{"pageNumber":"230","pageRowStart":"5725","pageSize":"25","recordCount":46677,"records":[{"id":70215379,"text":"70215379 - 2020 - Four decades of land-cover change on the Kenai Peninsula, Alaska: Detecting disturbance-influenced vegetation shifts using landsat legacy data","interactions":[],"lastModifiedDate":"2020-10-16T11:48:29.969933","indexId":"70215379","displayToPublicDate":"2020-10-09T06:45:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Four decades of land-cover change on the Kenai Peninsula, Alaska: Detecting disturbance-influenced vegetation shifts using landsat legacy data","docAbstract":"
Across Alaska’s Kenai Peninsula, disturbance events have removed large areas of forest over the last half century. Simultaneously, succession and landscape evolution have facilitated forest regrowth and expansion. Detecting forest loss within known pulse disturbance events is often straightforward given that reduction in tree cover is a readily detectable and measurable land-cover change. Land-cover change is more difficult to quantify when disturbance events are unknown, remote, or environmental response is slow in relation to human observation. While disturbance events and related land-cover change are relatively instant, assessing patterns of post-disturbance succession requires long term monitoring. Here, we describe a method for classifying land cover and quantifying land-cover change over time, using Landsat legacy imagery for three historical eras on the western Kenai Peninsula: 1973–2002, 2002–2017, and 1973–2017. Scenes from numerous Landsat sensors, including summer and winter seasons, were acquired between 1973 and 2017 and used to classify vegetation cover using a random forest classifier. Land-cover type was summarized by era and combined to produce a dataset capturing spatially explicit land-cover change at a moderate 30-m resolution. Our results document large-scale forest loss across the study area that can be attributed to known disturbance events including beetle kill and wildfire. Despite numerous and extensive disturbances resulting in forest loss, we estimate that the study area has experienced net forest gain over the duration of our study period due to reforestation within large fire events that predate this study. Transition between forest and graminoid non-forest land cover including wetlands and herbaceous uplands is the most common land-cover change—representing recruitment of a graminoid dominated understory following forest loss and the return of forest canopy given sufficient time post-disturbance. 
","language":"English","publisher":"MDPI","doi":"10.3390/land9100382","usgsCitation":"Baughman, C., Loehman, R.A., Magness, D.R., Saperstein, L., and Sherriff, R., 2020, Four decades of land-cover change on the Kenai Peninsula, Alaska: Detecting disturbance-influenced vegetation shifts using landsat legacy data: Land, v. 9, no. 10, 382, 22 p., https://doi.org/10.3390/land9100382.","productDescription":"382, 22 p.","ipdsId":"IP-116240","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":455092,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land9100382","text":"Publisher Index Page"},{"id":436759,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92BGHW1","text":"USGS data release","linkHelpText":"Land Cover Estimates for the Kenai Peninsula Lowlands; 1973, 2002, and 2017"},{"id":379452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kenai Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.10546875,\n 59.0405546167585\n ],\n [\n -147.48046875,\n 59.0405546167585\n ],\n [\n -147.48046875,\n 61.52269494598361\n ],\n [\n -153.10546875,\n 61.52269494598361\n ],\n [\n -153.10546875,\n 59.0405546167585\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-10-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Baughman, Carson 0000-0002-9423-9324 cbaughman@usgs.gov","orcid":"https://orcid.org/0000-0002-9423-9324","contributorId":169657,"corporation":false,"usgs":true,"family":"Baughman","given":"Carson","email":"cbaughman@usgs.gov","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":801904,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loehman, Rachel A. 0000-0001-7680-1865 rloehman@usgs.gov","orcid":"https://orcid.org/0000-0001-7680-1865","contributorId":187605,"corporation":false,"usgs":true,"family":"Loehman","given":"Rachel","email":"rloehman@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":false,"id":801905,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Magness, Dawn R.","contributorId":243262,"corporation":false,"usgs":false,"family":"Magness","given":"Dawn","email":"","middleInitial":"R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":801907,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saperstein, Lisa","contributorId":218974,"corporation":false,"usgs":false,"family":"Saperstein","given":"Lisa","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":801906,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sherriff, Rosemary L.","contributorId":243263,"corporation":false,"usgs":false,"family":"Sherriff","given":"Rosemary L.","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":801908,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228541,"text":"70228541 - 2020 - Methods for estimating vital rates of greater sage-grouse broods: A review","interactions":[],"lastModifiedDate":"2022-02-14T20:52:41.251421","indexId":"70228541","displayToPublicDate":"2020-10-08T15:52:18","publicationYear":"2020","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":"Methods for estimating vital rates of greater sage-grouse broods: A review","docAbstract":"

Biologists use a variety of methods to estimate productivity and resource selection of birds. The effectiveness and suitability of each method depends on the study's objectives, but is also influenced by many important traits, including detection probability, disturbance of focal birds and sampling frequency. We reviewed 504 greater sage-grouse Centrocercus urophasianus papers published from 1990 to 2019 to document the most common brood survey methods used by investigators and summarized if and how they used brood survey data to estimate brood survival and detection probability. Of the 504 papers, 16.1% (n = 81) had useful information relevant to the review. The most common methods included daytime visual surveys (46.9%; n = 38), daytime flush surveys (33.3%; n = 27), nocturnal spotlight surveys (19.8%; n = 16), radio-tagged chicks (16.0%; n = 13), wing surveys (9.9%; n = 8), brood routes (4.9%; n = 4) and pointing dogs (4.9%; n = 4). Fifty-nine of the 81 papers used >1 method, only 2 of the 81 papers measured or reported detection probability, and none reported the level of disturbance caused by the method. Studies varied widely regarding the age of the brood when brood fate was confirmed (x̄ = 44.4 days post-hatch, range 14–84 days). The frequency of brood sampling visits also varied greatly among studies (range = 1.19–3.85 surveys/brood/week) and this variation complicates comparison in fecundity and survival estimates across studies. Furthermore, 35 papers used >1 maternal behavior as purported indicators of brood fate, but none of them documented how accurate their indicators were. Future studies could reduce variance in estimates of sage-grouse fecundity and brood survival by employing empirical methods to estimate detection probability, standardizing brood sampling methods and conducting trials to document the effects of hen or brood capture, handling and flushing on brood survival estimates. Moreover, the accuracy of commonly used indicators of brood fate, including maternal behaviors, flocking behavior and distance moved after flush needs verification.

