Drylands make up roughly 40% of the Earth's land surface, and billions of people depend on services provided by these critically important ecosystems. Despite their relatively sparse vegetation, dryland ecosystems are structurally and functionally diverse, and emerging evidence suggests that these ecosystems play a dominant role in the trend and variability of the terrestrial carbon sink. More, drylands are highly sensitive to climate and are likely to have large, non-linear responses to hydroclimatic change. Monitoring the spatiotemporal dynamics of dryland ecosystem structure (e.g., leaf area index) and function (e.g., primary production and evapotranspiration) is therefore a high research priority. Yet, dryland remote sensing is defined by unique challenges not typically encountered in mesic or humid regions. Major challenges include low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils (i.e., biological soil crusts), high spatial heterogeneity from plot to regional scales, and irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought. Additionally, there is a relative paucity of continuous, long-term measurements in drylands, which impedes robust calibration and evaluation of remotely-sensed dryland data products. Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly constrained estimates of dryland ecosystem structural and functional dynamics. Here, we review past achievements and current progress in remote sensing of dryland ecosystems, including a detailed discussion of the major challenges associated with remote sensing of key dryland structural and functional dynamics. We then identify strategies aimed at leveraging new and emerging opportunities in remote sensing to overcome previous challenges and more accurately contextualize drylands within the broader Earth system. Specifically, we recommend: 1) Exploring novel combinations of sensors and techniques (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) across a range of spatiotemporal scales to gain new insights into dryland structural and functional dynamics; 2) utilizing near-continuous observations from new-and-improved geostationary satellites to capture the rapid responses of dryland ecosystems to diurnal variation in water stress; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; 4) developing algorithms that are specifically tuned to dryland ecosystems by utilizing expanded ground observational network data; and 5) coupling remote sensing observations with process-based models using data assimilation to improve mechanistic understanding of dryland ecosystem dynamics and to better constrain ecological forecasts and long-term projections.
Estimates of the magnitudes of annual peak streamflows with annual exceedance probabilities of 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, and 0.002 (equivalent to recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years, respectively) were computed for 391 streamgages in Ohio and adjacent states based on data collected through the 2015 water year. The flood-frequency estimates were computed following guidance outlined in Bulletin 17C, developed by the Advisory Committee on Water Information. The Bulletin 17C guidelines retain the basic statistical framework of the superseded Bulletin 17B guidelines; however, the Bulletin 17C guidelines add several enhancements including an improved method of moments approach for fitting the log-Pearson Type III (LPIII) distribution to the flood peaks (called the expected moments algorithm), a generalization of the Grubbs Beck low-outlier test (called the Multiple Grubbs Beck test) that permits identification of multiple potentially influential low floods, and new methods for estimating regional skew and uncertainty.
Equations for estimating flood-frequency characteristics at ungaged sites on rural, unregulated streams in Ohio were developed with a two-step process involving ordinary least-squares and generalized least-squares regression techniques. Data from 333 streamgages with 10 or more years of unregulated record were screened for redundancy and a regression dataset was selected that was composed of flood-frequency and basin-characteristic data for 275 streamgages in Ohio and adjacent states. Two sets of equations were developed—one set, referred to as the “simple model,” uses regression region and drainage area as regressor variables, and a second set, referred to as the “full model,” uses regression region, drainage area, main-channel slope, and the percentage of the watershed covered by water and wetlands as regressor variables.
The average standard errors of prediction ranged from about 40.5 to 46.5 percent for the simple-model equations and from about 37.2 to 40.3 percent for the full-model equations. For sites meeting the rural, unregulated criteria, flood-frequency estimates determined by means of LPIII analyses are reported along with weighted flood-frequency estimates, computed as a function of the LPIII estimates and the regression estimates. For sites with homogenous periods of regulation, flood-frequency estimates determined by means of LPIII analyses are reported. Ninety-five percent confidence limits are reported for all estimates.
Values of regressor variables were determined from digital spatial datasets by means of a geographic information system (GIS). The GIS datasets and the new full-model equations have been incorporated into Ohio’s StreamStats application, a web-based, GIS-backed system designed to facilitate the estimation of streamflow statistics at ungaged locations on streams.
Seasonal patterns in peak flows were assessed for 295 streamgages in Ohio. Annual peak flows occurred most frequently between January and April, with March having the highest frequency of occurrence. The month with the fewest number of annual peaks was October. Peak-of-record flows occurred most frequently in March, followed by January (months in which two of Ohio’s most severe widespread floods in recent history occurred). None of the peak-of-record flows occurred in October and only two occurred in November.
Temporal trend in annual peak flows were assessed for 133 streamgages on unregulated streams in Ohio with 30 or more years of systematic record. Trends were assessed by computing the rank correlation (as measured with the two-sided Kendall’s tau statistic) between time and annual peak flows. Weak but statistically significant trends were indicated at 15 of the 133 streamgages. Of the 15 streamgages with significant trend in annual peak flows, 12 had an upward trend (positive tau) and 3 had a downward trend (negative tau). All 12 streamgages with positive tau values were at latitudes north of 40°33', and streamgages with negative tau values were at latitudes south of 40°33'.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195018","collaboration":"Prepared in cooperation with the Ohio Department of Transportation","usgsCitation":"Koltun, G.F., 2019, Flood-frequency estimates for Ohio streamgages based on data through water year 2015 and techniques for estimating flood-frequency characteristics of rural, unregulated Ohio streams: U.S. Geological Survey Scientific Investigations Report 2019–5018, 25 p., https://doi.org/10.3133/sir20195018.","productDescription":"Report: vi, 25 p.; 2 Tables; Appendices 1.1-1.8; Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-100946","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368118,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5018/sir20195018.pdf","text":"Report ","size":"6.45 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\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard Ste 100
Columbus, OH 43229–1737
Digital flood-inundation maps for a 4-mile reach of Nimishillen Creek near North Industry, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the Muskingum Watershed Conservancy District, Ohio, and the Stark County Board of Commissioners. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping (FIM) Program website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on Nimishillen Creek at North Industry, Ohio (station number 03118500). Near-real-time stages at this streamgage can be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site.
