-28T10:18:37","indexId":"70205217","displayToPublicDate":"2019-08-29T11:53:05","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Black Carp in North America: A description of range, habitats, time of year, and methods of reported captures","docAbstract":"Black Carp Mylopharyngodon piceus are considered invasive in North America. Since the first wild capture in 2003, collection records have increased, yet information summarizing successful collection methods is lacking. Reported capture methods throughout the Black Carp's native and introduced ranges vary providing minimal aid for determining control and monitoring methods. Here, we describe the current species range and the spatial and temporal variation among captures. The size of fish can affect capture; thus, we report captured fish and gear dimensions and recommend appropriate scientific collection methods. We focus on collection data from 302 Black Carp ranging from 410 to 1,607 mm total length received from 2011 to February 2019. The reported range of Black Carp has expanded in the Cumberland, Illinois, Mississippi, Missouri, Ohio, and Tennessee rivers. Captures most frequently occurred in channel (41%), side‐channel (24%), and backwater (22%) habitat types, with increased records in May, June, and July. Most records were commercial captures, of which hoop net (51%) and gill net (26%) were most common. Results suggest that standard scientific methods for sampling fish in large rivers and standing water by hoop net and gill net may be used to monitor Black Carp, but a robust study design needs to be applied to determine gear selectivity and to determine if catch rates are density dependent or incidental.
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U.S.","interactions":[],"lastModifiedDate":"2019-09-03T09:49:54","indexId":"70205094","displayToPublicDate":"2019-08-28T09:47:52","publicationYear":"2019","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":"Characterizing and imaging sedimentary strata using depth-converted spectral ratios: An example from the Atlantic Coastal Plain of the Eastern U.S.","docAbstract":"Unconsolidated, near-surface sediments can have a profound influence on the amplitudes and frequencies of ground shaking during earthquakes, and these effects should be accounted for when using amplitude observations for seismic hazard assessments. This study explores methods to use teleseismic arrivals recorded on linear receiver arrays to characterize widespread, shallow sedimentary deposits, including estimation of the velocities, determination of the fundamental resonance peaks, and imaging of the major reflectors. The examples used are the extensive Atlantic Coastal Plain (ACP) and associated Mississippi Embayment (ME) strata of the Central and Eastern United States. The large contrast in material properties at the bedrock surface beneath these sediments produces a strong fundamental resonance peak in the 0.2 to 4 Hz frequency range, which is estimated here by computing spectral ratios at each receiver site relative to bedrock sites at the ends of the receiver arrays. Sediment thicknesses derived from published contour maps made from drill hole data allow for the computation of average velocities to match the observed frequencies of resonance peaks with theoretical values at each receiver site, with the sloping bedrock surface allowing for computation of an average velocity versus depth function if horizontal layers are assumed. The velocity function is then used to convert the spectral ratios from frequency to depth, resulting in an image of the subsurface similar to that of a seismic reflection profile. The results demonstrate the use of teleseismic signals for characterizing and imaging shallow sedimentary strata.\n ","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120180046","usgsCitation":"Pratt, T.L., 2019, Characterizing and imaging sedimentary strata using depth-converted spectral ratios: An example from the Atlantic Coastal Plain of the Eastern U.S.: Bulletin of the Seismological Society of America, v. 108, no. 5A, p. 2801-2815, https://doi.org/10.1785/0120180046.","productDescription":"15 p.","startPage":"2801","endPage":"2815","ipdsId":"IP-098671","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":367129,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Atlantic Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.498046875,\n 39.198205348894795\n ],\n [\n -77.7392578125,\n 38.71980474264237\n ],\n [\n -86.9677734375,\n 33.7243396617476\n ],\n [\n -92.0654296875,\n 32.58384932565662\n ],\n [\n -85.2978515625,\n 30.524413269923986\n ],\n [\n -81.1669921875,\n 27.994401411046148\n ],\n [\n -80.85937499999999,\n 26.391869671769022\n ],\n [\n -77.607421875,\n 31.052933985705163\n ],\n [\n -74.0478515625,\n 34.813803317113155\n ],\n [\n -75.1904296875,\n 39.13006024213511\n ],\n [\n -75.498046875,\n 39.198205348894795\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"108","issue":"5A","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":770000,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70204980,"text":"ofr20191097 - 2019 - Juvenile Chinook salmon (Oncorhynchus tshawytscha) survival in Lookout Point Reservoir, Oregon, 