","language":"English","publisher":"BioOne","doi":"10.2981/wlb.00700","usgsCitation":"Riley, I.P., and Conway, C.J., 2020, Methods for estimating vital rates of greater sage-grouse broods: A review: Wildlife Biology, v. 4, wlb.00700, 12 p., https://doi.org/10.2981/wlb.00700.","productDescription":"wlb.00700, 12 p.","ipdsId":"IP-117997","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":455093,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2981/wlb.00700","text":"Publisher Index Page"},{"id":395937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","noUsgsAuthors":false,"publicationDate":"2020-10-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Riley, Ian P.","contributorId":272044,"corporation":false,"usgs":false,"family":"Riley","given":"Ian","email":"","middleInitial":"P.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":834835,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834534,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70205608,"text":"sim3443 - 2020 - Geologic map of the greater Portland metropolitan area and surrounding region, Oregon and Washington","interactions":[],"lastModifiedDate":"2022-03-10T17:43:21.40102","indexId":"sim3443","displayToPublicDate":"2020-10-08T09:21:19","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3443","displayTitle":"Geologic Map of the Greater Portland Metropolitan Area and Surrounding Region, Oregon and Washington","title":"Geologic map of the greater Portland metropolitan area and surrounding region, Oregon and Washington","docAbstract":"

The Portland-Vancouver-Hillsboro Metropolitan Area (metro area) has great scenic, natural, and cultural resources and is the major economic hub of Oregon. The metro area is subject to a variety of geologic hazards. Underthrusting of the oceanic plate along the Cascadia plate boundary fault, or megathrust, deforms the leading edge of North America and produces earthquakes on the megathrust and in the overlying plate. Rising magma from the deeper parts of the subduction zone produces active volcanoes that form the Cascades Arc, including Mount Hood and Mount St. Helens visible from Portland. Both volcanism and strong ground-shaking from earthquakes have impacted the metro area, most recently in the 1980 eruptions of Mount St. Helens and the 1993 magnitude (M) 5.7 Scotts Mills earthquake. Great offshore earthquakes as large as M 9 on the Cascadia megathrust have shaken the metro area every 500 years or so, most recently in 1700. Giant floods have inundated the metro area, from the ice age Missoula floods about 20,000 to 15,000 years ago to the flood generated by collapse of the Bridge of the Gods landslide dam on the Columbia River around 1421–1447 A.D.

Geologic resources of the metro area include the southern part of the Mist Natural Gas Storage Field in the northwest corner of the map area, the Columbia South Shore Well Field aquifer in the Portland Basin, the Columbia River Basalt aquifer of the Tualatin Basin, and the Tualatin Basin Aquifer Storage and Recovery projects. The metro area includes several well-known American Viticultural Areas in the western part of the map area and numerous transportation, electrical transmission, and pipeline corridors.

We created this map to provide a uniform, modern geologic database for the greater Portland metro area to better understand its tectonic setting, active faults, volcanoes, landslide hazards, and distribution of geologic materials and resources. Information in this database will be used to improve seismic hazard and resource assessments in this economically important region.