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated to the current stage-discharge relation at the streamgage on Nimishillen Creek at North Industry and documented high-water marks from the flood of January 12, 2017.
The hydraulic model was then used to compute seven water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 8 to 14 ft, which is from “action stage” to above “major flood stage” as reported by the National Weather Service. The simulated water-surface profiles were then used in combination with a geographic information system (GIS) digital elevation model derived from light detection and ranging data to delineate the areas flooded at each water level.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts. Forecasts for the USGS streamgage on Nimishillen Creek at North Industry, Ohio are issued as needed during times of high water, but are not routinely available (National Weather Service, 2017).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195083","collaboration":"Prepared in cooperation with the Muskingum Watershed Conservancy District, Ohio, and the Stark County Board of Commissioners","usgsCitation":"Whitehead, M.T., 2019, Flood-inundation maps for Nimishillen Creek near North Industry, Ohio, 2019: U.S. Geological Survey Scientific Investigations Report 2019–5083, 11 p., https://doi.org/10.3133/sir20195083.\n","productDescription":"Report: vi, 11 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-104812","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368076,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WFOVN2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets and hydraulic model for flood-inundation maps of Nimishillen Creek near North Industry, Ohio:"},{"id":368075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5083/sir20195083.pdf","text":"Report","size":"14.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5083"},{"id":368074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5083/coverthb.jpg"}],"country":"United States","state":"Ohio","county":"Stark County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.0864,40.9879],[-81.0865,40.9839],[-81.0866,40.978],[-81.0869,40.9013],[-81.0873,40.728],[-81.0922,40.7285],[-81.1001,40.7281],[-81.1989,40.7292],[-81.1991,40.7224],[-81.2373,40.7237],[-81.241,40.6507],[-81.2755,40.651],[-81.2791,40.6511],[-81.304,40.6518],[-81.3173,40.6519],[-81.4372,40.6529],[-81.4365,40.6584],[-81.4395,40.6625],[-81.4467,40.6657],[-81.4589,40.6654],[-81.4675,40.6555],[-81.6489,40.6346],[-81.6491,40.6681],[-81.6483,40.7371],[-81.648,40.9145],[-81.4201,40.9064],[-81.4164,40.9889],[-81.3932,40.9887],[-81.1059,40.9882],[-81.0925,40.988],[-81.0864,40.9879]]]},\"properties\":{\"name\":\"Stark\",\"state\":\"OH\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard
Columbus OH 43229–1753
Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Threats to our recreational and drinking waters include disease-causing (pathogenic) organisms from fecal contamination and toxins produced by some species of cyanobacteria (cyanotoxins) that can cause acute and (or) chronic illnesses. Because traditional laboratory methods for detecting these threats take too long for prompt public health protection, tools for real-time assessments are needed to protect public health. To address this need, the U.S. Geological Survey is collaborating with State and local partners to develop models that provide real-time estimates of Escherichia coli (E. coli) (for pathogens) and (or) microcystin (for freshwater cyanotoxins) levels at inland and Great Lakes beaches and drinking-water intakes. Model results are then used to inform the public of water-quality conditions in near-real time through the Great Lakes NowCast (https://ny.water.usgs.gov/maps/nowcast/). Behind the scenes, the NowCast provides speed and efficiency for managers by automating data management and standardizing methods among agencies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193061","collaboration":"Prepared in cooperation with U.S. Environmental Protection Agency, Great Lakes Restoration Initiative","usgsCitation":"Francy, D.S., Brady, A.M., and Zimmerman, T.M., 2019, Real-time assessments of water quality—A nowcast for Escherichia coli and cyanobacterial toxins: U.S. Geological Survey Fact Sheet 2019–3061, 4 p., https://doi.org/10.3133/fs20193061.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-111133","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368188,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3061/coverthb.jpg"},{"id":368189,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3061/fs20193061.pdf","text":"Report","size":"970 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3061"}],"contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard, Suite 100
Columbus, OH 43229
In 2007 the U.S. Geological Survey (USGS) completed an assessment of undiscovered, technically recoverable oil and gas resources in the East Greenland Rift Basins Province of Northeast Greenland. The province was selected as the prototype for the U.S. Geological Survey Circum-Arctic Resource Appraisal (CARA). In collaboration with the Geological Survey of Denmark and Greenland (GEUS), the province was subdivided into nine geologically distinctive areas. Seven of these were defined as Assessment Units (AUs), of which five were quantitatively assessed. These are: North Danmarkshavn Salt Basin, South Danmarkshavn Basin, Thetis Basin, Northeast Greenland Volcanic Province, and Liverpool Land Basin. Jameson Land Basin and the Jameson Land Basin Subvolcanic Extension were defined as AUs but were not quantitatively assessed.
Onshore studies by GEUS and other organizations suggest that at least four stratigraphic intervals may contain potential source rocks for petroleum. The geological history of related areas in western Norway and burial history modeling suggest that Upper Jurassic strata are most likely to contain petroleum source rocks. A wide variety of possible trapping mechanisms are expected within the province. Potential traps in the North Danmarkshavn Salt Basin AU are dominated by structures formed through salt tectonics; those in the South Danmarkshavn Basin and the Northeast Greenland Volcanic Province are characterized by extensional structures and by stratigraphic traps in submarine fan complexes. Prospective inversion structures of Tertiary age are present along the western margin of South Danmarkshavn Basin AU, and the large horst block structures that separate the Danmarkshavn and Thetis Basins may provide numerous opportunities for traps in fault blocks and along various facies-related permeability barriers. Possible reservoirs include shallow marine to nonmarine sandstones of Middle Jurassic age, sandstones in Upper Jurassic synrift deposits, Cretaceous sandstones in submarine fan complexes, sandstones in Paleogene progradational sequences, and in Upper Carboniferous to Lower Permian warm-water carbonate sequences, especially in northern Danmarkshavn Basin. Marine shales are expected to provide the main sealing lithologies in most AUs.