2018","interactions":[],"lastModifiedDate":"2019-08-28T10:07:50","indexId":"ofr20191097","displayToPublicDate":"2019-08-27T13:00:05","publicationYear":"2019","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":"2019-1097","displayTitle":"Juvenile Chinook Salmon (Oncorhynchus tshawytscha) Survival in Lookout Point Reservoir, Oregon, 2018","title":"Juvenile Chinook salmon (Oncorhynchus tshawytscha) survival in Lookout Point Reservoir, Oregon, 2018","docAbstract":"A field study was conducted to estimate survival of juvenile Chinook salmon (Oncorhynchus tshawytscha) in Lookout Point Reservoir, Oregon, during 2018. The study consisted of releasing three groups of genetically-marked fish into the reservoir, and sampling them monthly. Juveniles were released during April 10–13 (116,708 fish), May 15–18 (31,911 fish), and June 19–20 (11,758 fish). Reservoir sampling began in May and occurred monthly through October, consisting of 5-day events where juvenile Chinook salmon were collected using electrofishing, shoreline traps, and gill nets. Data were analyzed using a staggered release-recovery model and a parentage-based tagging (PBT) N-mixture model. The staggered release-recovery model provided survival estimates from three periods: mid-April to mid-May (SSRRM1); mid-May to mid-June (SSRRM2); and mid-April to mid-June (SSRRM12). Multiple estimates of survival were possible for each period using different combinations of recovery data from the three groups of fish that were released. Survival probability estimates for SSRRM1 ranged from 0.98520 to 0.98954; estimates for SSRRM2 ranged from 0.09338 to 0.62142; and the estimate for cumulative survival from mid-April to mid-June (SSRRM12) were 0.75211. We suspect that issues with release groups in May (R2) and June (R3) led to biased survival results using the staggered release-recovery model. The PBT N-mixture model provided survival estimates from six periods: mid-April to mid-May (SNMIX1); mid-May to mid-June (SNMIX2), mid-June to mid-July (SNMIX3), mid-July to mid-August (SNMIX4), mid-August to mid-September (SNMIX5); and mid-September to mid-October (SNMIX6). Survival estimates from the PBT N-mixture model were lowest for SNMIX6 (0.41620) and highest for SNMIX1 (0.79587). These results differed from those in 2017 when monthly survival increased across months. This suggests that one or more factors could have affected juvenile Chinook salmon survival in Lookout Point Reservoir. One possible factor could be copepods (which were highly prevalent on juvenile Chinook salmon during summer 2018), but environmental factors such as reserveroir elevation, discharge at Lookout Point Dam, and fish distributions within the reservoir differed between study years. Two PBT N-mixture models provided cumulative survival estimates from mid-April to mid-October. Estimates from the two models were 0.061 and 0.039, which suggests that survival of subyearling Chinook salmon in Lookout Point Reservoir was very low in 2018. Additional research is recommended to better understand inter-annual variability of subyearling Chinook salmon in the reservoir and to gain insights into factors that affect their survival.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191097","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and Oregon State University","usgsCitation":"Kock, T.J., Perry, R.W., Hansen, G.S., Haner, P.V., Pope, A.C., Plumb, J.M., Cogliati, K.M., and Hansen, A.C., 2019, Juvenile Chinook salmon (Oncorhynchus tshawytscha) survival in Lookout Point Reservoir, Oregon, 2018: U.S. Geological Survey Open-File Report 2019–1097, 41 p., https://doi.org/10.3133/ofr20191097.","productDescription":"vi, 41 p.","onlineOnly":"Y","ipdsId":"IP-108747","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":366974,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1097/coverthb.jpg"},{"id":366975,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1097/ofr20191097.pdf","text":"Report","size":"6.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1097"}],"country":"United States","state":"Oregon","otherGeospatial":"Lookout Point Reservoir, Middle Fork Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.8165054321289,\n 43.80009302166679\n ],\n [\n -122.55970001220705,\n 43.80009302166679\n ],\n [\n -122.55970001220705,\n 43.93919224634882\n ],\n [\n -122.8165054321289,\n 43.93919224634882\n ],\n [\n -122.8165054321289,\n 43.80009302166679\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Hyperalkaline (pH greater than 12) ponds and groundwater exist at Big Marsh near Lake Calumet, Chicago, Illinois, a site used by the steel industry during the mid-1900s to deposit steel- and iron-making waste, in particular, slag. The hyperalkaline ponds may pose a hazard to human health and the environment. The U.S. Geological Survey (USGS), in cooperation with the Environmental Protection Agency (EPA) and in collaboration with the City of Chicago’s Park District, completed a study to evaluate the hydrologic balance, water quality, and chemical-mass balance of hyperalkaline ponds at Big Marsh and geochemical modeling used to evaluate remediation options for water quality at the site based on data collected in 2016–17.