NOTE: The sheet 1 map was divided into two parts—sheet 1 (north) and sheet 1 (south)—to facilitate printing and plotting the map.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3443","collaboration":"Prepared in Cooperation with Oregon Department of Geology and Mineral Industries and Washington Geological Survey","usgsCitation":"Wells, R.E., Haugerud, R.A., Niem, A.R., Niem, W.A., Ma, L., Evarts, R.C., O’Connor, J.E., Madin, I.P., Sherrod, D.R., Beeson, M.H., Tolan, T.L., Wheeler, K.L., Hanson, W.B., and Sawlan, M.G., 2020, Geologic map of the greater Portland metropolitan area and surrounding region, Oregon and Washington: U.S. Geological Survey Scientific Investigations Map 3443, pamphlet 55 p., 2 sheets, scale 1:63,360, https://doi.org/10.3133/sim3443.","productDescription":"Pamphlet: iv, 55 p.; 2 Sheets: 58.43 x 60.16 inches and 38.76 x 30.86 inches; Table 3; Database; Metadata; Read Me","numberOfPages":"55","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081424","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":396997,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3443/sim3443_sheet1_south.pdf","text":"Sheet 1 South","size":"55 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- NOTE: The sheet 1 map was divided into two parts—sheet 1 (north) and sheet 1 (south)—to facilitate printing and plotting the map."},{"id":396996,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3443/sim3443_sheet1_north.pdf","text":"Sheet 1 North","size":"55 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- NOTE: The sheet 1 map was divided into two parts—sheet 1 (north) and sheet 1 (south)—to facilitate printing and plotting the map."},{"id":376987,"rank":8,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3443/database","text":"Database directory"},{"id":376984,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3443/metadata","text":"Metadata directory"},{"id":376983,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3443/sim3443_table3.xlsx","text":"Table 3","size":"110 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":376982,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3443/sim3443_sheet2.pdf","text":"Sheet 2","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376981,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3443/sim3443_sheet1.pdf","text":"Sheet 1","size":"80 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376980,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3443/sim3443_readme.txt","size":"5 KB","linkFileType":{"id":2,"text":"txt"}},{"id":376979,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3443/sim3443_pamphlet.pdf","text":"Pamphlet","size":"28 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376953,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3443/covrthb.jpg"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Greater Portland metropolitan area and surrounding region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.20617675781251,\n 44.95702412512118\n ],\n [\n -122.0855712890625,\n 44.95702412512118\n ],\n [\n -122.0855712890625,\n 46.145588688591964\n ],\n [\n -123.20617675781251,\n 46.145588688591964\n ],\n [\n -123.20617675781251,\n 44.95702412512118\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2020-10-08","noUsgsAuthors":false,"publicationDate":"2020-10-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Wells, Ray E. 0000-0002-7796-0160 rwells@usgs.gov","orcid":"https://orcid.org/0000-0002-7796-0160","contributorId":149772,"corporation":false,"usgs":true,"family":"Wells","given":"Ray","email":"rwells@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":771833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haugerud, Ralph A. 0000-0001-7302-4351 rhaugerud@usgs.gov","orcid":"https://orcid.org/0000-0001-7302-4351","contributorId":2691,"corporation":false,"usgs":true,"family":"Haugerud","given":"Ralph","email":"rhaugerud@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":771838,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niem, Alan R.","contributorId":219264,"corporation":false,"usgs":false,"family":"Niem","given":"Alan","email":"","middleInitial":"R.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":771839,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Niem, Wendy A.","contributorId":219265,"corporation":false,"usgs":false,"family":"Niem","given":"Wendy","email":"","middleInitial":"A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":771840,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ma, Lina","contributorId":204020,"corporation":false,"usgs":false,"family":"Ma","given":"Lina","email":"","affiliations":[{"id":32397,"text":"Oregon Department of Geology and Mineral Industries","active":true,"usgs":false}],"preferred":false,"id":771834,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Evarts, Russell C. 0000-0001-5103-9085","orcid":"https://orcid.org/0000-0001-5103-9085","contributorId":219266,"corporation":false,"usgs":false,"family":"Evarts","given":"Russell C.","affiliations":[],"preferred":false,"id":771841,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O'Connor, Jim E. 0000-0002-7928-5883 oconnor@usgs.gov","orcid":"https://orcid.org/0000-0002-7928-5883","contributorId":140771,"corporation":false,"usgs":true,"family":"O'Connor","given":"Jim E.","email":"oconnor@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":771837,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Madin, Ian P.","contributorId":66404,"corporation":false,"usgs":true,"family":"Madin","given":"Ian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":771835,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sherrod, David R. 0000-0001-9460-0434 dsherrod@usgs.gov","orcid":"https://orcid.org/0000-0001-9460-0434","contributorId":527,"corporation":false,"usgs":true,"family":"Sherrod","given":"David","email":"dsherrod@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":771836,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Beeson, Marvin H.","contributorId":219267,"corporation":false,"usgs":false,"family":"Beeson","given":"Marvin","email":"","middleInitial":"H.","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":771842,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Tolan, Terry L.","contributorId":219268,"corporation":false,"usgs":false,"family":"Tolan","given":"Terry","email":"","middleInitial":"L.","affiliations":[{"id":39980,"text":"Intera Geoscience and Engineering Solutions","active":true,"usgs":false}],"preferred":false,"id":771843,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wheeler, Karen L.","contributorId":219269,"corporation":false,"usgs":false,"family":"Wheeler","given":"Karen L.","affiliations":[],"preferred":false,"id":771844,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hanson, William B.","contributorId":219270,"corporation":false,"usgs":false,"family":"Hanson","given":"William","email":"","middleInitial":"B.","affiliations":[{"id":39981,"text":"private consultant","active":true,"usgs":false}],"preferred":false,"id":771845,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Sawlan, Michael G. 0000-0003-0637-2051 msawlan@usgs.gov","orcid":"https://orcid.org/0000-0003-0637-2051","contributorId":2291,"corporation":false,"usgs":true,"family":"Sawlan","given":"Michael","email":"msawlan@usgs.gov","middleInitial":"G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":771846,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70214611,"text":"sim3463 - 2020 - Bathymetry of Deadmans Lake, Golf Course Reservoir 9, Ice Lake, Kettle Lakes 1–3, and Non-Potable Reservoirs 1–4 at the U.S. Air Force Academy, Colorado, 2019","interactions":[],"lastModifiedDate":"2020-10-07T23:40:27.941947","indexId":"sim3463","displayToPublicDate":"2020-10-07T15:35:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3463","displayTitle":"Bathymetry of Deadmans Lake, Golf Course Reservoir 9, Ice Lake, Kettle Lakes 1–3, and Non-Potable Reservoirs 1–4 at the U.S. Air Force Academy, Colorado, 2019","title":"Bathymetry of Deadmans Lake, Golf Course Reservoir 9, Ice Lake, Kettle Lakes 1–3, and Non-Potable Reservoirs 1–4 at the U.S. Air Force Academy, Colorado, 2019","docAbstract":"

The U.S. Geological Survey, in cooperation with the U.S. Air Force Academy (USAFA), carried out bathymetric and topographic surveys to characterize the volume of Deadmans Lake, Golf Course Reservoir 9, Ice Lake, Kettle Lakes 1–3, and Non-Potable Reservoirs 1–4 at the U.S. Air Force Academy, Colorado. Bathymetric maps of each lake and reservoir are presented with figures of the elevation-volume curves. The bathymetric surveys were carried out from October 15, 2019, to December 12, 2019, using a manually operated, boat-mounted, single-beam echo sounder integrated with a Real-Time Kinematic Global Navigation Satellite Systems receiver. Topographic surveys were carried out during the same time period using Real-Time Kinematic Global Navigation Satellite System to collect elevation data at and above the water surface and up to the elevation of the dam or spillway at the time of the surveys. The topographic and bathymetric datasets were imported into Esri ArcMap 10.7.1. The combined survey points were then interpolated into digital elevation models, which were used to determine lake or reservoir volumes that correspond to water-surface elevations between the lakebed and the approximate top of the dam or spillway.

