The Alaska Volcano Observatory (AVO) has maintained an earthquake catalog since 1989 that now contains over 120,000 hypocenters and magnitudes that occurred near Alaskan volcanoes. Since 1989 the seismic instrumentation and data acquisition and processing techniques have undergone numerous changes as computer systems and seismic processing software have advanced and evolved. In this report we recalculate earthquake hypocenters and magnitudes in a consistent manner between October 1989 and January 2018. The new hypocenters and magnitudes are archived at the AVO within an AQMS database and in the compressed UNIX tar file that accompanies this publication.
Contact Information,
Volcano Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
Using data from six wild Mojave Desert Tortoise (Gopherus agassizii (Cooper, 1861)) populations, we quantified seasonal differences in immune system measurements and microbial load in the respiratory tract, pertinent to this species’ susceptibility to upper respiratory tract disease. We quantified bacteria-killing activity of blood plasma and differential leukocyte counts to detect trends in temporal variation in immune function. We used centered log-ratio (clr) transformations of leukocyte counts and stress that such transformations are necessary for compositional data. We tested animals for the potential pathogen Pasteurella testudinis Snipes and Biberstein, 1982 with a newly created quantitative polymerase chain reaction (qPCR) assay, as well as for the known respiratory pathogens Mycoplasma agassizii Brown et al., 2001 and Mycoplasma testudineum Brown et al., 2004. We found very little disease and suggest that P. testudinis is a prevalent, commensal microbe in these Mojave Desert Tortoise populations, and its quantification may be a tool to study natural fluctuations in microbe levels in Mojave Desert Tortoise respiratory tracts. Our analyses showed that both the potential for inflammatory responses and microbe levels are highest in the spring for healthy Mojave Desert Tortoises, when lymphocyte levels are lowest. The genetic and statistical tools that we used are easily applicable to other wildlife systems and provide the necessary data to quantify species-wide trends in health and test hypotheses pertinent to host–microbe dynamics.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjz-2018-0255","usgsCitation":"Sandmeier, F.C., Leonard, K.L., Tracy, C., Drake, K.K., Esque, T., Nussear, K.E., and Germano, J., 2019, Tools to understand seasonality in health: quantification of microbe loads and analyses of compositional ecoimmunological data reveal complex patterns in Mojave Desert Tortoise (Gopherus agassizii) populations: Canadian Journal of Zoology, v. 97, no. 9, p. 841-848, https://doi.org/10.1139/cjz-2018-0255.","productDescription":"8 p.","startPage":"841","endPage":"848","ipdsId":"IP-107373","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":501004,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/96565","text":"External Repository"},{"id":380189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"97","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sandmeier, F. C.","contributorId":244500,"corporation":false,"usgs":false,"family":"Sandmeier","given":"F.","email":"","middleInitial":"C.","affiliations":[{"id":48921,"text":"Colorado State University-Pueblo","active":true,"usgs":false}],"preferred":false,"id":804067,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leonard, K. L.","contributorId":244501,"corporation":false,"usgs":false,"family":"Leonard","given":"K.","email":"","middleInitial":"L.","affiliations":[{"id":48921,"text":"Colorado State University-Pueblo","active":true,"usgs":false}],"preferred":false,"id":804068,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tracy, C. R.","contributorId":244502,"corporation":false,"usgs":false,"family":"Tracy","given":"C. R.","affiliations":[{"id":48922,"text":"University of Nevado, Reno","active":true,"usgs":false}],"preferred":false,"id":804069,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drake, K. Kristina 0000-0003-0711-7634 kdrake@usgs.gov","orcid":"https://orcid.org/0000-0003-0711-7634","contributorId":3799,"corporation":false,"usgs":true,"family":"Drake","given":"K.","email":"kdrake@usgs.gov","middleInitial":"Kristina","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":804070,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":804071,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nussear, K. E.","contributorId":204375,"corporation":false,"usgs":false,"family":"Nussear","given":"K.","email":"","middleInitial":"E.","affiliations":[{"id":36924,"text":"Univerisity of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":804072,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Germano, J","contributorId":244503,"corporation":false,"usgs":false,"family":"Germano","given":"J","email":"","affiliations":[{"id":38703,"text":"New Zealand Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":804073,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70203301,"text":"ofr20191049 - 2019 - Data management plan for the U.S. Geological Survey Washington Water Science Center","interactions":[],"lastModifiedDate":"2019-05-16T10:25:56","indexId":"ofr20191049","displayToPublicDate":"2019-05-14T14:08:30","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":"2019-1049","displayTitle":"Data Management Plan for the U.S. Geological Survey Washington Water Science Center","title":"Data management plan for the U.S. Geological Survey Washington Water Science Center","docAbstract":"The primary mission of the U.S. Geological Survey (USGS) Water Mission Area is to collect and disseminate reliable, impartial, and timely information needed to understand the water resources of the Nation, including data on streamflow, groundwater, water quality, water use, and availability. Management of data throughout the entire data lifecycle is necessary to meet the mission and maintain the USGS reputation of producing high-quality data as the Nation’s primary earth-science information agency. This document describes the data management procedures of the USGS Washington Water Science Center, including responsibilities of staff and workflow procedures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191049","usgsCitation":"Conn, K.E., Mastin, M.C., Long, A.J., Dinicola, R.S., and Barton, C., 2019, Data management plan for the U.S. Geological Survey Washington Water Science Center : U.S. Geological Survey Open-File Report 2019-1049, 23 p., https://doi.org/10.3133/ofr20191049.","productDescription":"Report: iv, 23 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-104277","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":363807,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1049/coverthb.jpg"},{"id":363808,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1049/ofr20191049.pdf","text":"Report","size":"573 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1049"}],"country":"United States","state":"Washington","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Choices in ecological research and natural resource management require balancing multiple, often competing objectives. Examples include maximizing species persistence in a wildlife conservation context, while minimizing cost, or balancing opposing stakeholder objectives when managing wildlife populations. Multiple-objective optimization (MOO) provides a unifying framework for solving multiple objective problems. Model selection is a critical component of ecological inference and prediction and requires balancing the competing objectives of model fit and model complexity. The tradeoff between model fit and model complexity provides a basis for describing the model-selection problem within the MOO framework. We discuss MOO and two strategies for solving the MOO problem; modeling preferences pre-optimization and post-optimization. Most conventional model selection methods can be formulated as solutions of MOO problems via specification of pre-optimization preferences. We reconcile model selection within the MOO framework. We also consider model selection using post-optimization specification of preferences. That is, by first identifying Pareto optimal solutions, and then selecting among them. We demonstrate concepts with an ecological application of model selection using avian species richness data in the continental United States.