Synoptic measurements of surface-water and groundwater elevations were used to determine flow directions and to enable a preliminary estimate of the hydrologic balance for the ponds. Water-quality samples also were collected and analyzed for selected constituents including major anions and cations, nutrients, metals, and trace elements. The results of the water-quality analyses were used to develop a geochemical model to evaluate concentrations, factors affecting pH, and the state of equilibrium between surface waters and atmospheric carbon dioxide. The geochemical model was used to evaluate remediation scenarios using riprap, spillways, or active aeration. The results indicate that active aeration will decrease the pH to near 7.5 in about 8 hours, the fastest rate of the scenarios. Passive aeration, such as riprap or spillways, also can be effective at decreasing the pH in about 45 hours, but spatial obstacles limit their implementation. Seasonal variations in temperature also affect the rate of equilibration, where colder temperatures may have a lower pH than warmer temperatures and may affect the timing and frequency of remediation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195078","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency, Brownfields Program, and in collaboration with the City of Chicago’s Park District","usgsCitation":"Gahala, A.M., Seal, R.R., and Piatak, N.M., 2019, Hydrologic balance, water quality, chemical-mass balance, and geochemical modeling of hyperalkaline ponds at Big Marsh, Chicago, Illinois, 2016–17: U.S. Geological Survey Scientific Investigations Report 2019–5078, 31 p., https://doi.org/10.3133/sir20195078.","productDescription":"Report: vi, 31 p.; Data Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-091826","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science 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Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Daily mean discharges are needed for rivers in New Hampshire for the management of instream flows. It is impractical, however, to continuously gage all streams in New Hampshire, and at many sites where information is needed, the discharge data required do not exist. For such sites, techniques for estimating discharge are available. The U.S. Geological Survey, in cooperation with the New Hampshire Department of Environmental Services, developed and evaluated the accuracy of estimated discharge records for six discontinued U.S. Geological Survey streamgages in New Hampshire.
The estimated records were developed by using the maintenance of variance extension, type 1 (MOVE.1), record extension technique and were generated for periods with concurrent observed records to allow for evaluation. The six discontinued streamgages were on New Hampshire designated rivers throughout the State and had drainage areas ranging from 35.6 to 395 square miles with little to no regulation.
Estimated records for four of the six streamgages had Nash-Sutcliffe efficiency coefficients greater than 0.85. The other two streamgages had Nash-Sutcliffe efficiency coefficients between 0.45 and 0.60. For the four streamgages with the higher Nash-Sutcliffe efficiency coefficients, more than 35 percent of the estimated record was within 15 percent of the observed record. At the other two streamgages, more than 23 percent of the estimated record was within 15 percent of the observed record.
At lower discharges (exceeded 80 percent of the time), for four of the six streamgages, more than 40 percent of the estimated record was within 15 percent of the observed record. The site with the lowest Nash-Sutcliffe efficiency coefficient had more than 14 percent of the estimated record at low discharges within 15 percent of the observed record.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195066","collaboration":"Prepared in cooperation with the New Hampshire Department of Environmental Services","usgsCitation":"Olson, S.A., and Meyerhofer, A.J., 2019, Development and evaluation of a record extension technique for estimating discharge at selected stream sites in New Hampshire: U.S. Geological Survey Scientific Investigations Report 2019–5066, 23 p., https://doi.org/10.3133/sir20195066.","productDescription":"iv, 23 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-102986","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":366553,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5066/coverthb2.jpg"},{"id":366554,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5066/sir20195066.pdf","text":"Report","size":"8.60 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Hampshire\",\"nation\":\"USA \"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
In 2018, the U.S. Geological Survey (USGS), in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, studied floods through the period of record to create a library of flood-inundation maps for the Medina River at Bandera, Texas. Digital flood-inundation maps for a 23-mile reach of the Medina River at and near Bandera, from the confluence with Winans Creek to English Crossing Road, were developed. The flood-inundation maps depict estimates of the areal extent and depth of flooding corresponding to a range of different gage heights (gage height is commonly referred to as “stage,” or the water-surface elevation at a streamflow-gaging station) at USGS streamflow-gaging station 08178880 Medina River at Bandera, Tex. (hereinafter referred to as the “Bandera station”). Water-surface profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The stage-discharge (streamflow) relation effective in 2018 was used to calibrate the model, and stages from four recent flood events were used to independently validate the model. The calibrated hydraulic model was then used to compute 29 water-surface profiles for stages at 1-foot (ft) increments referenced to the station datum and ranging from 10 ft (near bankfull) to 38 ft, which exceeds the major flood stage of the National Weather Service Advanced Hydrologic Prediction Service of 24 ft. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.4-ft vertical accuracy and 1.6-ft horizontal resolution) to delineate the area flooded for stages ranging from 10 to 38 ft.