Most of the undiscovered oil, gas, and natural gas liquids are likely to be in the offshore areas of the province and are inferred to belong to an Upper Jurassic Composite Total Petroleum System. The USGS estimated that the East Greenland Rift Basins Province contains approximately (mean) 31,400 million barrels oil equivalent (MMBOE) of oil, natural gas, and natural gas liquids. Of the five assessed AUs, North Danmarkshavn Salt Basin and the South Danmarkshavn Basin are estimated to contain most of the undiscovered petroleum.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1824K","usgsCitation":"Gautier, D.L., 2019, Geology and assessment of undiscovered oil and gas resources of the East Greenland Rift Basins Province, 2008, chap. K of Moore, T.E., and Gautier, D.L., eds., The 2008 Circum-Arctic Resource Appraisal: U.S. Geological Survey Professional Paper 1824, 20 p., https://doi.org/10.3133/pp1824K.","productDescription":"Report: vii, 20 p.; Appendices 1-7","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-051000","costCenters":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":368214,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K"},{"id":368213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1824/k/coverthb.jpg"},{"id":368223,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appendixes.zip","text":"Appendixes","size":"150 KB","linkFileType":{"id":6,"text":"zip"},"description":"Professional Paper 1824 K"},{"id":368222,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx7.pdf","text":"Appendix 7","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Jameson Land Basin Subvolcanic Extension Assessment Unit"},{"id":368221,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx6.pdf","text":"Appendix 6","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Jameson Land Basin Assessment Unit"},{"id":368220,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx5.pdf","text":"Appendix 5","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Liverpool Land Basin Assessment Unit"},{"id":368218,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx3.pdf","text":"Appendix 3","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Northeast Greenland Volcanic Province Assessment Unit"},{"id":368217,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx2.pdf","text":"Appendix 2","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the South Danmarkshavn Basin Assessment Unit"},{"id":368216,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx1.pdf","text":"Appendix 1","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the North Danmarkshavn Salt Basin Assessment Unit"},{"id":368219,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx4.pdf","text":"Appendix 4","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Thetis Basin Assessment Unit"}],"country":"Greenland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -21.181640624999996,\n 70.11048478105927\n ],\n [\n -31.9921875,\n 69.1312712296365\n ],\n [\n -43.505859375,\n 66.65297740055279\n ],\n [\n -46.7578125,\n 62.103882522897855\n ],\n [\n -43.33007812499999,\n 57.70414723434193\n ],\n [\n -36.03515625,\n 60.973107109199404\n ],\n [\n -21.357421875,\n 68.33437594128185\n ],\n [\n -21.181640624999996,\n 70.11048478105927\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information,
Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
Temporary wetlands are characterized by frequent drying resulting in a unique, highly specialized assemblage of often rare or specialized plant and animal species. They are found on all continents and in a variety of landscape settings. Although accurate estimates of the abundance of temporary wetlands are available in only a few countries, global estimations identify a decline in number and quality. The key environmental factors driving the structure of ecological communities in temporary wetlands are the duration, timing, frequency and predictability of the aquatic and dry phases, which varies greatly with region and hydrogeomorphic setting. Temporary wetlands have been historically neglected, but improved social awareness of the functions and values of, and increases in scientific interest, suggest that this is changing. They play an ecological role in both global cycles (i.e., CO2 emissions) and biodiversity (in proportion to their size, they contribute disproportionately to regional and global biodiversity). Moreover, they provide valuable ecosystem services including wildlife habitat, nutrient flux to adjacent ecosystems, flood control, water filtration, and cultural services. Effective conservation of temporary wetlands requires addressing threats (i.e., inconsistent and inadequate regulatory protections; climate change; changes in land use) and management challenges (i.e., management at both local and landscape scales; incomplete understanding of the ecosystem services provided by them; the need to enhance inventories). The most suitable approaches for conserving temporary wetlands include (1) regulations or other forms of protection; (2) sustainable management; (3) restoration and creation; and (4) collaborative conservation.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference Module in Earth Systems and Environmental Sciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-409548-9.12003-2","usgsCitation":"Boix, D., Calhoun, A.J., Mushet, D.M., Bell, K.P., Fitzsimons, J.A., and Isselin-Nondedeu, F., 2019, Conservation of temporary wetlands, chap. of Reference Module in Earth Systems and Environmental Sciences, https://doi.org/10.1016/B978-0-12-409548-9.12003-2.","ipdsId":"IP-109438","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":502628,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/chapter/Conservation_of_Temporary_Wetlands/20674152","text":"External Repository"},{"id":369820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boix, Dani","contributorId":177733,"corporation":false,"usgs":false,"family":"Boix","given":"Dani","affiliations":[],"preferred":false,"id":774855,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Calhoun, Aram J.K.","contributorId":177732,"corporation":false,"usgs":false,"family":"Calhoun","given":"Aram","email":"","middleInitial":"J.K.","affiliations":[{"id":13065,"text":"Department of Wildlife, Fisheries, and Conservation Biology, University of Maine","active":true,"usgs":false}],"preferred":false,"id":774856,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":774854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bell, Kathleen P.","contributorId":171584,"corporation":false,"usgs":false,"family":"Bell","given":"Kathleen","email":"","middleInitial":"P.