Rock environments both underground and on Earth’s surface show indications of energetic macroscale fracture. In tunnels and excavations, these manifest as rockbursts—energetic explosions of rock that can damage engineering projects, and may pose ongoing financial and safety risk as rock stresses adjust during post-failure relaxation. In natural settings at the surface, evidence for rockbursts exist in the form of tent-like structures of ruptured exfoliation sheets, but few direct observations of such events exist, precluding the analysis of how natural rock formations may evolve after rupture. Here we investigate the post-failure evolution of a granitic rock dome following rapid fracture events (i.e., surficial rockbursts) that occurred in California, USA during 2014–2016. Building upon previous work that showed a thermal stress origin for the observed fracturing, we investigate the return to background stress conditions (i.e., stress relaxation) observed in both short- (week, month) and long-term (multi-year) rock deformation trends. Acoustic emissions, deformation, and environmental monitoring data indicate that partially detached rock sheets forming the surface of the dome undergo fracture aperture closing during cooling periods, concurrent with reduction of rock stress by the source of forcing (i.e., thermal stress). However, with sufficient critical and/or subcritical fracture, our observations also show that rock sheets can become decoupled from the source of stress, resulting in a long-term return to background stress conditions. Our results provide insight into the cyclic and likely ephemeral nature of rock fracture in surficial rock domes, as well as in underground rockburst environments.
Due to concerns over the effects of oil production activities on groundwater quality in California, chemical, isotopic, dissolved gas and age-dating tracers were analyzed in samples collected from public-supply wells and produced-water sites in the Fruitvale oil field (FVOF). A combination of newly collected and historical data was used to determine whether oil formation fluids have mixed with groundwater used for public supply and what the potential pathways for the migration of oil formation fluids into groundwater may be. Stable isotopes of water (δ2H and δ18O) and age dating (3H, 3Hetrit, SF6 and 14C) tracers in groundwater samples were consistent with the Kern River being the main source of recharge to aquifers. The distribution of major ion concentrations and pH with distance from the Kern River indicate that natural processes were the primary controls on groundwater salinity. Two of 14 groundwater samples had δ13C-DIC values (−2.4 to +1.9 per mil) consistent with mixtures of <1 to about 9 percent oil-field water. Concentrations of TDS in groundwater samples were generally much lower (129–1,200 milligrams per liter (mg/l), median 216 mg/l) than produced water samples (586–24,930 mg/l, median 2,717 mg/l), suggesting that any mixing of oil-field water with groundwater has not significantly affected groundwater salinity. Trace concentrations of thermogenic methane were detected in three groundwater samples that did not have dissolved inorganic or isotopic indicators consistent with mixing of oil-field water, suggesting that stray gases may have migrated from the subsurface via preferential pathways such as leaky well bores into groundwater aquifers. Low concentrations of petroleum hydrocarbons were detected in samples that also contained anthropogenic VOCs and components of post- and pre-1950s recharge, indicating that petroleum hydrocarbons could have come from subsurface and/or surface sources. Overall, the results of this study indicated that groundwater currently used for public supply in the FVOF was of good quality with little, if any, effects from oil production activities. This may be due in part to the relatively rapid flushing of the aquifer system by recharge from the Kern River.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2019.05.003","usgsCitation":"Wright, M., McMahon, P.B., Landon, M.K., and Kulongoski, J.T., 2019, Groundwater quality of a public supply aquifer in proximity to oil development, Fruitvale Oil Field, Bakersfield, California: Applied Geochemistry, v. 106, p. 82-95, https://doi.org/10.1016/j.apgeochem.2019.05.003.","productDescription":"14 p.","startPage":"82","endPage":"95","ipdsId":"IP-093942","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"links":[{"id":467620,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2019.05.003","text":"Publisher Index Page"},{"id":365096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Bakersfield","otherGeospatial":"Fruitvale Oil Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.57107543945311,\n 35.17493084974928\n ],\n [\n -119.57107543945311,\n 35.17493084974928\n ],\n [\n -119.57107543945311,\n 35.17493084974928\n ],\n [\n -119.57107543945311,\n 35.17493084974928\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.00115966796875,\n 35.48890875085949\n ],\n [\n -119.04373168945314,\n 35.48541438776276\n ],\n [\n -119.0478515625,\n 35.48275857019274\n ],\n [\n -119.04870986938477,\n 35.46808010116232\n ],\n [\n -119.04029846191405,\n 35.46108941215697\n ],\n [\n -119.02072906494142,\n 35.44724605551148\n ],\n [\n -119.02072906494142,\n 35.44221151715641\n ],\n [\n -119.01643753051758,\n 35.43941441533686\n ],\n [\n -119.01163101196289,\n 35.436617216330966\n ],\n [\n -119.00922775268555,\n 35.42878454211113\n ],\n [\n -119.01884078979492,\n 35.42374884923695\n ],\n [\n -119.01248931884766,\n 35.411018333025666\n ],\n [\n -119.00871276855467,\n 35.40682101870289\n ],\n [\n -118.98828506469725,\n 35.41003897923532\n ],\n [\n -118.96957397460938,\n 35.41269719754907\n ],\n [\n -118.96322250366211,\n 35.42290953648285\n ],\n [\n -118.94914627075195,\n 35.43395978725954\n ],\n [\n -118.9460563659668,\n 35.44235136969617\n ],\n [\n -118.95429611206056,\n 35.453259119161764\n ],\n [\n -118.9621925354004,\n 35.46808010116232\n ],\n [\n -118.97283554077148,\n 35.474930386870035\n ],\n [\n -118.99686813354491,\n 35.485833719356805\n ],\n [\n -119.00115966796875,\n 35.48890875085949\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wright, Michael 0000-0003-0653-6466 mtwright@usgs.gov","orcid":"https://orcid.org/0000-0003-0653-6466","contributorId":151031,"corporation":false,"usgs":true,"family":"Wright","given":"Michael","email":"mtwright@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Landon, Matthew K. 0000-0002-5766-0494 landon@usgs.gov","orcid":"https://orcid.org/0000-0002-5766-0494","contributorId":392,"corporation":false,"usgs":true,"family":"Landon","given":"Matthew","email":"landon@usgs.gov","middleInitial":"K.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765171,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":765172,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202978,"text":"ofr20191037 - 2019 - Monitoring live vegetation in semiarid and arid rangeland environments with satellite remote sensing in northern Kenya","interactions":[],"lastModifiedDate":"2019-05-14T11:37:50","indexId":"ofr20191037","displayToPublicDate":"2019-05-13T11:49:01","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":"2019-1037","displayTitle":"Monitoring Live Vegetation in Semiarid and Arid Rangeland Environments with Satellite Remote Sensing in Northern Kenya","title":"Monitoring live vegetation in semiarid and arid rangeland environments with satellite remote sensing in northern Kenya","docAbstract":"As part of the U.S. Department of the Interior’s (DOI) commitment to provide technical assistance to the Kenyan Northern Rangelands Trust (NRT), the U.S. Geological Survey, in collaboration with the DOI International Technical Assistance Program and the U.S. Agency for International Development’s regional mission in East Africa, created a high spatial and time-sensitive live vegetation monitoring system for NRT. The system built with advanced field and sensor technologies produced directly calibrated and highly accurate satellite mapping that is extendable both forward and backward in time. The maps are