This report provides an updated characterization of storage capacity and improved understanding of present (2019) water capacity in the lakes and reservoirs at the USAFA. In addition, these surveys serve as a baseline that could be compared with future surveys of the lakes and reservoirs. The differences in these and future surveys could then be used to determine sedimentation infill rates and provide estimates of the lifespan of the lakes and reservoirs.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3463","collaboration":"Prepared in cooperation with the U.S. Air Force Academy","usgsCitation":"Kohn, M.S., and Hempel, L.A., 2020, Bathymetry of Deadmans Lake, Golf Course Reservoir 9, Ice Lake, Kettle Lakes 1–3, and Non-Potable Reservoirs 1–4 at the U.S. Air Force Academy, Colorado, 2019: U.S. Geological Survey Scientific Investigations Map 3463, pamphlet 12 p., https://doi.org/10.3133/sim3463.","productDescription":"Pamphlet: vi, 12 p.; 1 Sheet: 36.00 x 32.00 inches; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-114390","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":378914,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3463/coverthb.jpg"},{"id":378915,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3463/sim3463_map.pdf","text":"Map","size":"9.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3463"},{"id":378917,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LTH0RO","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Survey and Bathymetric Data of Deadmans Lake, Golf Course Reservoir 9, Ice Lake, Kettle Lakes 1-3, and Non-Potable Reservoirs 1-4 at the U.S. Air Force Academy, Colorado, 2019 (ver. 1.1, June 2020)"},{"id":378916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3463/sim3463_pamphlet.pdf","text":"Pamphlet","size":"1.17 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Colorado","city":"Colorado Springs","otherGeospatial":"U.S. Airforce Academy","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.91188049316406,\n 38.91561302513129\n ],\n [\n -104.77867126464842,\n 38.93163900447185\n ],\n [\n -104.8267364501953,\n 39.03731965210478\n ],\n [\n -104.92767333984374,\n 39.03731965210478\n ],\n [\n -104.91188049316406,\n 38.91561302513129\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225

Contact Pubs Warehouse

","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2020-10-07","noUsgsAuthors":false,"publicationDate":"2020-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Kohn, Michael S. 0000-0002-5989-7700 mkohn@usgs.gov","orcid":"https://orcid.org/0000-0002-5989-7700","contributorId":4549,"corporation":false,"usgs":true,"family":"Kohn","given":"Michael","email":"mkohn@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":800222,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hempel, Laura A. 0000-0001-5020-6056","orcid":"https://orcid.org/0000-0001-5020-6056","contributorId":224286,"corporation":false,"usgs":true,"family":"Hempel","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":800223,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218453,"text":"70218453 - 2020 - Habitat characterization and species distribution model of the only large-lake population of the endangered Silver Chub (Macrhybopsis storeriana, Kirtland 1844)","interactions":[],"lastModifiedDate":"2021-02-26T13:59:53.610438","indexId":"70218453","displayToPublicDate":"2020-10-07T07:55:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Habitat characterization and species distribution model of the only large-lake population of the endangered Silver Chub (Macrhybopsis storeriana, Kirtland 1844)","docAbstract":"

The endangered Silver Chub (Macrhybopsis storeriana, Kirtland 1844) is native to North America and primarily riverine, with the only known large‐lake population in Lake Erie. Once a major component of the Lake Erie fish community, it declined and became nearly extirpated in the mid‐1900s. Recent collections in western Lake Erie suggest that Silver Chub may be able to recover, but their habitat and distribution are poorly known. A recent work showed an extensive area of western Lake Erie with the potential to support large numbers of Silver Chub, but was based on a geographically limited dataset. We developed a neural network‐based species distribution model for the Silver Chub in western Lake Erie, improved by new synoptic data and using habitat variables resistant to anthropogenic activities. The Potential model predictions were compared with a model that included anthropogenic‐sensitive variables. The Potential model used 10 habitat variables and performed well, explaining > 99% of data variation and had generally low error rates. Predictions indicated that a large area of the waters approximately 2–9 m deep contained Appropriate habitat and the highest abundances should be supported by habitat in a wide arc through the western end of the basin. The model indicated that Appropriate Silver Chub habitat was associated with relatively deep water, near coastal wetlands, where effective fetch is less than average. Disturbance model predictions were similar, but predicted poorer Silver Chub habitat in more areas than that predicted by the Potential model. Our Potential model reveals Appropriate habitat conditions for Silver Chub and its spatial distribution, indicating that extensive areas of western Lake Erie could support Silver Chub. Comparisons with Disturbance model predictions demonstrate that Potential model predictions may be used in conjunction with analyses of degrading conditions in the system to better conserve and manage for this endangered species.

","language":"English","publisher":"Wiley","doi":"10.1002/ece3.6830","usgsCitation":"McKenna, J.E., and Kocovsky, P., 2020, Habitat characterization and species distribution model of the only large-lake population of the endangered Silver Chub (Macrhybopsis storeriana, Kirtland 1844): Ecology and Evolution, v. 10, no. 21, p. 12076-12090, https://doi.org/10.1002/ece3.6830.","productDescription":"15 p.","startPage":"12076","endPage":"12090","ipdsId":"IP-062648","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":455100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.6830","text":"Publisher Index Page"},{"id":383637,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ohio","otherGeospatial":"Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.8916015625,\n 41.062786068733026\n ],\n [\n -81.05712890625,\n 41.062786068733026\n ],\n [\n -81.05712890625,\n 42.601619944327965\n ],\n [\n -83.8916015625,\n 42.601619944327965\n ],\n [\n -83.8916015625,\n 41.062786068733026\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"21","noUsgsAuthors":false,"publicationDate":"2020-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, James E. Jr. 0000-0002-1428-7597 jemckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":195894,"corporation":false,"usgs":true,"family":"McKenna","given":"James","suffix":"Jr.","email":"jemckenna@usgs.gov","middleInitial":"E.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":810978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kocovsky, Patrick 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":150837,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":810979,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215189,"text":"70215189 - 2020 - A novel approach for next generation water use mapping using Landsat and Sentinel-2 satellite data","interactions":[],"lastModifiedDate":"2020-10-29T15:15:46.334579","indexId":"70215189","displayToPublicDate":"2020-10-07T07:27:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1927,"text":"Hydrological Sciences Journal","active":true,"publicationSubtype":{"id":10}},"title":"A novel approach for next generation water use mapping using Landsat and Sentinel-2 satellite data","docAbstract":"