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":774857,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fitzsimons, James A.","contributorId":177734,"corporation":false,"usgs":false,"family":"Fitzsimons","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":774858,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isselin-Nondedeu, Francis","contributorId":177735,"corporation":false,"usgs":false,"family":"Isselin-Nondedeu","given":"Francis","email":"","affiliations":[],"preferred":false,"id":774859,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70207517,"text":"70207517 - 2019 - Modeling control of Common Carp (Cyprinus carpio) in a shallow lake–wetland system","interactions":[],"lastModifiedDate":"2019-12-21T10:37:06","indexId":"70207517","displayToPublicDate":"2019-10-09T10:35:06","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Modeling control of Common Carp (Cyprinus carpio) in a shallow lake–wetland system","docAbstract":"The introduction of Common Carp (Cyprinus carpio) into North American waterways has led to widespread alteration of aquatic ecosystems. Control of this invader has proven extremely difficult due to its capacity for rapid population growth. To help understand how Common Carp can potentially be controlled we developed a population dynamics model (CarpMOD) to explore the efficacy of active and passive control measures that impose mortality on multiple life stages (embryos, juveniles and adults). We applied CarpMOD to Common Carp in Malheur Lake, a large shallow lake in Southeast Oregon, USA. Simulated control measures included commercial harvest of adults, trapping of juveniles, embryo electroshocking, and passive removal imposed via avian predation. Results from CarpMOD suggest that no single active removal method would decrease Common Carp biomass below the targeted 50 kg/ha threshold. Combinations of two or all three active removal methods could, however, reduce biomass below the desired threshold due to cumulative mortality on multiple life stages. CarpMOD simulations suggest that the level of carp removal necessary to reach the desired biomass threshold is approximately 40% at each life-stage, which may be unrealistic to maintain over longer time scales. Passive removal via avian predation may also contribute to suppression of Common Carp, but was not sufficient in isolation to reduce biomass below the desired threshold. Collectively, our results indicate control of Common Carp as a sole means of ecosystem restoration is unlikely to be effective in the system we modeled. This suggests additional means of restoration may be warranted, perhaps in combination with control of Common Carp, or development of more effective control measures.","language":"English","publisher":"Springer","doi":"10.1007/s11273-019-09685-0","usgsCitation":"Pearson, J.B., Dunham, J.B., Bellmore, J., and Lyons, D.E., 2019, Modeling control of Common Carp (Cyprinus carpio) in a shallow lake–wetland system: Wetlands Ecology and Management, v. 27, no. 5-6, p. 663-682, https://doi.org/10.1007/s11273-019-09685-0.","productDescription":"20 p.","startPage":"663","endPage":"682","ipdsId":"IP-106782","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":370603,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Malheur Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.35272216796874,\n 43.135065496929165\n ],\n [\n -118.51776123046875,\n 43.135065496929165\n ],\n [\n -118.51776123046875,\n 43.50274467820439\n ],\n [\n -119.35272216796874,\n 43.50274467820439\n ],\n [\n -119.35272216796874,\n 43.135065496929165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"5-6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Pearson, James B","contributorId":221480,"corporation":false,"usgs":false,"family":"Pearson","given":"James","email":"","middleInitial":"B","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":778338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":778339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bellmore, J Ryan","contributorId":178561,"corporation":false,"usgs":false,"family":"Bellmore","given":"J Ryan","affiliations":[],"preferred":false,"id":778340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyons, Donald E.","contributorId":204663,"corporation":false,"usgs":false,"family":"Lyons","given":"Donald","email":"","middleInitial":"E.","affiliations":[{"id":13016,"text":"Department of Fisheries and Wildlife, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":778341,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215328,"text":"70215328 - 2019 - Foraging ecology mediates response to ecological mismatch during migratory stopover","interactions":[],"lastModifiedDate":"2020-10-16T14:09:17.600138","indexId":"70215328","displayToPublicDate":"2019-10-09T09:04:15","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}},"title":"Foraging ecology mediates response to ecological mismatch during migratory stopover","docAbstract":"Impacts of ecological mismatches should be most pronounced at points of the annual cycle when populations depend on a predictable, abundant, and aggregated food resource that changes in timing or distribution. The degree to which species specialize on a key prey item, therefore, should determine their sensitivity to mismatches. We evaluated the hypothesis that the effects of ecological mismatch during migratory stopover are mediated by a species’ foraging ecology by comparing two similar long‐distance migratory species that differ in their foraging strategies during stopover. We predicted that a specialist foraging strategy would make species more sensitive to effects of mismatch with a historically abundant prey, while an active, generalist foraging strategy should help buffer against changing local conditions. We estimated arrival times, start of mass gain, and rate of mass gain during spring stopover in Delaware Bay, USA. At this site, shorebirds feed on a temporally aggregated food resource (horseshoe crab Limulus polyphemus eggs), the timing of which is linked to water temperature; red knot (Calidris canutus rufa) specializes on these while the ruddy turnstone (Arenaria interpres) feeds more generally. We used a hierarchical nonlinear model to estimate the effect of mismatch between shorebird arrivals and timing of crab spawning on the timing and rate of mass gain over 22 yr. In years with cooler water temperature, crabs spawned later, which was associated with later and faster mass gain for the knots. Turnstones exhibited less inter‐annual variation in the timing and rate of mass gain than knots, and we found no relationship between mass gain dynamics and the availability of horseshoe crab eggs for this generalist species. Long‐distance migrants rely on predictable resources en route and even when these linkages are simple and predictable, populations can be vulnerable to change; these results suggest that generalist foraging strategies may buffer migratory species against phenological mismatch. We provide a framework to evaluate population responses to changes in prey phenology at sites vulnerable to climatic change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2898","usgsCitation":"Tucker, A., McGowan, C.P., Catalano, M., Derose-Wilson, A., Robinson, R., and Zimmerman, J., 2019, Foraging ecology mediates response to ecological mismatch during migratory stopover: Ecosphere, v. 10, no. 10, e02898, 17 p., https://doi.org/10.1002/ecs2.2898.","productDescription":"e02898, 17 p.","ipdsId":"IP-104145","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":459585,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2898","text":"Publisher Index Page"},{"id":379464,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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The models produced plant-level withdrawal and consumption estimates using consistent methods for 1,122 water-using, utility-scale thermoelectric power plants in the United States for 2015. Total estimated withdrawal for 2015 was about 103 billion gallons per day (Bgal/d), and total estimated consumption was about 2.7 Bgal/d. Model-estimated withdrawals decreased approximately 26 Bgal/d, or 20 percent, since 2010, and consumption decreased approximately 734 million gallons per day, or 21 percent. The decrease in thermoelectric water use between 2010 and 2015 can be attributed in part to a 7-percent decrease in total thermoelectric utility-scale electricity production, a combination of decreased electricity production and closure of coal-fired plants with once-through cooling systems, and the increase of electricity production at natural gas combined-cycle plants, which are more energy- and water-efficient than conventional thermoelectric plants.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195103","collaboration":"U.S. Geological Survey Water Availability and Use Science Program","usgsCitation":"Harris, M.A., and Diehl, T.H., 2019, Withdrawal and consumption of water by thermoelectric power plants in the United States, 2015: U.S. Geological Survey Scientific Investigations Report 2019–5103, 15 p., https://doi.org/10.3133/sir20195103.","productDescription":"Report: iv, 15 p.; Data 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States\"}}]}","contact":"U.S. Geological Survey