produced in a simple 0–100-percent representation of live vegetation status and change over time. The backbone of the mapping is the Sentinel satellite remote sensing systems with 5-day collection frequencies and ground spatial resolutions of 10 meters. The European Space Agency (ESA) offers free Sentinel satellite image data through conveniently accessed websites and free user-friendly image processing software downloadable directly onto a personal workstation. ESA provides free online software support. The mapping capability was extended from the forward mapping of Sentinel back in time with the Landsat satellite remote sensing system that has an available and free data archive back to 1983. Although Landsat has coarser spatial resolution, the Landsat to Sentinel live vegetation mapping comparison supports the use of Landsat to provide NRT the historical recreation of prominent live vegetation changes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191037","collaboration":"Prepared in cooperation with the U.S. Agency for International Development","usgsCitation":"Rangoonwala, Amina, and Ramsey, E.W., III, 2019, Monitoring live vegetation in semiarid and arid rangeland environments with satellite remote sensing in northern Kenya: U.S. Geological Survey Open-File Report 2019–1037, 83 p., https://doi.org/10.3133/ofr20191037.","productDescription":"Report: vii, 83 p.; 15 Figures","numberOfPages":"96","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105119","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":363609,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037.pdf","text":"Report","size":"22.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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"},{"id":363623,"rank":16,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig40b.tif","text":"Figure 40B—high resolution—","description":"OFR 2019–1037 Figure 40B","linkHelpText":"Live vegetation cover proportion for the core-Kenyan Northern Rangelands Trust conservancies in June 2018 "},{"id":363624,"rank":17,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig41.jpg","text":"Figure 41—high resolution—","description":"OFR 2019–1037 Figure 41","linkHelpText":"June 2018 synthetic aperture radar (SAR) vertical send and vertical receive (VV) and vertical send and horizontal receive (VH) images"}],"country":"Kenya","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 33.8818359375,\n -0.9667509997666298\n ],\n [\n 37.72705078125,\n -3.601142320158722\n ],\n [\n 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U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
Topographical differencing and edge-matching analyses were used to evaluate agreement of the Coastal National Elevation Database Applications Project’s Northern Gulf of Mexico topobathymetric digital elevation model (TBDEM) with The National Map 3-Dimensional Elevation Program (3DEP) 1/3 arc-second digital elevation models (DEMs). In addition to topographic map products provided through the National Geospatial Program, the model integrates bathymetric and topobathymetric datasets for three-dimensional (3D) mapping of rivers, lakes, and bays in the upland and intertidal wetlands to offshore environments in coastal zones from the border between Texas and Louisiana to east of Mobile Bay, Alabama.
Contoured elevation differences between the Northern Gulf of Mexico TBDEM and the 3DEP 1/3 arc-second DEMs indicate that 85 percent of elevation data in the Northern Gulf of Mexico TBDEM agree (no difference for contoured elevations) between 95 and 100 percent with 3DEP 1/3 arc-second DEMs. Edge matching differences between adjacent Northern Gulf of Mexico TBDEM source projects or between the TBDEM and 3DEP DEMs indicate most seams between integrated and 3DEP DEMs are smooth. Where seams did not match, most differences were in the range of tenths to hundredths of a meter. Valid differences that are greater than plus or minus 2 meters in areas of bathymetric data are found in the Mississippi River, Atchafalaya River, Lower Atchafalaya River, Wax Lake Pass channel, the Vermilion Bay bathymetric datasets, and where topobathymetric datasets are integrated in the model. Areas with positive or negative outlier difference elevations seem to be a result of site conditions that affect light detection and ranging (lidar) waveform return signals, misclassification of surface features, or possibly because of interpolation required to develop a smooth elevation surface. Results of this analysis provide information to help understand model parameters and agreement of the Northern Gulf of Mexico TBDEM developed using different data types from different sources with The National Map 3DEP DEMs.
Inclusion of bathymetric and topobathymetric data types in the 3DEP aligns with the mission to respond to growing needs for a wide range of three-dimensional representations of the Nation and supports the U.S. Geological Survey strategy for developing a National Terrain Model to provide hydrographic and elevation data that extend the elevation surface below water bodies. The 3D Nation Requirements and Benefits Study sponsored by the U.S. Geological Survey and National Oceanic and Atmospheric Administration to assess local to regional Tribal, State, and Federal technical requirements, needs, and benefits for using topographic and bathymetric 3DEP elevation data will be used to help develop and refine future program alternatives for 3D elevation data that include a category for bathymetry and topobathymetry. At the time of this report (2019), 3DEP acquisition is specific to topographic lidar that meets lidar DEM specifications and which requires surface-water feature areas to be hydroflattened. Cataloging bathymetric and topobathymetric DEMs as part of the 3DEP will require new specifications for acoustic, lidar, merged acoustic and lidar, and possibly other bathymetric and topobathymetric survey data types.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191016","usgsCitation":"Miller-Corbett, C., 2019, Analysis for agreement of the Northern Gulf of Mexico topobathymetric digital elevation model with 3-Dimensional Elevation Program 1/3 arc-second digital elevation models: U.S. Geological Survey Open-File Report 2019–1016, 44 p., https://doi.org/10.3133/ofr20191016.","productDescription":"vi, 43 p.","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-081383","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":363655,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1016/ofr20191016.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1016"},{"id":363654,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1016/coverthb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.48193359375,\n 28.43971381702788\n ],\n [\n -84.13330078125,\n 28.43971381702788\n ],\n [\n -84.13330078125,\n 31.39115752282472\n ],\n [\n -96.48193359375,\n 31.39115752282472\n ],\n [\n -96.48193359375,\n 28.43971381702788\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
1. Hotspot analysis is a commonly used method in ecology and conservation to identify areas of high biodiversity or conservation concern. However, delineating and mapping hotspots is subjective and various approaches can lead to different conclusions with regard to the classification of particular areas as hotspots, complicating long-term conservation planning and implementation efforts.
2. We present a comparative analysis of recent approaches for identifying waterbird hotspots, with the goal of developing insights about the appropriate use of these methods. We selected four commonly used measures to identify persistent areas of high use: kernel density estimation, Getis-Ord Gi*, hotspot persistence, and hotspots conditional on presence, which represent the range of quantitative hotspot estimation approaches used in waterbird analyses. We applied each of the methods to aerial survey waterbird count data collected in the Great Lakes from 2012-2014 using a 5 km2 grid. For each approach, we identified areas of high use for seven species/species groups and then compared the results across all methods.