Evapotranspiration (ET) is needed in a range of applications in hydrology, climatology, ecology, and agriculture. Remote sensing-based estimation is the only viable and economical method for ET estimation over large areas. The current Landsat satellites provide images every 16 days limiting the ability to capture biophysical changes affecting ET. Thus, we explored the potential integration of Landsat 8 and Sentinel-2 data for estimating ET using a surface energy balance model. The results indicate the proposed Landsat-Sentinel data fusion approach substantially reduced relative errors from 48% to 10% on area-wide and from 49% to 17% on pixel-wide compared to linear interpolation between two Landsat images. The proposed approach had a better agreement with expected actual ET maps across high-vegetation conditions than in low-vegetation conditions. The finer temporal resolution and better accuracy of ET maps based on Landsat-Sentinel integration is of great importance in managing limited water resources.

","language":"English","publisher":"Taylor and Francis","doi":"10.1080/02626667.2020.1817461","usgsCitation":"Singh, R., Khand, K.B., Kagone, S., Schauer, M., Senay, G., and Wu, Z., 2020, A novel approach for next generation water use mapping using Landsat and Sentinel-2 satellite data: Hydrological Sciences Journal, v. 65, no. 14, p. 2508-2519, https://doi.org/10.1080/02626667.2020.1817461.","productDescription":"12 p.","startPage":"2508","endPage":"2519","ipdsId":"IP-113350","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":455102,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02626667.2020.1817461","text":"Publisher Index Page"},{"id":379288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California","otherGeospatial":"Palo Verde Irrigation District","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.9444580078125,\n 32.9764120829052\n ],\n [\n -114.3402099609375,\n 32.9764120829052\n ],\n [\n -114.3402099609375,\n 33.911454454267606\n ],\n [\n -114.9444580078125,\n 33.911454454267606\n ],\n [\n -114.9444580078125,\n 32.9764120829052\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"65","issue":"14","noUsgsAuthors":false,"publicationDate":"2020-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Singh, Ramesh 0000-0002-8164-3483","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":210983,"corporation":false,"usgs":true,"family":"Singh","given":"Ramesh","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":801106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Khand, Kul Bikram 0000-0002-1593-1508","orcid":"https://orcid.org/0000-0002-1593-1508","contributorId":242921,"corporation":false,"usgs":true,"family":"Khand","given":"Kul","email":"","middleInitial":"Bikram","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":801107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":216913,"corporation":false,"usgs":true,"family":"Kagone","given":"Stefanie","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":801108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schauer, Matthew 0000-0002-4198-3379","orcid":"https://orcid.org/0000-0002-4198-3379","contributorId":216909,"corporation":false,"usgs":true,"family":"Schauer","given":"Matthew","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":801109,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":801110,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":801111,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215231,"text":"70215231 - 2020 - Spatiotemporal pattern of interactions between an apex predator and sympatric species","interactions":[],"lastModifiedDate":"2020-12-14T16:39:26.143997","indexId":"70215231","displayToPublicDate":"2020-10-07T07:04:59","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7153,"text":"Journal of Mammology","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal pattern of interactions between an apex predator and sympatric species","docAbstract":"

Increases in apex predator abundance can influence the behavior of sympatric species, particularly when the available habitat and/or resources are limited. We assessed the temporal and spatiotemporal interactions between Florida panthers (Puma concolor coryi) and six focal sympatric species in South Florida, where Florida panther abundance has increased by more than 6-fold since the 1990’s. Using camera trap data, we quantified species’ diel activity patterns, temporal overlap, and time-to-encounter (i.e., time between consecutive visits of a Florida panther and a focal species and vice versa). The Florida panther and bobcat (Lynx rufus) displayed a nocturnal activity pattern; the black bear (Ursus americanus), white-tailed deer (Odocoileus virginianus), wild boar (Sus scrofa), and wild turkey (Meleagris gallopavo) were mostly diurnal; and the raccoon (Procyon lotor) was cathemeral. Prey species and black bears minimized encounters with Florida panthers by being active during the day and displaying longer time-to-encounter, whereas Florida panthers visited a site after a prey species at higher probabilities than after competitor species, and were more likely to visit an elevated site or upland habitat. Our results suggest that interactions between Florida panthers and sympatric species in our study system are driven by species-specific behavioral responses. Gaining a better understanding of the crucial interactions driving species coexistence is important for a better understanding of the structure and function of ecological communities and help manage the potential expansion of the Florida panther into Central Florida.