National Water Use Science Project Team
The key to Eastern Meadowlark (Sturnella magna) management is providing large areas of contiguous grassland of moderate height with significant grass cover and moderate forb density. Eastern Meadowlarks have been reported to use habitats with 10–187 centimeters (cm) average vegetation height, 6–88 cm visual obstruction reading, 53–86 percent grass cover, 4–50 percent forb cover, less than or equal to (≤) 4 percent shrub cover, less than 38 percent bare ground, 6–23 percent litter cover, and ≤13 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842MM","usgsCitation":"Hull, S.D., Shaffer, J.A., and Igl, L.D., 2019, The effects of management practices on grassland birds—Eastern Meadowlark (Sturnella magna), chap. MM of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 26 p., https://doi.org/10.3133/pp1842MM.","productDescription":"iv, 26 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":367879,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/mm/coverthb.jpg"},{"id":367880,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/mm/pp1842mm.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–MM"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
In 2015, the U.S. Geological Survey, in cooperation with the Iroquois River Conservancy District, deployed continuous water-quality monitors and began collecting representative discrete water-quality samples at the Iroquois River near Foresman, Indiana, streamflow-gaging station (U.S. Geological Survey station 05524500). By relating continuously monitored water-quality data and discrete water-quality samples collected from April 2015 through July 2018, regression models that estimate concentrations of suspended sediment, total nitrogen, and total phosphorus were developed. Developed regression models indicated a strong correlation between turbidity and streamflow with suspended-sediment concentration (adjusted coefficient of determination equals 0.84, predicted residual error sum of squares equals 0.493), nitrate plus nitrite and streamflow with total nitrogen (adjusted coefficient of determination equals 0.99, predicted residual error sum of squares equals 0.0202), and specific conductance and turbidity with total phosphorus (adjusted coefficient of determination equals 0.84, predicted residual error sum of squares equals 0.0935).
Daily loads of suspended sediment, total nitrogen, and total phosphorus were computed as the product of daily mean regression model concentrations and daily mean streamflow. During periods when regression model concentrations could not be computed, rloadest models, the R programming language version of the LOADEST FORTRAN program, were used to compute daily loads of each constituent. For 2016 and 2017, the estimated annual suspended-sediment loads were 25,000 and 32,100 tons; estimated total nitrogen loads were 4,260 and 5,780 tons; and estimated total phosphorus loads were 104 and 128 tons, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195087","collaboration":"Prepared in cooperation with the Iroquois River Conservancy District","usgsCitation":"Lathrop, T.R., Bunch, A.R., Downhour, M.S., and Perkins, D.M., 2019, Regression models for estimating sediment and nutrient concentrations and loads at the Iroquois River near Foresman, Indiana, March 2015 through July 2018: U.S. Geological Survey Scientific Investigation Report 2019–5087, 14 p., https://doi.org/10.3133/sir20195087.","productDescription":"Report: vi, 14 p.; Data Releases","numberOfPages":"24","ipdsId":"IP-107470","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368030,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YCAELC","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data and regression models for total nitrogen and total phosphorus for the Iroquois River near Foresman, Indiana, March 20, 2015, to July 19, 2018"},{"id":368029,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RFLONI","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data and regression model for suspended sediment for Iroquois River near Foresman, Indiana, March 20, 2015, to July 19, 2018"},{"id":368028,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91FL2GY","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Suspended sediment, total nitrogen, and total phosphorus loads for Iroquois River near Foresman, Indiana, April 2015 to July 2018"},{"id":368027,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5087/sir20195087.pdf","text":"Report","size":"855 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5087"},{"id":368026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5087/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Newton County","city":"Foresman","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-87.5263,41.1661],[-87.4801,41.1701],[-87.4587,41.1702],[-87.4484,41.1744],[-87.4466,41.174],[-87.4411,41.1731],[-87.4147,41.1619],[-87.4055,41.1625],[-87.4,41.1625],[-87.394,41.1625],[-87.38,41.1726],[-87.3448,41.1824],[-87.3405,41.1824],[-87.3313,41.1829],[-87.3241,41.1862],[-87.2859,41.2154],[-87.2762,41.2187],[-87.2757,41.1733],[-87.2754,41.0866],[-87.275,40.9991],[-87.2768,40.9405],[-87.2759,40.9133],[-87.268,40.9134],[-87.2664,40.8249],[-87.2655,40.7383],[-87.3807,40.738],[-87.4905,40.7381],[-87.5263,40.7378],[-87.5263,40.741],[-87.5265,40.839],[-87.5262,40.981],[-87.5262,40.9832],[-87.5265,41.0142],[-87.5264,41.1231],[-87.5263,41.1661]]]},\"properties\":{\"name\":\"Newton\",\"state\":\"IN\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Understanding how stream fishes respond to changes in habitat availability is complicated by low occurrence rates of many species, which in turn reduces the ability to quantify species–habitat relationships and account for imperfect detection in estimates of species richness. Multispecies occupancy models have been used sparingly in the analysis of fisheries data, but address the aforementioned deficiencies by allowing information to be shared among ecologically similar species, thereby enabling species–habitat relationships to be estimated for entire fish communities, including rare species. Here, we highlight the utility of hierarchical multispecies occupancy models for the analysis of fish community data and demonstrate the modeling framework on a stream fish community dataset collected in the Delaware Water Gap National Recreation Area, USA. In particular, we demonstrate the ability of the modeling framework to make inferences at the species-, guild-, and community-levels, thereby making it a powerful tool for understanding and predicting how environmental variables influence species occupancy probabilities and structure fish assemblages.