3. Our results indicate that formal hotspot analysis frameworks do not always lead to the same conclusions. The kernel density and Getis-Ord Gi* methods yielded the most similar results across all species analyzed. We found that these two models can differ substantially from the hotspot persistence and hotspots conditional on presence estimation approaches, which were not consistently similar to one another. The hotspot persistence approach differed most significantly from the other methods but is the only method to explicitly account for temporal variation.
4. We recommend considering the ecological question and scale of any conservation or management activities prior to designing survey methodologies. Deciding the appropriate definition and scale for analysis is critical for interpretation of hotspot analysis results. Combining methods using an integrative approach, either within a single analysis or post-hoc, could lead to greater consistency in the identification of waterbird hotspots.
","language":"English","publisher":"British Ecological society","doi":"10.1111/2041-210X.13209","usgsCitation":"Sussman, A.L., Gardner, B., Adams, E.M., Salas, L., Kenow, K.P., Luukkonen, D.R., Monfils, M.J., Mueller, W.P., Williams, K.A., Leduc-Lapierre, M., and Zipkin, E.F., 2019, A comparative analysis of common methods to identify waterbird hotspots: Methods in Ecology and Evolution, v. 10, no. 9, p. 1454-1468, https://doi.org/10.1111/2041-210X.13209.","productDescription":"15 p.","startPage":"1454","endPage":"1468","ipdsId":"IP-091670","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":467621,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13209","text":"Publisher Index Page"},{"id":367534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie, Lake Huron, Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.76953125,\n 41.21172151054787\n ],\n [\n -78.75,\n 41.21172151054787\n ],\n [\n -78.75,\n 46.164614496897094\n ],\n [\n -88.76953125,\n 46.164614496897094\n ],\n [\n -88.76953125,\n 41.21172151054787\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"9","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Sussman, Allison L.","contributorId":219074,"corporation":false,"usgs":false,"family":"Sussman","given":"Allison","email":"","middleInitial":"L.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":771216,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gardner, 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kkenow@usgs.gov","orcid":"https://orcid.org/0000-0002-3062-5197","contributorId":3339,"corporation":false,"usgs":true,"family":"Kenow","given":"Kevin","email":"kkenow@usgs.gov","middleInitial":"P.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":771215,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Luukkonen, David R.","contributorId":219076,"corporation":false,"usgs":false,"family":"Luukkonen","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":771220,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monfils, Michael J.","contributorId":219077,"corporation":false,"usgs":false,"family":"Monfils","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":39957,"text":"Michigan State University Extension","active":true,"usgs":false}],"preferred":false,"id":771221,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mueller, William P.","contributorId":219078,"corporation":false,"usgs":false,"family":"Mueller","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":39958,"text":"Western Great Lakes Bird and Bat Observatory","active":true,"usgs":false}],"preferred":false,"id":771222,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Williams, Kate A.","contributorId":219079,"corporation":false,"usgs":false,"family":"Williams","given":"Kate","email":"","middleInitial":"A.","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":771223,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Leduc-Lapierre, Michelle","contributorId":219080,"corporation":false,"usgs":false,"family":"Leduc-Lapierre","given":"Michelle","email":"","affiliations":[{"id":13509,"text":"Great Lakes Commission","active":true,"usgs":false}],"preferred":false,"id":771224,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zipkin, Elise F. 0000-0003-4155-6139","orcid":"https://orcid.org/0000-0003-4155-6139","contributorId":192755,"corporation":false,"usgs":false,"family":"Zipkin","given":"Elise","email":"","middleInitial":"F.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":771225,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70203819,"text":"70203819 - 2019 - Emperor geese (Anser canagicus) are exposed to a diversity of influenza A viruses, are infected during the non-breeding period and contribute to intercontinental viral dispersal","interactions":[],"lastModifiedDate":"2019-09-16T12:18:04","indexId":"70203819","displayToPublicDate":"2019-05-11T11:22:32","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3849,"text":"Transboundary and Emerging Diseases","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Emperor geese (Anser canagicus) are exposed to a diversity of influenza A viruses, are infected during the non‐breeding period and contribute to intercontinental viral dispersal","title":"Emperor geese (Anser canagicus) are exposed to a diversity of influenza A viruses, are infected during the non-breeding period and contribute to intercontinental viral dispersal","docAbstract":"Emperor geese (Anser canagicus) are endemic to coastal areas within Beringia and have previously been found to have antibodies to or to be infected with influenza A viruses (IAVs) in Alaska. In this study, we use virological, serological and tracking data to further elucidate the role of emperor geese in the ecology of IAVs in Beringia during the non‐breeding period. Specifically, we assess evidence for: (a) active IAV infection during spring staging, autumn staging and wintering periods; (b) infection with novel Eurasian‐origin or interhemispheric reassortant viruses; (c) contemporary movement of geese between East Asia and North America; (d) previous exposure to viruses of 14 haemagglutinin subtypes, including Eurasian lineage highly pathogenic (HP) H5 IAVs; and (e) subtype‐specific antibody seroconversion and seroreversion. Emperor geese were found to shed IAVs, including interhemispheric reassortant viruses, throughout the non‐breeding period; migrate between Alaska and the Russian Far East prior to and following remigial moult; have antibodies reactive to a diversity of IAVs including, in a few instances, Eurasian lineage HP H5 IAVs; and exhibit relatively broad and stable patterns of population immunity among breeding females. Results of this study suggest that emperor geese may play an important role in the maintenance and dispersal of IAVs within Beringia during the non‐breeding period and provide information that may be used to further optimize surveillance activities focused on the early detection of Eurasian‐origin IAVs in North America.