The digital flood-inundation maps are delivered through the USGS Flood Inundation Mapper application that presents map libraries and provides detailed information on flood-inundation extents and stages for modeled sites. The flood-inundation maps developed in this study, in conjunction with the real-time stage data from the Bandera station, are intended to help guide the public in taking individual safety precautions and provide emergency management personnel with a tool to efficiently manage emergency flood operations and post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195067","collaboration":"Prepared in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board","usgsCitation":"Choi, N., and Engel, F.L., 2019, Flood-inundation maps for a 23-mile reach of the Medina River at Bandera, Texas, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5067, 15 p., https://doi.org/10.3133/sir20195067.","productDescription":"Report: viii, 15 p.; Fact Sheet: 2 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-104084","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":366755,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/fs20193043","text":"FS 2019–3043","size":"895 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3043","linkHelpText":" Flood Warning Toolset for the Medina River in Bandera County, Texas"},{"id":366756,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WYD6LS","text":"USGS data release ","linkHelpText":"Geospatial and survey data for flood-inundation maps in a 23-mile reach of the Medina River at Bandera, Texas, 2018"},{"id":366666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5067/coverthb.jpg"},{"id":366667,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5067/sir20195067.pdf","text":"Report","size":"3.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5067"}],"contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level using widely available remote sensing data, regional changes in forest composition can readily be monitored. In this study, wall-to-wall maps of species-level AFB were generated for forests in Northeast China by integrating forest inventory data with Moderate Resolution Imaging Spectroradiometer (MODIS) images and environmental variables through applying the optimal k-nearest neighbor (kNN) imputation model. By comparing the prediction accuracy of 630 kNN models, we found that the models with random forest (RF) as the distance metric showed the highest accuracy. Compared to the use of single-month MODIS data for September, there was no appreciable improvement for the estimation accuracy of species-level AFB by using multi-month MODIS data. When k > 7, the accuracy improvement of the RF-based kNN models using the single MODIS predictors for September was essentially negligible. Therefore, the kNN model using the RF distance metric, single-month (September) MODIS predictors and k = 7 was the optimal model to impute the species-level AFB for entire Northeast China. Our imputation results showed that average AFB of all species over Northeast China was 101.98 Mg/ha around 2000. Among 17 widespread species, larch was most dominant, with the largest AFB (20.88 Mg/ha), followed by white birch (13.84 Mg/ha). Amur corktree and willow had low AFB (0.91 and 0.96 Mg/ha, respectively). Environmental variables (e.g., climate and topography) had strong relationships with species-level AFB. By integrating forest inventory data and remote sensing data with complete spatial coverage using the optimal kNN model, we successfully mapped the AFB distribution of the 17 tree species over Northeast China. We also evaluated the accuracy of AFB at different spatial scales. The AFB estimation accuracy significantly improved from stand level up to the ecotype level, indicating that the AFB maps generated from this study are more suitable to apply to forest ecosystem models (e.g., LINKAGES) which require species-level attributes at the ecotype scale.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11172005","usgsCitation":"Fu, Y., He, H.S., Hawbaker, T., Henne, P., Zhu, Z., and Larsen, D.R., 2019, Evaluating k-nearest neighbor (kNN) imputation models for species-level aboveground forest biomass mapping in northeast China: Remote Sensing, v. 17, no. 11, p. 1-20, https://doi.org/10.3390/rs11172005.","productDescription":"20 p.","startPage":"1","endPage":"20","ipdsId":"IP-109980","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":467340,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11172005","text":"Publisher Index Page"},{"id":437358,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MOB5E3","text":"USGS data release","linkHelpText":"Data release for: Evaluating k-nearest neighbor (kNN) imputation models for species-level aboveground forest biomass mapping in northeast China"},{"id":367198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 124.8046875,\n 40.3130432088809\n ],\n [\n 129.90234375,\n 43.32517767999296\n ],\n [\n 131.484375,\n 42.293564192170095\n ],\n [\n 135.17578125,\n 48.45835188280866\n ],\n [\n 130.95703125,\n 47.87214396888731\n ],\n [\n 124.27734374999999,\n 53.54030739150022\n ],\n [\n 120.41015624999999,\n 52.696361078274485\n ],\n [\n 118.47656249999999,\n 49.61070993807422\n ],\n [\n 116.54296874999999,\n 49.724479188712984\n ],\n [\n 115.83984375,\n 47.87214396888731\n ],\n [\n 119.00390625,\n 46.92025531537451\n ],\n [\n 110.91796875,\n 44.465151013519616\n ],\n [\n 118.65234374999999,\n 37.996162679728116\n ],\n [\n 124.8046875,\n 40.3130432088809\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Fu, Yuanyuan","contributorId":218762,"corporation":false,"usgs":false,"family":"Fu","given":"Yuanyuan","email":"","affiliations":[{"id":39903,"text":"School of Geographical Sciences, Northeast Normal University, Changchun, China","active":true,"usgs":false}],"preferred":false,"id":770181,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"He, Hong S","contributorId":218764,"corporation":false,"usgs":false,"family":"He","given":"Hong","email":"","middleInitial":"S","affiliations":[{"id":39904,"text":"University of Missouri, School of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":770182,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":770183,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Henne, Paul D. 0000-0003-1211-5545 phenne@usgs.gov","orcid":"https://orcid.org/0000-0003-1211-5545","contributorId":169166,"corporation":false,"usgs":true,"family":"Henne","given":"Paul D.","email":"phenne@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":770180,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"preferred":true,"id":770184,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Larsen, David R. 0000-0001-5861-8952","orcid":"https://orcid.org/0000-0001-5861-8952","contributorId":218763,"corporation":false,"usgs":false,"family":"Larsen","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":36845,"text":"School