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0125","collaboration":"National Park Service","usgsCitation":"White, S., Faulk, E., Tzilkowski, C., Weber, A., Marshall, M., and Wagner, T., 2019, Predicting fish species richness and habitat relationships using Bayesian hierarchical multispecies occupancy models: Canadian Journal Fisheries and Aquatic Sciences, v. 77, no. 3, 9 p., https://doi.org/10.1139/cjfas-2019-0125.","productDescription":"9 p.","ipdsId":"IP-101955","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":388400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York New Jersey, Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.12451171875,\n 40.96330795307353\n ],\n [\n -74.15771484375,\n 40.96330795307353\n ],\n [\n -74.15771484375,\n 41.60722821271717\n ],\n [\n -75.12451171875,\n 41.60722821271717\n ],\n [\n -75.12451171875,\n 40.96330795307353\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"77","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Shannon","contributorId":264595,"corporation":false,"usgs":false,"family":"White","given":"Shannon","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":821720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Faulk, Evan","contributorId":264596,"corporation":false,"usgs":false,"family":"Faulk","given":"Evan","email":"","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":821721,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tzilkowski, Caleb","contributorId":264597,"corporation":false,"usgs":false,"family":"Tzilkowski","given":"Caleb","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":821722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weber, Andrew","contributorId":264598,"corporation":false,"usgs":false,"family":"Weber","given":"Andrew","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":821723,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Marshall, Matt","contributorId":264599,"corporation":false,"usgs":false,"family":"Marshall","given":"Matt","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":821724,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":821719,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208122,"text":"70208122 - 2019 - Estimating sightability of greater sage-grouse at leks using an aerial infrared system and N-mixture models","interactions":[],"lastModifiedDate":"2020-01-29T16:24:01","indexId":"70208122","displayToPublicDate":"2019-10-03T13:36:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating sightability of greater sage-grouse at leks using an aerial infrared system and N-mixture models","docAbstract":"Counts of grouse present at leks (breeding grounds) during spring are widely used to monitor population numbers and assess trends. However, only a proportion of birds available to count are detected resulting in a biased population index. We designed a study using an aerial integrated infrared imaging system (AIRIS) and experimental pseudo-leks to quantify sightability (proportion of birds detected) of conventional ground-based visual (GBV) surveys for greater sage-grouse Centrocercus urophasianus. Specifically, we calibrated AIRIS at pseudo-leks composed of known numbers of captively-raised birds, primarily ring-necked pheasant Phasianus colchicus. We then carried out AIRIS and GBV surveys, simultaneously, on nearby sage-grouse leks, allowing us to model AIRIS and GBV sightability. AIRIS detected ∼93% of birds on pseudo-leks while GBV detected ∼86% of sage-grouse on leks. Thus, the ground count observation error was –14% from the ‘true' number of male sage-grouse attending the leks. We also found sagebrush cover decreased sightability for GBV counts but did not influence sightability by AIRIS. Because standard GBV protocols typically make repeated counts of sage-grouse in a single morning, we also modeled repeated GBV counts using N-mixture models and found an 88% sightability, which was nearly the same as GBV sightability from the AIRIS analysis. This suggests that the use of repeated morning counts can potentially account for imperfect detection in the standard GBV surveys currently implemented. We also provide generalized correction values that could be employed by resource managers using either GBV or AIRIS to better estimate ‘true’ numbers of sage-grouse attending leks within similar environments to this study. The findings and interpretation presented can help guide effective monitoring protocols that account for observation error and improve accuracy of data used for population trend and abundance estimation.
","language":"English","publisher":"BioONE","doi":"10.2981/wlb.00552","usgsCitation":"Coates, P.S., Wann, G.T., Gillette, G.L., Ricca, M.A., Prochazka, B.G., Severson, J.P., Andrle, K.M., Espinosa, S.P., Casazza, M.L., and Delehanty, D.J., 2019, Estimating sightability of greater sage-grouse at leks using an aerial infrared system and N-mixture models: Wildlife Biology, v. 2019, no. 1, wlb.00552, 11 p., https://doi.org/10.2981/wlb.00552.","productDescription":"wlb.00552, 11 p.","ipdsId":"IP-100795","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":459615,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2981/wlb.00552","text":"Publisher Index Page"},{"id":371652,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.81640624999999,\n 39.16414104768742\n ],\n [\n -112.08251953125,\n 39.16414104768742\n ],\n [\n -112.08251953125,\n 44.88701247981298\n ],\n [\n -121.81640624999999,\n 44.88701247981298\n ],\n [\n -121.81640624999999,\n 39.16414104768742\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2019","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research 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0000-0002-1754-6689","orcid":"https://orcid.org/0000-0002-1754-6689","contributorId":213469,"corporation":false,"usgs":true,"family":"Severson","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780575,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Andrle, Katie M.","contributorId":221865,"corporation":false,"usgs":false,"family":"Andrle","given":"Katie","email":"","middleInitial":"M.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":780576,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Espinosa, Shawn P.","contributorId":195583,"corporation":false,"usgs":false,"family":"Espinosa","given":"Shawn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":780577,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780578,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Delehanty, David J.","contributorId":195584,"corporation":false,"usgs":false,"family":"Delehanty","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":780579,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70205773,"text":"70205773 - 2019 - Prediction and inference of flow-duration curves using multi-output neural networks","interactions":[],"lastModifiedDate":"2019-10-02T12:09:28","indexId":"70205773","displayToPublicDate":"2019-10-02T12:06:59","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Prediction and inference of flow-duration curves using multi-output neural networks","docAbstract":"We develop multi-output neural network models (MNNs) to predict flow-duration curves (FDCs) in 9,203 ungaged locations in the Southeastern United States for six decades between 1950-2009. The model architecture contains multiple response variables in the output layer that correspond to individual quantiles along the FDC. During training, predictions are made for each quantile, and a combined loss function is used for back propagation and parameter updating. The loss function accounts for the covariance between the