","language":"English","publisher":"Wiley","doi":"10.1111/tbed.13226","usgsCitation":"Ramey, A.M., Uher-Koch, B.D., Reeves, A.B., Schmutz, J.A., Poulson, R., and Stallknecht, D.E., 2019, Emperor geese (Anser canagicus) are exposed to a diversity of influenza A viruses, are infected during the non-breeding period and contribute to intercontinental viral dispersal: Transboundary and Emerging Diseases, v. 66, no. 5, p. 1958-1970, https://doi.org/10.1111/tbed.13226.","productDescription":"13 P.","startPage":"1958","endPage":"1970","ipdsId":"IP-106647","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":467622,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/tbed.13226","text":"Publisher Index Page"},{"id":437465,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VFN3JD","text":"USGS data release","linkHelpText":"Influenza A Virus Data from Emperor Geese, Alaska"},{"id":364698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","state":"Alaska","volume":"66","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":764260,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uher-Koch, Brian D. 0000-0002-1885-0260 buher-koch@usgs.gov","orcid":"https://orcid.org/0000-0002-1885-0260","contributorId":5117,"corporation":false,"usgs":true,"family":"Uher-Koch","given":"Brian","email":"buher-koch@usgs.gov","middleInitial":"D.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":764261,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reeves, Andrew B. 0000-0002-7526-0726 areeves@usgs.gov","orcid":"https://orcid.org/0000-0002-7526-0726","contributorId":167362,"corporation":false,"usgs":true,"family":"Reeves","given":"Andrew","email":"areeves@usgs.gov","middleInitial":"B.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":764262,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmutz, Joel A. 0000-0002-6516-0836 jschmutz@usgs.gov","orcid":"https://orcid.org/0000-0002-6516-0836","contributorId":1805,"corporation":false,"usgs":true,"family":"Schmutz","given":"Joel","email":"jschmutz@usgs.gov","middleInitial":"A.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":764263,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poulson, Rebecca L.","contributorId":198807,"corporation":false,"usgs":false,"family":"Poulson","given":"Rebecca L.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":764264,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stallknecht, David E.","contributorId":14323,"corporation":false,"usgs":false,"family":"Stallknecht","given":"David","email":"","middleInitial":"E.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":764265,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70204238,"text":"70204238 - 2019 - A comparison of chlorophyll a values obtained from an autonomous underwater vehicle to satellite-based measures for Lake Michigan","interactions":[],"lastModifiedDate":"2019-08-13T15:39:03","indexId":"70204238","displayToPublicDate":"2019-05-10T10:18:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of chlorophyll a values obtained from an autonomous underwater vehicle to satellite-based measures for Lake Michigan","docAbstract":"Accurate methods to track changes in lake productivity through time and space are critical to fisheries management. Chlorophyll a is the most widely studied proxy for ecosystem primary production, and has been the topic of many studies. The main sources of chlorophyll a measurements are ship-based measures or multi-spectral satellite data. Autonomous underwater vehicles can survey large spatial extents approaching the scale of satellite data, but with the accuracy of ship-based water sampling methods. We use several statistical measures to compare measures of chlorophyll a collected in Lake Michigan with spatiotemporally matched satellite-derived measures of chlorophyll a from the MODIS Aqua multi-spectral sensor using NASA’s OC3 and the Great Lakes Fit algorithms. Our findings show a near one to one relationship between AUV data and both satellite-derived data sets when the AUV data are coarsened to the resolution of the satellite data. A comparison of satellite-based chlorophyll a to AUV-derived chlorophyll summarized in discrete water depth bins suggested that, based on decreasing coefficients of determination, satellite estimates of chlorophyll accounted for the most variability in chlorophyll a concentrations in the upper 10 m of the water column, even though satellite sensors may detect past this depth.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2019.04.003","usgsCitation":"Bennion, D., Warner, D., Esselman, P., Hobson, B., and Kieft, B., 2019, A comparison of chlorophyll a values obtained from an autonomous underwater vehicle to satellite-based measures for Lake Michigan: Journal of Great Lakes Research, v. 45, no. 4, p. 726-734, https://doi.org/10.1016/j.jglr.2019.04.003.","productDescription":"9 p.","startPage":"726","endPage":"734","ipdsId":"IP-096378","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":467624,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2019.04.003","text":"Publisher Index Page"},{"id":365575,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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Center","active":true,"usgs":true}],"preferred":true,"id":766120,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warner, David 0000-0003-4939-5368","orcid":"https://orcid.org/0000-0003-4939-5368","contributorId":216543,"corporation":false,"usgs":true,"family":"Warner","given":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":766121,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":766122,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hobson, Brett","contributorId":216922,"corporation":false,"usgs":false,"family":"Hobson","given":"Brett","email":"","affiliations":[{"id":37324,"text":"Monterey Bay Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":766173,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kieft, Brian","contributorId":216923,"corporation":false,"usgs":false,"family":"Kieft","given":"Brian","email":"","affiliations":[{"id":37324,"text":"Monterey Bay Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":766174,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204561,"text":"70204561 - 2019 - A field evaluation of the growth and survival of age-0 Oncorhynchus mykiss tagged with 8-mm passive integrated transponder (PIT) tags","interactions":[],"lastModifiedDate":"2019-08-05T09:47:02","indexId":"70204561","displayToPublicDate":"2019-05-10T07:17:50","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}},"displayTitle":"A field evaluation of the growth and survival of age-0 Oncorhynchus mykiss tagged with 8-mm passive integrated transponder (PIT) tags","title":"A field evaluation of the growth and survival of age-0 Oncorhynchus mykiss tagged with 8-mm passive integrated transponder (PIT) tags","docAbstract":"In fish tagging studies, tag size limits the size of fish that can be tagged, the fraction of a population that can be represented, and ultimately inferences that can be made about the study population, particularly when juvenile fish are the subject of interest. Introduction of an 8-mm passive integrated transponder (PIT) reduced the minimum taggable size of fish, but it has not been evaluated in field trials. We evaluated the growth and survival of age-0 Oncorhynchus mykiss tagged with 8-mm PIT tags in four streams in southwest Washington, USA.
A total of 351 PIT tagged fish and 340 control fish (marked with pelvic fin clips) were released, but recapture rates were low, particularly for control fish. Growth in length and mass did not differ between small (42–54 mm) and large (55–64 mm) PIT tagged fish. There was a slightly positive, but weak, relation between tag burden and growth in mass; however, there was considerable variability in this relation (R2 = 0.115). Summer to autumn joint probability of fish surviving and remaining in the study area estimated with a Bayesian mark-recapture model ranged from 0.228 to 0.478 in study streams. We found no significant relation between tag burden and survival, suggesting neither tag burden nor fish size at tagging affected survival.
Although this study was limited in scope, it provided insight into how age-0 O. mykiss tagged with 8-mm PIT tags grew and survived under natural conditions. We showed that fish as small as 42 mm could be tagged without detrimental effects, which should allow researchers to represent a larger portion of study populations through PIT tagging.