","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyaa071","usgsCitation":"Guitart, M.P., Onorato, D.P., Hines, J.E., and Oli, M.K., 2020, Spatiotemporal pattern of interactions between an apex predator and sympatric species: Journal of Mammology, v. 101, no. 5, p. 1279-1288, https://doi.org/10.1093/jmammal/gyaa071.","productDescription":"10 p.","startPage":"1279","endPage":"1288","ipdsId":"IP-102728","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":379342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Central Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.8369140625,\n 26.31311263768267\n ],\n [\n -79.8046875,\n 26.31311263768267\n ],\n [\n -79.8046875,\n 29.611670115197377\n ],\n [\n -82.8369140625,\n 29.611670115197377\n ],\n [\n -82.8369140625,\n 26.31311263768267\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"101","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Guitart, Marta P 0000-0002-2368-1422","orcid":"https://orcid.org/0000-0002-2368-1422","contributorId":242968,"corporation":false,"usgs":false,"family":"Guitart","given":"Marta","email":"","middleInitial":"P","affiliations":[{"id":38084,"text":"Univ. of Florida","active":true,"usgs":false}],"preferred":false,"id":801239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Onorato, David P","contributorId":242969,"corporation":false,"usgs":false,"family":"Onorato","given":"David","email":"","middleInitial":"P","affiliations":[{"id":48592,"text":"Florida Fish & Wildlife Conservation Comm.","active":true,"usgs":false}],"preferred":false,"id":801240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":801241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oli, Madan K. 0000-0001-6944-0061","orcid":"https://orcid.org/0000-0001-6944-0061","contributorId":201302,"corporation":false,"usgs":false,"family":"Oli","given":"Madan","email":"","middleInitial":"K.","affiliations":[{"id":13453,"text":"University of Florida, Gainesville, FL","active":true,"usgs":false}],"preferred":false,"id":801242,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70213240,"text":"sir20205054 - 2020 - Estimating flood magnitude and frequency on streams and rivers in Connecticut, based on data through water year 2015","interactions":[],"lastModifiedDate":"2020-10-06T21:49:41.168644","indexId":"sir20205054","displayToPublicDate":"2020-10-06T16:00:00","publicationYear":"2020","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":"2020-5054","displayTitle":"Estimating Flood Magnitude and Frequency on Streams and Rivers in Connecticut, Based on Data Through Water Year 2015","title":"Estimating flood magnitude and frequency on streams and rivers in Connecticut, based on data through water year 2015","docAbstract":"

The U.S. Geological Survey, in cooperation with the Connecticut Department of Transportation, updated flood-frequency estimates with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively) for 141 streamgages in Connecticut and 11 streamgages in adjacent States using annual peak-flow data through water year 2015. Peak-flow regression equations were derived for estimating flows at ungaged stream sites with annual exceedance probabilities from 50 to 0.2 percent. Methods for estimating prediction intervals for the peak-flow regression equations are presented. The regression equations are applicable for basins in Connecticut with drainage areas ranging from 0.69 to 325 square miles that are not affected by flood-control regulation or flow diversions.

The flood discharges for select annual exceedance probabilities were estimated following new (2018) national guidelines for flood-frequency analyses. New guidelines have improved statistical methods for flood-frequency analysis including (1) the expected moments algorithm to help describe uncertainty in annual peak flows and to better represent missing and historical record and (2) the generalized multiple Grubbs-Beck test to screen out potentially influential low outliers and to better fit the upper end of the peak-flow distribution. Additionally, a new regional skew (0.37) derived for New England was used in the flood-frequency analysis for the streamgages.

Annual peak flows were analyzed for trends for four time periods (30, 50, 70, and 90 years) through 2015. Trend results show some statistical evidence of increasing peak flows in each of the time periods analyzed; however, multidecadal climate cycles may be influencing the number and magnitude of the trends. Historical peak-flow trends in and near Connecticut do not offer clear and convincing evidence for incorporating trends into flood-frequency analyses. For this study, the traditional assumption of stationarity is used with no adjustment for trends.

Generalized least squares regression techniques were used to develop the final set of multivariable regression equations for estimating flood discharges with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities. The standard error of prediction for the regional regression equations ranged from 26.3 to 45.0 percent. The standard error of prediction was slightly smaller in the current study compared to the 2004 study, indicating an improvement in the predictive ability of the equations (6 percent smaller at the 50-percent annual exceedance probability to about 1 percent smaller at the 1-percent annual exceedance probability). Generalized least squares regression techniques also were used to develop a one-variable (drainage-area-only) equation. Drainage-area-only equations can be used as an alternative to the multiexplanatory variable statewide regression equations if decreased accuracy is acceptable.