quantiles and generates physically consistent outputs (i.e., monotonically increasing quantiles with increasing nonexceedance probabilities). We use neural-network dropout to generate posterior-predictive distributions for FDCs, and test model performance under cross validation. Finally, we demonstrate how local surrgotate models, via the Local Interpretable Model-agnostic Explanations (LIME) method, can be used to infer the relation between basin characteristics and the predicted FDCs. Results suggest that MNNs can learn the monotonic relations between adjacent quantiles on an FDC, they result in better predictions than single output neural-network models that predict each quantile independently, and basin characteristics are most useful for predicting smaller quantiles, whereas bias terms from neighboring quantiles are most informative for predicting higher quantiles.","language":"English","publisher":"Wiley","doi":"10.1029/2018WR024463","usgsCitation":"Worland, S.C., Steinschneider, S., Asquith, W.H., Knight, R., and Wieczorek, M., 2019, Prediction and inference of flow-duration curves using multi-output neural networks: Water Resources Research, v. 55, no. 8, p. 6850-6868, https://doi.org/10.1029/2018WR024463.","productDescription":"19 p.","startPage":"6850","endPage":"6868","onlineOnly":"N","ipdsId":"IP-091599","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":459623,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018wr024463","text":"Publisher Index Page"},{"id":437314,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YGKZZV","text":"USGS data release","linkHelpText":"Estimated quantiles for the pour points of 9,203 level-12 Hydrologic Unit Code in the Southeastern United States--1950--2010"},{"id":367923,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"8","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Worland, Scott C. 0000-0001-6384-2457 scworland@usgs.gov","orcid":"https://orcid.org/0000-0001-6384-2457","contributorId":5802,"corporation":false,"usgs":true,"family":"Worland","given":"Scott","email":"scworland@usgs.gov","middleInitial":"C.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":772278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steinschneider, Scott 0000-0002-8882-1908","orcid":"https://orcid.org/0000-0002-8882-1908","contributorId":206359,"corporation":false,"usgs":false,"family":"Steinschneider","given":"Scott","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":772283,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":772284,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knight, Rodney 0000-0001-9588-0167 rrknight@usgs.gov","orcid":"https://orcid.org/0000-0001-9588-0167","contributorId":152422,"corporation":false,"usgs":true,"family":"Knight","given":"Rodney","email":"rrknight@usgs.gov","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":772285,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wieczorek, Michael E. 0000-0003-0999-5457 mewieczo@usgs.gov","orcid":"https://orcid.org/0000-0003-0999-5457","contributorId":178736,"corporation":false,"usgs":true,"family":"Wieczorek","given":"Michael E.","email":"mewieczo@usgs.gov","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":772286,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205674,"text":"70205674 - 2019 - A multidisciplinary coastal vulnerability assessment for local government focused on ecosystems, Santa Barbara area, California","interactions":[],"lastModifiedDate":"2019-11-13T13:43:35","indexId":"70205674","displayToPublicDate":"2019-10-02T11:26:47","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2926,"text":"Ocean and Coastal Management","active":true,"publicationSubtype":{"id":10}},"title":"A multidisciplinary coastal vulnerability assessment for local government focused on ecosystems, Santa Barbara area, California","docAbstract":"Incorporating coastal ecosystems in climate adaptation planning is needed to maintain the well-being of both natural and human systems. Our vulnerability study uses a multidisciplinary approach to evaluate climate change vulnerability of an urbanized coastal community that could serve as a model approach for communities worldwide, particularly in similar Mediterranean climates. We synthesize projected changes in climate, coastal erosion and flooding, watershed runoff and impacts to two important coastal ecosystems, sandy beaches and coastal salt marshes. Using downscaled climate models along with other regional models, we find that temperature, extreme heat events, and sea level are expected to increase in the future, along with more intense rainfall events, despite a negligible change in annual rainfall. Consequently, more droughts are expected but the magnitude of larger flood events will increase. Associated with the continuing rise of mean sea level, extreme coastal water levels will occur with increasingly greater magnitudes and frequency. Severe flooding will occur for both natural (wetlands, beaches) and built environments (airport, harbor, freeway, and residential areas). Adaptation actions can reduce the impact of rising sea level, which will cause losses of sandy beach zones and salt marsh habitats that support the highest biodiversity in these ecosystems, including regionally rare and endangered species, with substantial impacts occurring by 2050. Providing for inland transgression of coastal habitats, effective sediment management, reduced beach grooming and removal of shoreline armoring are adaptations that would help maintain coastal ecosystems and the beneficial services they provide.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ocecoaman.2019.104921","usgsCitation":"Myers, M., Barnard, P., Beighley, E., Cayan, D., Dugan, J.E., Feng, D., Iacobellis, S.F., Melack, J.M., and Page, H.M., 2019, A multidisciplinary coastal vulnerability assessment for local government focused on ecosystems, Santa Barbara area, California: Ocean and Coastal Management, v. 182, 104921, https://doi.org/10.1016/j.ocecoaman.2019.104921.","productDescription":"104921","ipdsId":"IP-107676","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":459626,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ocecoaman.2019.104921","text":"Publisher Index Page"},{"id":367925,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Santa Barbara","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.48249816894531,\n 34.41370754457088\n ],\n [\n -119.47906494140624,\n 34.4142740103038\n ],\n [\n -119.86564636230467,\n 34.44485746154028\n ],\n [\n -119.8773193359375,\n 34.36384353883067\n ],\n [\n -119.47631835937499,\n 34.36100946506246\n ],\n [\n -119.48249816894531,\n 34.41370754457088\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"182","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Myers, Monique","contributorId":219345,"corporation":false,"usgs":false,"family":"Myers","given":"Monique","email":"","affiliations":[{"id":39996,"text":"California Sea Grant","active":true,"usgs":false}],"preferred":false,"id":772060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":772059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beighley, Edward","contributorId":219346,"corporation":false,"usgs":false,"family":"Beighley","given":"Edward","email":"","affiliations":[{"id":38331,"text":"Northeastern University","active":true,"usgs":false}],"preferred":false,"id":772061,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cayan, Daniel R.","contributorId":219347,"corporation":false,"usgs":false,"family":"Cayan","given":"Daniel R.