","language":"English","publisher":"BioMed Central Ltd","doi":"10.1186/s40317-019-0171-9","usgsCitation":"Tiffan, K., Jezorek, I., and Perry, R., 2019, A field evaluation of the growth and survival of age-0 Oncorhynchus mykiss tagged with 8-mm passive integrated transponder (PIT) tags: Animal Biotelemetry, v. 7, Article 9, 8 p., https://doi.org/10.1186/s40317-019-0171-9.","productDescription":"Article 9, 8 p.","ipdsId":"IP-102909","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":460385,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-019-0171-9","text":"Publisher Index Page"},{"id":366095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.76074218749999,\n 45.897654534346906\n ],\n [\n -119.10278320312499,\n 45.897654534346906\n ],\n [\n -119.10278320312499,\n 47.69497434186282\n ],\n [\n -124.76074218749999,\n 47.69497434186282\n ],\n [\n -124.76074218749999,\n 45.897654534346906\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Tiffan, Kenneth 0000-0002-5831-2846","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":217812,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":767570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jezorek, Ian 0000-0002-3842-3485","orcid":"https://orcid.org/0000-0002-3842-3485","contributorId":217813,"corporation":false,"usgs":true,"family":"Jezorek","given":"Ian","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":767571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":217814,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":767572,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70205124,"text":"70205124 - 2019 - oSCR: A spatial capture–recapture R package for inference about spatial ecological processes","interactions":[],"lastModifiedDate":"2019-09-04T15:47:48","indexId":"70205124","displayToPublicDate":"2019-05-08T15:45:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"oSCR: A spatial capture–recapture R package for inference about spatial ecological processes","docAbstract":"Spatial capture–recapture (SCR) methods have become widely applied in ecology. The immediate adoption of SCR is due to the fact that it resolves some major criticisms of traditional capture–recapture methods related to heterogeneity in detectabililty, and the emergence of new technologies (e.g. camera traps, non‐invasive genetics) that have vastly improved our ability to collection spatially explicit observation data on individuals. However, the utility of SCR methods reaches far beyond simply convenience and data availability. SCR presents a formal statistical framework that can be used to test explicit hypotheses about core elements of population and landscape ecology, and has profound implications for how we study animal populations. In this software note, we describe the technical basis and analytical workflow of oSCR, an R package for analyzing spatial encounter history data using a multi‐session sex‐structured likelihood. The impetus for developing oSCR was to create an accessible and transparent analysis tool that allows users to conveniently and intuitively formulate statistical models that map directly to fundamental processes of interest in spatial population ecology (e.g. space use, resource selection, density and connectivity). We have placed an emphasis on creating a transparent and accessible code base that is coupled with a logical workflow that we hope stimulates active participation in further technical developments.
","language":"English","publisher":"Wiley","doi":"10.1111/ecog.04551","usgsCitation":"Chris Sutherland, Royle, J.A., and Linden, D., 2019, oSCR: A spatial capture–recapture R package for inference about spatial ecological processes: Ecography, v. 42, no. 9, p. 1459-1469, https://doi.org/10.1111/ecog.04551.","productDescription":"11 p.","startPage":"1459","endPage":"1469","ipdsId":"IP-108080","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467628,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/ecog.04551","text":"External Repository"},{"id":367195,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"9","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Chris Sutherland","contributorId":196873,"corporation":false,"usgs":false,"family":"Chris Sutherland","affiliations":[],"preferred":false,"id":770127,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":770126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Linden, Dan","contributorId":218743,"corporation":false,"usgs":false,"family":"Linden","given":"Dan","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":770128,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210516,"text":"70210516 - 2019 - Distribution of mineral phases in the Eocene Green River Formation, Piceance Basin, Colorado – Implications for the evolution of Lake Uinta","interactions":[],"lastModifiedDate":"2020-06-08T19:43:39.040549","indexId":"70210516","displayToPublicDate":"2019-05-08T14:38:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2789,"text":"Mountain Geologist","active":true,"publicationSubtype":{"id":10}},"title":"Distribution of mineral phases in the Eocene Green River Formation, Piceance Basin, Colorado – Implications for the evolution of Lake Uinta","docAbstract":"The mineralogy of the Eocene Green River Formation in the Piceance Basin, Colorado, has been the subject of numerous studies since the 1920s. Most previous work has focused on the resource potential of these lacustrine mudrocks, which in addition to substantial oil shale potential (in-place resources of 353 billion barrels of synthetic crude oil for rocks yielding at least 25 gallons per ton, GPT), includes nahcolite, a currently utilized soda ash resource, and dawsonite, a potential alternative source of aluminum. Another reason to study the mineralogy in this system is that the geographic and stratigraphic distribution of various authigenic minerals may provide insights into the geochemistry and depositional environment of the long-lived Eocene Lake Uinta. In this study, legacy non-quantitative (presence/absence) X-ray diffraction (XRD) data recently published by the U.S. Geological Survey (USGS) for more than nine-thousand samples collected from thirty coreholes in the Green River Formation, Piceance Basin were examined. These data were used to better define the stratigraphic and paleogeographic extent of a set of indicator minerals (illite, analcime, albite, dawsonite, and nahcolite) within the Piceance Basin lacustrine strata. This set of minerals was selected based on observations from previous work and variability in their occurrence and co-occurrence within the Piceance Basin. The USGS database has been used to (1) construct maps showing geographic variations in mineral occurrences for 14 stratigraphically defined rich and lean oil shale zones; (2) assess co-occurrences of indicator minerals; and (3) compare occurrence results with quantitative XRD datasets collected on Piceance Basin oil shales. Occurrences of many authigenic minerals (analcime, dawsonite, and nahcolite) varied in the lacustrine strata near and around the depocenter, but others, like quartz, dolomite, and feldspar (potassium + undifferentiated), were widely and consistently present (>90% of samples) across the basin. Shifts in the distribution of indicator mineral occurrences generally coincide with changes identified in previous lake history descriptions and indicate that the water chemistry of Lake Uinta varied significantly going from near-shore to the depocenter and through time.","language":"English","publisher":"Rocky Mountain Association of Geologists","doi":"10.31582/rmag.mg.56.2.73","usgsCitation":"Birdwell, J.E., Johnson, R.C., and Brownfield, M.E., 2019, Distribution of mineral phases in the Eocene Green River Formation, Piceance Basin, Colorado – Implications for the evolution of Lake Uinta: Mountain Geologist, v. 56, no. 2, p. 73-141, https://doi.org/10.31582/rmag.mg.56.2.73.","productDescription":"69 p.","startPage":"73","endPage":"141","ipdsId":"IP-102092","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":375424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Piceance Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.072265625,\n 38.54816542304656\n ],\n [\n -106.435546875,\n 38.54816542304656\n ],\n [\n -106.435546875,\n 40.96330795307353\n ],\n [\n -109.072265625,\n 40.96330795307353\n ],\n [\n -109.072265625,\n 38.54816542304656\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":790489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Ronald C. 0000-0002-6197-5165 rcjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-6197-5165","contributorId":1550,"corporation":false,"usgs":true,"family":"Johnson","given":"Ronald","email":"rcjohnson@usgs.gov","middleInitial":"C.