of Natural Resources, University of Missouri","active":true,"usgs":false}],"preferred":false,"id":770185,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70204968,"text":"70204968 - 2019 - Acoustic tag retention rate varies between juvenile green and hawksbill sea turtles","interactions":[],"lastModifiedDate":"2019-10-11T15:49:10","indexId":"70204968","displayToPublicDate":"2019-08-24T14:33:09","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Acoustic tag retention rate varies between juvenile green and hawksbill sea turtles","docAbstract":"Background\nBiotelemetry has become a key tool for studying marine animals in the last decade, and a wide range of electronic tags are now available for answering a range of research questions. However, comparatively, less attention has been given to attachment methods for these tags and the implications of tag retention on study design, especially when designing a comparative study looking at multiple species. Here, we reported our findings on acoustic tag retention rates for juveniles of two species of marine turtle: the green sea turtle (Chelonia mydas) and the hawksbill sea turtle (Eretmochelys imbricata). We captured both species twice annually (spring and fall) from 2012 through 2017, as part of a capture–mark–recapture study at Buck Island Reef National Monument, St. Croix, U.S. Virgin Islands. We assessed tag retention rates using physical recaptures of turtles previously outfitted with an acoustic tag.\n\nResults\nWe deployed 72 acoustic tags on 60 juvenile greens and 37 acoustic tags on 29 hawksbills. We estimated the half-life for tags on greens to be 150 days (95% CI 117–188 days), whereas the half-life for tags on hawksbills was 1077 days (95% CI 870–2118 days), a marked difference. We observed that tag attachment holes, drilled into the posterior marginal scutes, migrated laterally towards the outer edge of the marginals in both species. Green turtles tended to exhibit tear-outs, as their attachment holes wore and/or tags grew near the edge of their scutes, whereas hawksbills tended to maintain the structure of these holes and did not exhibit these tear-outs.\n\nConclusions\nWe conclude that hawksbills can be tagged with long-battery-life acoustic tags for long-term studies of habitat use and movement patterns, whereas greens are likely to shed their tags in the 1st year, making long-term studies difficult. This study is the first clear evidence that tagging protocols should vary between species of hard-shelled turtles. Furthermore, shed tags on the seafloor continue to be detected by acoustic receivers, creating a challenge in data filtering before analysis. We encourage future research into an efficient method for filtering these data points prior to analysis.","language":"English","publisher":"Springer","doi":"10.1186/s40317-019-0177-3","usgsCitation":"Selby, T., Smith, B., Cherkiss, M., Crowder, A., Hillis-Starr, Z., Pollock, C., and Hart, K., 2019, Acoustic tag retention rate varies between juvenile green and hawksbill sea turtles: Animal Biotelemetry, v. 7, Article 15, 8 p., https://doi.org/10.1186/s40317-019-0177-3.","productDescription":"Article 15, 8 p.","ipdsId":"IP-105289","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467341,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-019-0177-3","text":"Publisher Index Page"},{"id":437359,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P998D45N","text":"USGS data release","linkHelpText":"Capture histories and tag retention of acoustic-tagged green (Chelonia mydas) and hawksbill (Eretmochelys imbricata) sea turtles, Buck Island Reef National Monument, U.S. Virgin Islands, 2012-2017"},{"id":366973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Selby, Thomas 0000-0003-2116-0807","orcid":"https://orcid.org/0000-0003-2116-0807","contributorId":218465,"corporation":false,"usgs":true,"family":"Selby","given":"Thomas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":769318,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Brian 0000-0002-0531-0492 bjsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":202305,"corporation":false,"usgs":true,"family":"Smith","given":"Brian","email":"bjsmith@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":773012,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cherkiss, Michael 0000-0002-7802-6791","orcid":"https://orcid.org/0000-0002-7802-6791","contributorId":218466,"corporation":false,"usgs":true,"family":"Cherkiss","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":769319,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crowder, Andrew 0000-0001-6978-6265","orcid":"https://orcid.org/0000-0001-6978-6265","contributorId":218467,"corporation":false,"usgs":true,"family":"Crowder","given":"Andrew","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":769320,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hillis-Starr, Zandy","contributorId":179152,"corporation":false,"usgs":false,"family":"Hillis-Starr","given":"Zandy","email":"","affiliations":[],"preferred":false,"id":769321,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pollock, Clayton","contributorId":168497,"corporation":false,"usgs":false,"family":"Pollock","given":"Clayton","affiliations":[],"preferred":false,"id":769322,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":218468,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":769323,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70254786,"text":"70254786 - 2019 - Strategic conservation for lesser prairie-chickens among landscapes of varying anthropogenic influence","interactions":[],"lastModifiedDate":"2024-06-07T14:20:41.927474","indexId":"70254786","displayToPublicDate":"2019-08-24T09:10:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Strategic conservation for lesser prairie-chickens among landscapes of varying anthropogenic influence","docAbstract":"For millennia grasslands have provided a myriad of ecosystem services and have been coupled with human resource use. The loss of 46% of grasslands worldwide necessitates the need for conservation that is spatially, temporally, and socioeconomically strategic. In the Southern Great Plains of the United States, conversion of native grasslands to cropland, woody encroachment, and establishment of vertical anthropogenic features have made large intact grasslands rare for lesser prairie-chickens (Tympanuchus pallidicinctus). However, it remains unclear how the spatial distribution of grasslands and anthropogenic features constrain populations and influence conservation. We estimated the distribution of lesser prairie-chickens using data from individuals marked with GPS transmitters in Kansas and Colorado, USA, and empirically derived relationships with anthropogenic structure densities and grassland composition. Our model suggested decreased probability of use in 2-km radius (12.6 km2) landscapes that had greater than two vertical features, two oil wells, 8 km of county roads, and 0.15 km of major roads or transmission lines. Predicted probability of use was greatest in 5-km radius landscapes that were 77% grassland. Based on our model predictions, ~10% of the current expected lesser prairie-chicken distribution was available as habitat. We used our estimated species distribution to provide spatially explicit prescriptions for CRP enrollment and tree removal in locations most likely to benefit lesser prairie-chickens. Spatially incentivized CRP sign up has the potential to provide 4189 km2 of additional habitat and strategic application of tree removal has the potential to restore 1154 km2. Tree removal and CRP enrollment are conservation tools that can align with landowner goals and are much more likely to be effective on privately owned working lands.