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":772062,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dugan, Jenifer E.","contributorId":219348,"corporation":false,"usgs":false,"family":"Dugan","given":"Jenifer","email":"","middleInitial":"E.","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":772063,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Feng, Dongmei","contributorId":219349,"corporation":false,"usgs":false,"family":"Feng","given":"Dongmei","email":"","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":772064,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Iacobellis, Samuel F.","contributorId":219350,"corporation":false,"usgs":false,"family":"Iacobellis","given":"Samuel","email":"","middleInitial":"F.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":772065,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Melack, John M.","contributorId":219351,"corporation":false,"usgs":false,"family":"Melack","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":772066,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Page, Henry M.","contributorId":219352,"corporation":false,"usgs":false,"family":"Page","given":"Henry","email":"","middleInitial":"M.","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":772067,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70205702,"text":"70205702 - 2019 - A spatio-contextual probabilistic model for extracting linear features in hilly terrain from high-resolution DEM data","interactions":[],"lastModifiedDate":"2019-10-02T11:26:38","indexId":"70205702","displayToPublicDate":"2019-10-02T11:26:30","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2046,"text":"International Journal of Geographical Information Science","active":true,"publicationSubtype":{"id":10}},"title":"A spatio-contextual probabilistic model for extracting linear features in hilly terrain from high-resolution DEM data","docAbstract":"This paper introduces our research in developing a probabilistic model to extract linear terrain features from high resolution DEM (Digital Elevation Model) data. The proposed model takes full advantage of spatio-contextual information to characterize terrain changes. It first derives a quantifiable measure of spatio-contextual patterns of linear terrain feature, such as ridgelines, valley lines and crater boundaries, and then adopts multiple neighborhood analysis and a probability model to address data uncertainty in terrain surface modeling. Different from traditional approaches, the proposed model has the ability to achieve near-automated processing, and to support effective extraction of terrain features in both smooth and rough surfaces. Through a series of experiments, we demonstrate that the proposed approach outperforms existing techniques, including: thresholding, stream/drainage network analysis, visual descriptor, object-based image analysis and edge detection. We hope this work contributes to both the geospatial data science and geomorphology communities with a new way of utilizing high-resolution imagery in terrain analysis.","language":"English","publisher":"Taylor and Francis","doi":"10.1080/13658816.2018.1554814","usgsCitation":"Zhou, X., Li, W., and Arundel, S., 2019, A spatio-contextual probabilistic model for extracting linear features in hilly terrain from high-resolution DEM data: International Journal of Geographical Information Science, v. 33, no. 4, p. 666-686, https://doi.org/10.1080/13658816.2018.1554814.","productDescription":"21 p.","startPage":"666","endPage":"686","ipdsId":"IP-085062","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":367920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-21","publicationStatus":"PW","contributors":{"authors":[{"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":772121,"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":772120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":772119,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70205665,"text":"70205665 - 2019 - Modeling sediment bypassing around idealized rocky headlands","interactions":[],"lastModifiedDate":"2019-10-02T11:19:58","indexId":"70205665","displayToPublicDate":"2019-10-02T11:19:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Modeling sediment bypassing around idealized rocky headlands","docAbstract":"Alongshore sediment bypassing rocky headlands remains understudied despite the importance of characterizing littoral processes for erosion abatement, beach management, and climate change adaptation. To address this gap, a numerical model sediment transport study was developed to identify controlling factors and mechanisms for sediment headland bypassing potential. Four idealized headlands were designed to investigate sediment flux around the headlands using the process-based hydrodynamic model Delft-3D and spectral wave model SWAN. The 120 simulations explored morphologies, substrate compositions, sediment grain sizes, and physical forcings (i.e., tides, currents, and waves) commonly observed in natural settings. A generalized analytical framework based on flow disruption and sediment volume was used to refine which factors and conditions were more useful to address sediment bypassing. A bypassing parameter was developed for alongshore sediment flux between upstream and downstream cross-shore transects to determine the degree of blockage by a headland. The shape of the headland heavily influenced the fate of the sediment by changing the local angle between the shore and the incident waves, with oblique large waves generating the most flux. All headlands may allow sediment flux, although larger ones blocked sediment more effectively, promoting their ability to be littoral cell boundaries. The controlling factors on sediment bypassing were determined to be wave angle, size, and shape of the headland, and sediment grain size.
","language":"English","publisher":"MDPI","doi":"10.3390/jmse7020040","usgsCitation":"Douglas A. George, John L. Largier, Pasternack, G.B., Barnard, P., Storlazzi, C.D., and Erikson, L.H., 2019, Modeling sediment bypassing around idealized rocky headlands: Journal of Marine Science and Engineering, v. 7, no. 2, 40; 37 p., https://doi.org/10.3390/jmse7020040.","productDescription":"40; 37 p.","ipdsId":"IP-111887","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":459632,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse7020040","text":"Publisher Index Page"},{"id":367918,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Douglas A. George","contributorId":219341,"corporation":false,"usgs":false,"family":"Douglas A. George","affiliations":[{"id":39994,"text":"Bodega Marine Laboratory, University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":772020,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"John L. Largier","contributorId":219342,"corporation":false,"usgs":false,"family":"John L. Largier","affiliations":[{"id":39994,"text":"Bodega Marine Laboratory, University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":772021,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pasternack, Greg B.","contributorId":219343,"corporation":false,"usgs":false,"family":"Pasternack","given":"Greg","email":"","middleInitial":"B.","affiliations":[{"id":39995,"text":"Department of Hydrologic Sciences, University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":772022,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":772019,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":772023,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":772024,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}