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":790490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brownfield, Michael E. 0000-0003-3633-1138 mbrownfield@usgs.gov","orcid":"https://orcid.org/0000-0003-3633-1138","contributorId":1548,"corporation":false,"usgs":true,"family":"Brownfield","given":"Michael","email":"mbrownfield@usgs.gov","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":790491,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202744,"text":"ofr20191029 - 2019 - Spatial integration of biological and social objectives to identify priority landscapes for waterfowl habitat conservation","interactions":[],"lastModifiedDate":"2024-03-04T18:47:33.875141","indexId":"ofr20191029","displayToPublicDate":"2019-05-08T12:45:00","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":"2019-1029","displayTitle":"Spatial Integration of Biological and Social Objectives to Identify Priority Landscapes for Waterfowl Habitat Conservation","title":"Spatial integration of biological and social objectives to identify priority landscapes for waterfowl habitat conservation","docAbstract":"Waterfowl population management and habitat conservation compose one of the oldest and most successful adaptive management frameworks in the world. Since its inception, the North American Waterfowl Management Plan (NAWMP) has emphasized strategically targeted conservation investments in regions that most affect waterfowl population dynamics. By 2012, regional conservation had progressively become more science-based and strategic: many migratory bird partnerships had initiated or completed projects on mapping and modeling waterfowl distribution and abundances using geospatial techniques. However, when developing a map depicting and titled “Areas of Greatest Continental Significance to North American Ducks, Geese, and Swans” for the 2012 NAWMP Revision, waterfowl professionals articulated the need for improved decision frameworks and use of consistent datasets for refining large-scale spatial products depicting priority areas for waterfowl and people. This report describes a framework for developing a spatial value model to support the identification of North American geographies of importance to waterfowl during the breeding and non-breeding periods and to resource users who could potentially support (financially and (or) politically) waterfowl habitat conservation. Objectives used to identify priority geographies were determined through a collaborative process of the NAWMP Science Support Team, Priority Landscapes Committee (PLC), and other experts in the fields of waterfowl biology and ecology, environmental science, and human dimensions. ArcGIS Desktop was used as the platform for managing, analyzing, combining and displaying the spatial data as well as producing new data through spatial analysis functions. Thirty-eight spatial layers were developed, and several composite spatially explicit products (maps of North America) were produced based on PLC recommendations. The composite products have extensive similarities to the 2012 NAWMP map depicting areas of greatest continental significance to North American waterfowl. There are also some differences, especially in regions of the high Arctic and in Mexico. These differences between spatial value model maps and the 2012 NAWMP output likely arose from inclusion of social objectives, reduced dependence on expert opinion to generate abundance estimates, lack of population surveys in some regions and availability of expanded survey data in other regions, and use of model-based waterfowl population estimates for some unsurveyed areas.
The structured decision-making framework application in this study is discussed, and the appropriate use of the products and their limitations are outlined. Additionally, options for future improvements are presented by identifying gaps in data collection, waterfowl-habitat association assumptions, and uncertainties related to social objectives. These spatial products are intended for use by national, regional, and province/state level wildlife professionals to aid their decisions in targeting waterfowl habitat conservation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191029","usgsCitation":"Krainyk, A., Lyons, J.E., Brasher, M.G., Humburg, D.D., Soulliere, G.J., Coluccy, J.M., Petrie, M.J., Howerter, D.W., Slattery, S.M., Rice, M.B., and Fuller, J.C., 2019, Spatial integration of biological and social objectives to identify priority landscapes for waterfowl habitat conservation: U.S. Geological Survey Open-File Report 2019–1029, 41 p., https://doi.org/10.3133/ofr20191029.","productDescription":"Document: vii, 41 p.; Additional Report Piece; Data Release","numberOfPages":"53","ipdsId":"IP-100747","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":363506,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L7J5U4","text":"USGS data release","description":"USGS data release","linkHelpText":"Spatial Integration of Biological and Social Objectives to Identify Priority Landscapes for Waterfowl Habitat Conservation"},{"id":363574,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2019/1029/ofr20191029_supplementalinformation.pdf","text":"Supplemental Information","size":"5.41 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":363503,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1029/coverthb2.jpg"},{"id":363504,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1029/ofr20191029.pdf","text":"Report","size":"30.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1029"}],"contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road, Ste 4039
Laurel, MD 20708
Median summer water yields and resultant flows for streams are used in Michigan to regulate large water withdrawals to help prevent negative effects on characteristic fish populations. Large water withdrawals commonly are associated with irrigation in rural areas. In an earlier statewide report, an index-flow statistic for the period of record, defined as the median flow during the summer month of lowest flow, was used to characterize median summer flows and associated water yields. In this report, the annual series of median summer water yields for the period July 1 through September 30 within the period of record is used to characterize median summer water yields. For 27 streamgages included in both reports, the average index water yield was at the 37th percentile of the distribution of median summer water yields. In contrast to an index statistic, an annual time series provides a basis for detecting trends in median summer water yields and for determining basin, climatic, and irrigation factors affecting spatial and temporal variations in summer water yields. Daily flow data from 40 selected U.S. Geological Survey streamgages in southwestern Michigan were used in this analysis. Two mixed models were identified to estimate median summer water yields based on fixed basin characteristics and temporally varying climatic factors for 1945–2015. No irrigation data were available prior to 1970, so no irrigation variables were included in the mixed models for 1945–2015. Then, two mixed models were developed for 1970–2015, a period in which a partial annual series of county-level irrigation data also were available. One of the 1970–2015 mixed models provides a basis for estimating median summer water yields at sites in southwestern Michigan using an estimated trend component, and selected basin, climatic, and irrigation factors. Re-estimation of model parameters in this mixed model with more spatially precise information on irrigation withdrawals may improve model accuracy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185071","collaboration":"Prepared in cooperation with the Michigan Department of Environmental Quality","usgsCitation":"Holtschlag, D.J., 2019, Basin, climatic, and irrigation factors associated with median summer water yields for streams in southwestern Michigan, 1945–2015: U.S. Geological Survey Scientific Investigations Report 2018–5071, 23 p., https://doi.org/10.3133/sir20185071.","productDescription":"Report: vii, 23 p.; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-093386","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":363517,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5071/sir20185071.pdf","text":"Report","size":"1.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5071"},{"id":363516,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5071/coverthb.jpg"},{"id":363518,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://www.sciencebase.gov/catalog/item/5c7d7268e4b0fe48cb532c2f","text":"USGS data release","description":"USGS data release","linkHelpText":"- Data on Factors Affecting Spatial and Temporal Variations of Annual Summer Median Water Yields in Southwestern Michigan, 1945-2015"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.671142578125,\n 41.599013054830216\n ],\n [\n -84.385986328125,\n 41.599013054830216\n ],\n [\n -84.385986328125,\n 43.30919109985686\n ],\n [\n -86.671142578125,\n 43.30919109985686\n ],\n [\n -86.671142578125,\n 41.599013054830216\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way, Suite 5
Lansing, MI 48911
1. Climate change has been identified as one of the most important drivers of wildlife populations. The development of appropriate conservation strategies relies on reliable predictions of population responses to climate change, which require in-depth understanding of the complex relationships between climate and population dynamics through density dependent demographic processes. Integrated population models (IPMs) are a type of modeling approach that unify the analyses of demography and abundance data, providing opportunities to understand and predict population demography and dynamics under climate change. 2. In this study we developed dynamic N-mixture models for large scale population estimates, which became an important component of the IPM we used in data analysis. We then analyzed four decades (1974-2014) of Mallard (Anas platyrhynchos) breeding population survey, band-recovery, and climate data covering a large spatial extent from North American prairies through boreal habitat to Alaska. Our goals were to examine the complex relationships among climate, density dependent processes, waterfowl population demography and dynamics, identify the key demographic parameters that are sensitive to climate change and are influential to population growth, and forecast population responses to climate change. 3. Our results revealed the interactive effects of temperature and density dependent processes on Mallard recruitment and to a less extent apparent survival. We also found that recruitment explained more variance of population growth than apparent survival. We then forecasted a decrease in Mallard breeding population density in the Northern Prairie Potholes and an increase in Mallard breeding population density in the northern part of our study area, indicating potential shifts in Mallard population dynamics under future climate change. 4. Synthesis and applications Different strategies need to be considered across regions to conserve waterfowl populations under climate change. Strategies that facilitate recruitment are essential for high-density populations that are relatively vulnerable to climate change. By contrast, low-density populations are relatively resilient to climate change and their habitats may serve as future climate refugia. Adaptive management is essential for evaluating management consequences. Our modelling framework approach can be easily adapted for other species and thus has wide applications in ecology and conservation.