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2019.108213","usgsCitation":"Sullins, D.S., Haukos, D.A., Lautenbach, J.M., Lautenbach, J., Robinson, S.G., Rice, M.B., Sandercock, B.K., Kraft, J.D., Plumb, R.T., Reitz, J., Hutchinson, J.M., and Hagen, C., 2019, Strategic conservation for lesser prairie-chickens among landscapes of varying anthropogenic influence: Biological Conservation, v. 238, 108213, 10 p., https://doi.org/10.1016/j.biocon.2019.108213.","productDescription":"108213, 10 p.","ipdsId":"IP-105616","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467342,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2019.108213","text":"Publisher Index Page"},{"id":429644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.72145468354752,\n 39.86812103477851\n ],\n [\n -103.72145468354752,\n 37.00372049543908\n ],\n [\n -97.7109610420592,\n 37.00372049543908\n ],\n [\n -97.7109610420592,\n 39.86812103477851\n ],\n [\n -103.72145468354752,\n 39.86812103477851\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"238","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sullins, Daniel S.","contributorId":166689,"corporation":false,"usgs":false,"family":"Sullins","given":"Daniel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":902538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":902539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lautenbach, Joseph M.","contributorId":172788,"corporation":false,"usgs":false,"family":"Lautenbach","given":"Joseph","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":902540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lautenbach, Jonathan","contributorId":272579,"corporation":false,"usgs":false,"family":"Lautenbach","given":"Jonathan","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":902541,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robinson, Samantha G.","contributorId":172786,"corporation":false,"usgs":false,"family":"Robinson","given":"Samantha","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":902542,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rice, Mindy B.","contributorId":214399,"corporation":false,"usgs":false,"family":"Rice","given":"Mindy","email":"","middleInitial":"B.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":902543,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sandercock, Brett K.","contributorId":95816,"corporation":false,"usgs":true,"family":"Sandercock","given":"Brett","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":902544,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kraft, John D.","contributorId":172789,"corporation":false,"usgs":false,"family":"Kraft","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":902545,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Plumb, Reid T.","contributorId":172787,"corporation":false,"usgs":false,"family":"Plumb","given":"Reid","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":902546,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reitz, Jonathan H.","contributorId":337597,"corporation":false,"usgs":false,"family":"Reitz","given":"Jonathan H.","affiliations":[{"id":40103,"text":"cdpw","active":true,"usgs":false}],"preferred":false,"id":902547,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hutchinson, J. M. Shawn","contributorId":337599,"corporation":false,"usgs":false,"family":"Hutchinson","given":"J.","email":"","middleInitial":"M. Shawn","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":902548,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hagen, Christian A.","contributorId":279696,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":902549,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70207981,"text":"70207981 - 2019 - Balancing sampling intensity against spatial coverage for a community science monitoring programme","interactions":[],"lastModifiedDate":"2020-01-22T15:32:09","indexId":"70207981","displayToPublicDate":"2019-08-23T15:14:24","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Balancing sampling intensity against spatial coverage for a community science monitoring programme","docAbstract":"Small ponds—farm ponds, detention ponds, or impoundments below 0.01 km2—serve important human needs throughout most large river basins. Yet the role of small ponds in regional nutrient and sediment budgets is essentially unknown, currently making it impossible to evaluate their management potential to achieve water quality objectives. Here we used new hydrography data sets and found that small ponds, depending on their spatial position within both their local catchments and the larger river network, can dominate the retention of nitrogen, phosphorus, and sediment compared to rivers, lakes, and reservoirs. Over 300,000 small ponds are collectively responsible for 34%, 69%, and 12% of the mean annual retention of nitrogen, phosphorus, and sediment in the Northeastern United States, respectively, with a dominant influence in headwater catchments (54%, 85%, and 50%, respectively). Small ponds play a critical role among the many aquatic features in long‐term nutrient and sediment loading to downstream waters.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL083937","usgsCitation":"Schmadel, N., Harvey, J., Schwarz, G., Alexander, R., Gomez-Velez, J., Scott, D., and Ator, S., 2019, Small ponds in headwater catchments are a dominant influence on regional nutrient and sediment budgets: Geophysical Research Letters, v. 46, no. 16, p. 9669-9677, https://doi.org/10.1029/2019GL083937.","productDescription":"9 p.","startPage":"9669","endPage":"9677","ipdsId":"IP-109711","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},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":467351,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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M. 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":771266,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":771265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":219106,"corporation":false,"usgs":false,"family":"Schwarz","given":"Gregory E.","email":"gschwarz@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":false,"id":771267,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alexander, Richard 0000-0001-9166-0626","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":219107,"corporation":false,"usgs":true,"family":"Alexander","given":"Richard","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":771268,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gomez-Velez, Jesus D.","contributorId":219103,"corporation":false,"usgs":false,"family":"Gomez-Velez","given":"Jesus D.","affiliations":[{"id":39962,"text":"Department of Earth & Environmental Science, New Mexico Institute of Mining and Technology, Socorro, New Mexico, USA","active":true,"usgs":false}],"preferred":false,"id":771269,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, Durelle","contributorId":219088,"corporation":false,"usgs":false,"family":"Scott","given":"Durelle","affiliations":[{"id":39959,"text":"Virginia Tech.","active":true,"usgs":false}],"preferred":false,"id":771270,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ator, Scott W. 0000-0002-9186-4837","orcid":"https://orcid.org/0000-0002-9186-4837","contributorId":210852,"corporation":false,"usgs":true,"family":"Ator","given":"Scott W.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":771271,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70204932,"text":"70204932 - 2019 - Local, temporal trajectories explain population-level responses to climate change in saguaro (Carnegiea gigantea)","interactions":[],"lastModifiedDate":"2019-08-26T09:28:44","indexId":"70204932","displayToPublicDate":"2019-08-21T10:51:04","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Local, temporal trajectories explain population-level responses to climate change in saguaro (Carnegiea gigantea)","title":"Local, temporal trajectories explain population-level responses to climate change in saguaro (Carnegiea gigantea)","docAbstract":"Population demography is typically assumed to be strongly influenced by climatic factors, particularly with succulent plants and cacti. The saguaro cactus (Carnegiea gigantea) is a long‐lived columnar cactus of the Sonoran Desert that experiences episodic recruitment and mortality. Previous studies have attributed long‐term changes in saguaro populations to climatic factors, including increased germination and establishment during wet periods and mortality and reduced establishment during droughts and extreme freezes. We used a 48‐yr data set of marked individuals at the Desert Laboratory in Tucson, Arizona, to test the hypothesis that local, temporal population trajectories are mediated by topographic heterogeneity that interacts with fluctuating climatic conditions. We tested the influence of local slope and aspect vs. climatic variability on a population of saguaro using >5800 marked individuals that have been measured since 1964. We examined the relationship between demography and climatic variables (drought, precipitation, and extreme temperatures) and found significant differences in growth and survival among aspects and among census periods. Saguaro population growth was higher during wet and cool periods (e.g., 1964–1970), and changes in age structures suggest that topographic differences interact with climatic fluctuations to produce unexpected demographic patterns including large recruitment events during periods of relatively unfavorable climate conditions. Our results highlight the importance of long‐term data to detect demographic responses to climate that could not be predicted from short‐term studies of plant physiology and population demography.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.2844","usgsCitation":"Rodriguez-Buritica, S., Winkler, D.E., Webb, R., and Venable, L., 2019, Local, temporal trajectories explain population-level responses to climate change in saguaro (Carnegiea gigantea): Ecosphere, v. 10, no. 8, e02844, 17 p., https://doi.org/10.1002/ecs2.2844.","productDescription":"e02844, 17 p.","ipdsId":"IP-104411","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":467353,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecs2.2844","text":"External Repository"},{"id":366850,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"10","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Rodriguez-Buritica, Susana","contributorId":218389,"corporation":false,"usgs":false,"family":"Rodriguez-Buritica","given":"Susana","email":"","affiliations":[{"id":39838,"text":"Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85719","active":true,"usgs":false}],"preferred":false,"id":769148,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":769147,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Webb, Robert H. rhwebb@usgs.gov","contributorId":1573,"corporation":false,"usgs":false,"family":"Webb","given":"Robert H.","email":"rhwebb@usgs.gov","affiliations":[{"id":12625,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85721, USA","active":true,"usgs":false}],"preferred":false,"id":769149,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Venable, Lawrence","contributorId":218390,"corporation":false,"usgs":false,"family":"Venable","given":"Lawrence","email":"","affiliations":[{"id":39838,"text":"Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85719","active":true,"usgs":false}],"preferred":false,"id":769150,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204559,"text":"ds1115 - 2019 - Catalog of earthquake parameters and description of seismograph and infrasound stations at Alaskan volcanoes—January 1, 2013, through December 31, 2017","interactions":[],"lastModifiedDate":"2019-08-21T15:23:24","indexId":"ds1115","displayToPublicDate":"2019-08-21T09:34:53","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1115","displayTitle":"Catalog of Earthquake Parameters and Description of Seismograph and Infrasound Stations at Alaskan Volcanoes—January 1, 2013, through December 31, 2017","title":"Catalog of earthquake parameters and description of seismograph and infrasound stations at Alaskan volcanoes—January 1, 2013, through December 31, 2017","docAbstract":"Director,
Volcano Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
This publication consists of two map sheets that display shallow geologic structure, along with sediment distribution and thickness, for an approximately 150-km-long offshore section of the northern California coast between Punta Gorda and Point Arena. Each map sheet includes three maps at scales of either 1:100,000 or 1:200,000, and together the sheets include 30 figures that contain representative high-resolution seismic-reflection profiles. The maps and seismic-reflection surveys cover most of the continental shelf in this region. In addition, the maps show the locations of the shelf break and the 3-nautical-mile limit of California’s State Waters.