","language":"English","publisher":"Wiley","doi":"10.1111/ecog.04548","usgsCitation":"Qing Zhao, Boomer, S., and Royle, A., 2019, Integrated modeling reveals shifts in waterfowl population dynamics under climate change: Ecography, v. 42, no. 9, p. 1470-1481, https://doi.org/10.1111/ecog.04548.","productDescription":"12 p.","startPage":"1470","endPage":"1481","ipdsId":"IP-102365","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":367131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -180.703125,\n 52.696361078274485\n ],\n [\n -172.08984375,\n 49.724479188712984\n ],\n [\n -147.48046875,\n 59.44507509904714\n ],\n [\n -127.96875,\n 60.1524422143808\n ],\n [\n -128.32031249999997,\n 47.39834920035926\n ],\n [\n -105.99609375,\n 47.87214396888731\n ],\n [\n -104.58984375,\n 44.33956524809713\n ],\n [\n -99.31640625,\n 43.96119063892024\n ],\n [\n -99.49218749999999,\n 48.80686346108517\n ],\n [\n -80.33203125,\n 48.80686346108517\n ],\n [\n -80.15625,\n 49.95121990866204\n ],\n [\n -80.15625,\n 52.482780222078226\n ],\n [\n -128.84765625,\n 70.90226826757711\n ],\n [\n -161.89453125,\n 73.17589717422607\n ],\n [\n -180.703125,\n 52.696361078274485\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"9","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Qing Zhao","contributorId":213383,"corporation":false,"usgs":false,"family":"Qing Zhao","affiliations":[{"id":38743,"text":"Univ. Missouri","active":true,"usgs":false}],"preferred":false,"id":769943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boomer, Scott","contributorId":218697,"corporation":false,"usgs":false,"family":"Boomer","given":"Scott","email":"","affiliations":[{"id":7199,"text":"US FWS","active":true,"usgs":false}],"preferred":false,"id":769944,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":769942,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70203063,"text":"sir20195028 - 2019 - Flood-inundation maps for the Amite and Comite Rivers from State Highway 64 to U.S. Highway 190 at Central, Louisiana","interactions":[],"lastModifiedDate":"2019-05-07T12:56:46","indexId":"sir20195028","displayToPublicDate":"2019-05-07T08:40:45","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5028","displayTitle":"Flood-Inundation Maps for the Amite and Comite Rivers From State Highway 64 To U.S. Highway 190 at Central, Louisiana","title":"Flood-inundation maps for the Amite and Comite Rivers from State Highway 64 to U.S. Highway 190 at Central, Louisiana","docAbstract":"Flood-inundation maps for a 14.5-mile reach of the Amite River and a 20.2-mile reach of the Comite River from State Highway 64 to U.S. Highway 190 were created by the U.S. Geological Survey (USGS) in cooperation with the City of Central, Louisiana. These maps, which can be accessed through an interactive mapper at the USGS Flood Inundation Mapping Program website and from a companion USGS data release, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgages Amite River at Magnolia, La., (07377300) and Comite River near Comite, La. (07378000).
Flood profiles were computed for the Amite and Comite River reaches by using the two-dimensional (2D), finite-volume numerical modeling options in the U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System (USACE HEC-RAS) software version 5.0.3. Models were calibrated to the current (2018) stage-discharge relations at the Amite River at Magnolia, La., and Comite River near Comite, La., streamgages, water-surface profiles from the March and August 2016 floods, and documented high-water marks from the flood of August 2016.
The hydraulic models were used to compute 37 individual water-surface profiles (21 for the Amite River and 16 for the Comite River) at 1.0-foot intervals ranging from the National Weather Service flood stage to the highest peak on record at the two streamgages. The 37 simulated water-surface profiles were used with a light detection and ranging-derived digital elevation model to delineate the flood extent and associated depth at each water level. The delineated areas (inundation maps) were merged into 127 combinations or possible flooding scenarios based on annual peak stage information from the two streamgaging stations.
The availability of these maps, along with real-time data delivered via the internet, 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 recovery efforts after floods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195028","collaboration":"Prepared in cooperation with the City of Central, Louisiana","usgsCitation":"Storm, J.B., 2019, Flood-inundation maps for the Amite and Comite Rivers from State Highway 64 to U.S. Highway 190 at Central, Louisiana: U.S. Geological Survey Scientific Investigations Report 2019–5028, 20 p., https://doi.org/10.3133/sir20195028.\n","productDescription":"Report: viii, 20 p.; Data Release; Flood Inundation Mapper","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-100404","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":363430,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5028/sir20195028.pdf","text":"Report","size":"6.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5028"},{"id":363431,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PQKSYF","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood Inundation Maps for the Amite and Comite Rivers from State Highway 64 to U.S. Highway 190 – City of Central, Louisiana"},{"id":363429,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5028/coverthb.jpg"},{"id":363432,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program?qt-science_center_objects=0#qt-science_center_objects","text":"Flood Inundation Mapper","description":"Flood Inundation Mapper"}],"country":"United States","state":"Louisiana ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.3123779296875,\n 28.859107573773\n ],\n [\n -93.3123779296875,\n 31.475524020001806\n ],\n [\n -89.879150390625,\n 31.475524020001806\n ],\n [\n -89.879150390625,\n 28.859107573773\n ],\n [\n -93.3123779296875,\n 28.859107573773\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Ste 100
Nashville, TN 37211