The seismic-reflection data, which are the primary dataset used to develop the maps, were collected to support the California Seafloor Mapping Program, U.S. Geological Survey Offshore Geologic Hazards projects, and National Oceanic and Atmospheric Administration’s (NOAA’s) Ocean Explorer program. In addition to the two map sheets, this publication includes geographic information system data files of faults, sediment thicknesses, and depths-to-base of sediment
The map area includes the northernmost section of the right-lateral San Andreas Fault, which extends offshore from Point Arena in the south to Point Delgada in the north. The San Andreas Fault is the primary structure in the widely distributed plate boundary between the Pacific Plate and the Sierra Nevada–Great Valley Microplate, with estimates of cumulative right slip of as much as up to 450 km. South of Point Delgada, fault-related transtension has resulted in development of the Noyo Basin. North of Point Delgada, the San Andreas Fault transitions into a complex contractional zone in and (or) south of the King Range, including a possible nearshore fault that may connect with the Mattole Canyon Fault.
Quaternary sediments and bedrock underlie the shelf. On the seismic-reflection profiles, we digitally traced the thickness and depth of the uppermost seismic-stratigraphic unit, which is a focus of this publication. The upper contact of this unit is the seafloor; the lower contact is a transgressive surface of erosion, a commonly angular, wave-cut unconformity characterized by an upward change to lower amplitude, more diffuse reflections. On the basis of this lower contact, this stratigraphic unit is inferred to have been deposited on the shelf in the last about 21,000 years during the sea-level rise that followed the last major lowstand and the Last Glacial Maximum (LGM). Maps in this publication show both the thickness of this upper sediment unit and the depth to the base of the sediment unit. Within the map region, five different “domains” of post-LGM shelf sediment are delineated on the basis of sediment thickness and coastal geomorphology. Maximum sediment thickness (as much as 67 m) is found in the northern part of the region, along the steep south flank of the King Range. Minimum sediment thickness (areas of exposed bedrock) is found on fault-bounded uplifts, which include Tolo bank and Punta Gorda bank. Mean sediment thickness for the entire shelf in the map area between Punta Gorda and Point Arena is 8.9 m, and total sediment volume is 12,824×106 m3.
Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
The U.S. Army Corps of Engineers, Jacksonville District, plans to deepen the St. Johns River channel in Jacksonville, Florida, from 40 to 47 feet along 13 miles of the river channel, beginning at the mouth of the river at the Atlantic Ocean, to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, (1) installed continuous data collection stations to monitor discharge, salinity, and associated parameters at 23 sites prior to the commencement of dredging and (2) monitored stage and discharge at 13 sites and water temperature, specific conductance, and salinity at 16 sites; all parameters were monitored at some sites.
This is the second annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening and contains information pertinent to the data collection sites during the 2017 water year, from October 2016 to September 2017. One data collection site on the St. Johns River below Shands Bridge was added to the network during this timeframe after the previously monitored location was damaged by Hurricane Matthew.
Discharge and salinity varied widely during the data collection period, reflecting the effects of Hurricane Matthew in October 2016 and Hurricane Irma in September 2017. The annual mean discharge at Trout River was greatest among the tributaries, followed by annual mean discharges at Durbin Creek, Ortega River, Julington Creek, Pottsburg Creek, Clapboard Creek, Cedar River, Broward River, and Dunn Creek. Among the tributary sites, annual mean salinity was highest at the site closest to the Atlantic Ocean, Clapboard Creek, and lowest at the site farthest from the ocean, Durbin Creek. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which is expected. Relative to salinity for the 2016 water year, annual mean salinity in the tributaries was higher for the 2017 water year at four monitoring locations, lower at four monitoring locations, and the same at one location. Of the three sites where salinity was calculated on the main stem in the 2016 water year, salinity was higher at one monitoring location in the 2017 water year and lower at two locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191078","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2019, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2017: U.S. Geological Survey Open-File Report 2019–1078, 35 p., https://doi.org/10.3133/ofr20191078.","productDescription":"viii, 35 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-106628","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":366770,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1078/ofr20191078.pdf","text":"Report","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1078"},{"id":366769,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1078/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Lower St Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.1502685546875,\n 29.156958511360703\n ],\n [\n -81.2109375,\n 29.156958511360703\n ],\n [\n -81.2109375,\n 30.500750980290693\n ],\n [\n -82.1502685546875,\n 30.500750980290693\n ],\n [\n -82.1502685546875,\n 29.156958511360703\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559