The revised statistical procedures and additional streamgage data applied in the current study result in a more accurate representation of peak-flow conditions in Connecticut than was previously available. The regional regression equations will be integrated in the U.S. Geological Survey StreamStats program, which estimates basin and climatic characteristics and streamflow statistics at user-selected ungaged stream sites.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205054","collaboration":"Prepared in cooperation with the Connecticut Department of Transportation","usgsCitation":"Ahearn, E.A., and Hodgkins, G.A., 2020, Estimating flood magnitude and frequency on streams and rivers in Connecticut, based on data through water year 2015: U.S. Geological Survey Scientific Investigations Report 2020–5054, 42 p., https://doi.org/10.3133/sir20205054.","productDescription":"Report: v, 42 p.; 2 Tables; 2 Data Releases","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108818","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":378387,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EWHAYW","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Worksheet for computing annual exceedance probability flood discharges and prediction intervals at stream sites in Connecticut"},{"id":378386,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S4F751","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Flood frequency and source data used in regional regression analysis of annual peak flows in Connecticut"},{"id":378381,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5054/coverthb.png"},{"id":378382,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5054/sir20205054.pdf","text":"Report","size":"6.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5054"},{"id":378383,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5054/sir20205054_table01.xlsx","text":"Table 1","size":"35.3 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Descriptions of U.S. Geological Survey streamgages in Connecticut and adjacent States used in the flood-frequency analysis and regionalization of peaks flows in Connecticut"},{"id":378384,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5054/sir20205054_table01.csv","text":"Table 1","size":"33.5 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Descriptions of U.S. Geological Survey streamgages in Connecticut and adjacent States used in the flood-frequency analysis and regionalization of peaks flows in Connecticut"}],"country":"United States","state":"Connecticut","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-71.799242,42.008065],[-71.797922,41.935395],[-71.797649,41.928556],[-71.794161,41.841101],[-71.794161,41.840141],[-71.792786,41.80867],[-71.792767,41.807001],[-71.791062,41.770273],[-71.789678,41.724734],[-71.789672,41.724569],[-71.786994,41.655992],[-71.787637,41.639917],[-71.789356,41.59691],[-71.789359,41.596852],[-71.797683,41.416709],[-71.81839,41.419599],[-71.839649,41.412119],[-71.842563,41.409855],[-71.843472,41.40583],[-71.842131,41.395359],[-71.833443,41.384524],[-71.831613,41.370899],[-71.837738,41.363529],[-71.835951,41.353935],[-71.829595,41.344544],[-71.839013,41.334042],[-71.860513,41.320248],[-71.859566,41.3224],[-71.868235,41.330941],[-71.886302,41.33641],[-71.91671,41.332217],[-71.922092,41.334518],[-71.923282,41.335113],[-71.936284,41.337959],[-71.945652,41.337799],[-71.956747,41.329871],[-71.970955,41.324526],[-71.979447,41.329987],[-71.982194,41.329861],[-71.988153,41.320577],[-72.021898,41.316838],[-72.084487,41.319634],[-72.094443,41.314164],[-72.09982,41.306998],[-72.11182,41.299098],[-72.134221,41.299398],[-72.16158,41.310262],[-72.173922,41.317597],[-72.177622,41.322497],[-72.184122,41.323997],[-72.191022,41.323197],[-72.201422,41.315697],[-72.203022,41.313197],[-72.204022,41.299097],[-72.212924,41.291365],[-72.225276,41.299047],[-72.235531,41.300413],[-72.248161,41.299488],[-72.251895,41.29862],[-72.250515,41.294386],[-72.251323,41.289997],[-72.261487,41.282926],[-72.31776,41.277782],[-72.327595,41.27846],[-72.333894,41.282916],[-72.34146,41.28011],[-72.348643,41.277446],[-72.348068,41.269698],[-72.386629,41.261798],[-72.398688,41.278172],[-72.40593,41.278398],[-72.451925,41.278885],[-72.472539,41.270103],[-72.485693,41.270881],[-72.499534,41.265866],[-72.506634,41.260099],[-72.51866,41.261253],[-72.521312,41.2656],[-72.529416,41.264421],[-72.533247,41.26269],[-72.536746,41.256207],[-72.537776,41.255646],[-72.546833,41.250718],[-72.547235,41.250499],[-72.570655,41.267744],[-72.571076,41.268054],[-72.571136,41.268098],[-72.583336,41.271698],[-72.585181,41.271321],[-72.585934,41.271168],[-72.586674,41.271017],[-72.587926,41.270761],[-72.589818,41.270375],[-72.590967,41.270141],[-72.598036,41.268698],[-72.607863,41.270387],[-72.610236,41.270795],[-72.617237,41.271998],[-72.617521,41.27194],[-72.617983,41.271845],[-72.631363,41.269092],[-72.641001,41.267108],[-72.641538,41.266998],[-72.642811,41.266884],[-72.650697,41.266178],[-72.653838,41.265897],[-72.653931,41.265931],[-72.654715,41.266219],[-72.662203,41.268964],[-72.662838,41.269197],[-72.667176,41.268192],[-72.671673,41.267151],[-72.672339,41.266997],[-72.674319,41.26552],[-72.684939,41.257597],[-72.685414,41.252607],[-72.685539,41.251297],[-72.689446,41.247629],[-72.690237,41.246887],[-72.690439,41.246697],[-72.693441,41.245493],[-72.694744,41.24497],[-72.69547,41.244948],[-72.701806,41.244752],[-72.706236,41.244615],[-72.707212,41.244585],[-72.708658,41.24454],[-72.708963,41.24453],[-72.709193,41.244523],[-72.710595,41.24448],[-72.710821,41.244812],[-72.713674,41.249007],[-72.711208,41.251018],[-72.71246,41.254167],[-72.722439,41.259138],[-72.732813,41.254727],[-72.754444,41.266913],[-72.757477,41.266913],[-72.786142,41.264796],[-72.818737,41.252244],[-72.819372,41.254061],[-72.826883,41.256755],[-72.847767,41.25669],[-72.85021,41.255544],[-72.854055,41.24774],[-72.861344,41.245297],[-72.881445,41.242597],[-72.895445,41.243697],[-72.900803,41.245864],[-72.904345,41.247297],[-72.905245,41.248297],[-72.903045,41.252797],[-72.902808,41.252894],[-72.894745,41.256197],[-72.89473,41.25626],[-72.893845,41.259897],[-72.89637,41.263949],[-72.903129,41.274794],[-72.907962,41.282549],[-72.9082,41.282932],[-72.916827,41.282033],[-72.917037,41.281905],[-72.920062,41.280056],[-72.920658,41.271574],[-72.920714,41.27078],[-72.920846,41.268897],[-72.931887,41.261139],[-72.933472,41.260024],[-72.935646,41.258497],[-72.956984,41.25292],[-72.959633,41.252228],[-72.961345,41.25178],[-72.962047,41.251597],[-72.983751,41.235364],[-72.985095,41.234358],[-72.986247,41.233497],[-72.997948,41.222697],[-73.003639,41.215287],[-73.007548,41.210197],[-73.013465,41.205479],[-73.013988,41.205062],[-73.014948,41.204297],[-73.020149,41.204097],[-73.020167,41.204237],[-73.020195,41.204446],[-73.02021,41.204568],[-73.020254,41.204906],[-73.020449,41.206397],[-73.022549,41.207197],[-73.024783,41.207435],[-73.045602,41.209658],[-73.05065,41.210197],[-73.054947,41.208468],[-73.05935,41.206697],[-73.07761,41.195176],[-73.07945,41.194015],[-73.09122,41.184153],[-73.092,41.1835],[-73.092147,41.183377],[-73.104328,41.17317],[-73.105483,41.172203],[-73.105493,41.172194],[-73.107987,41.168738],[-73.110352,41.159697],[-73.109952,41.156997],[-73.108352,41.153718],[-73.111052,41.150797],[-73.130253,41.146797],[-73.16437,41.158565],[-73.170074,41.160532],[-73.170701,41.164945],[-73.177774,41.166697],[-73.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\"}}]}","contact":"

Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2020-10-06","noUsgsAuthors":false,"publicationDate":"2020-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Ahearn, Elizabeth A. 0000-0002-5633-2640 eaahearn@usgs.gov","orcid":"https://orcid.org/0000-0002-5633-2640","contributorId":194658,"corporation":false,"usgs":true,"family":"Ahearn","given":"Elizabeth","email":"eaahearn@usgs.gov","middleInitial":"A.","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true},{"id":377,"text":"Massachusetts-Rhode Island Water Science Center","active":false,"usgs":true}],"preferred":false,"id":798678,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":798679,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70214665,"text":"sir20205087 - 2020 - Transmissivity estimated from brief aquifer tests of domestic wells and compared with bedrock lithofacies and position on hillsides in the Appalachian Plateau of New York","interactions":[],"lastModifiedDate":"2020-10-06T21:42:12.920668","indexId":"sir20205087","displayToPublicDate":"2020-10-06T15:30:00","publicationYear":"2020","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":"2020-5087","displayTitle":"Transmissivity Estimated From Brief Aquifer Tests of Domestic Wells and Compared With Bedrock Lithofacies and Position on Hillsides in the Appalachian Plateau of New York","title":"Transmissivity estimated from brief aquifer tests of domestic wells and compared with bedrock lithofacies and position on hillsides in the Appalachian Plateau of New York","docAbstract":"

Procedures for undertaking and analyzing recovery from aquifer tests of 13 to 132 seconds (described in reports cited herein) were applied to 51 domestic drilled wells that penetrated bedrock outside major valleys in the part of the Appalachian Plateau of New York drained by the Susquehanna River. Transmissivities calculated from these tests ranged over three orders of magnitude in both the Catskill-Cattaraugus lithofacies (shales, mudstones, siltstones, medium to coarse sandstones, pebbly sandstones) and the Chemung-Hamilton lithofacies (shales, mudstones, siltstones, fine to medium sandstones). Median transmissivity values were 0.000425 foot squared per second (36.7 feet squared per day) in the Catskill-Cattaraugus lithofacies and 0.00055 foot squared per second (47.5 feet squared per day) in the Chemung-Hamilton lithofacies. The distributions of transmissivity values within the two lithofacies were likewise similar. The range and median values of transmissivity were also nearly the same on lower and midlevel hillsides and were only slightly greater on a few upper hillsides. Transmissivities estimated from such easily arranged and analyzed tests may be appropriate for estimating groundwater flux under the small gradients that prevail under natural conditions, but not under larger drawdowns and steeper gradients near clusters of domestic wells. Four of the 51 wells tested were also pumped for 10 to 32 minutes; analysis by the Theis recovery method yielded transmissivities consistent with the brief tests for 2 wells, but 7 to 9 times smaller for 2 wells.

Transmissivity values estimated by the PICKINGmodel were not significantly different from values estimated by an automated application of the Picking method (PPC-Recovery) at a probability of 95 percent. Transmissivities calculated by either method from data for time intervals of 120 seconds or less may be of limited practical value because they apply only to a small volume of bedrock close to the pumped well.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205087","usgsCitation":"Randall, A.D., and Mills, A.C., 2020, Transmissivity estimated from brief aquifer tests of domestic wells and compared with bedrock lithofacies and position on hillsides in the Appalachian Plateau of New York: U.S. Geological Survey Scientific Investigations Report 2020–5087, 21 p., https://doi.org/10.3133/sir20205087.","productDescription":"Report: iv, 21 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091051","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":378953,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KLZD9M","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Field Data From Brief Aquifer Tests of Domestic Wells Penetrating Bedrock in the Appalachian Plateau of New York and Best Fits to Theoretical Curves of Aquifer Properties"},{"id":378951,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5087/coverthb.jpg"},{"id":378952,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5087/sir20205087.pdf","text":"Report","size":"1.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5087"}],"country":"United States","state":"New York","otherGeospatial":"Appalachian Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.2391357421875,\n 42.0125705565935\n ],\n [\n -74.37744140625,\n 42.0125705565935\n ],\n [\n -74.37744140625,\n 43.03677585761058\n ],\n [\n -78.2391357421875,\n 43.03677585761058\n ],\n [\n -78.2391357421875,\n 42.0125705565935\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2020-10-06","noUsgsAuthors":false,"publicationDate":"2020-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Randall, Allan D. arandall@usgs.gov","contributorId":1168,"corporation":false,"usgs":true,"family":"Randall","given":"Allan","email":"arandall@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":800356,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mills, Andrew C.","contributorId":242016,"corporation":false,"usgs":false,"family":"Mills","given":"Andrew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":800357,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215030,"text":"ofr20201110 - 2020 - A clarification on the effects of urbanization on Golden Eagle (Aquila chrysaetos) habitat selection","interactions":[],"lastModifiedDate":"2020-10-06T21:34:36.833735","indexId":"ofr20201110","displayToPublicDate":"2020-10-06T11:38:02","publicationYear":"2020","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":"2020-1110","displayTitle":"A Clarification on the Effects of Urbanization on Golden Eagle (Aquila chrysaetos) Habitat Selection","title":"A clarification on the effects of urbanization on Golden Eagle (Aquila chrysaetos) habitat selection","docAbstract":"

Introduction

In 2018, the U.S. Geological Survey (USGS) published an Open-File Report (Tracey and others, 2018) presenting a Bayesian habitat selection model for golden eagles (Aquila chrysaetos) in San Diego County, California. The model used telemetry data to examine the effects of urban development, exurban development, and topography (characterized by a topographic position index and a vector ruggedness measure, TPI and VRM respectively) on golden eagle habitat selection probability. Based on figures 3 and 6 of Tracey and others (2018), we received inquiries from cooperators (U.S. Fish and Wildlife Service and California Department of Fish and Wildlife) about how the probability of eagle use declines with decreasing distance to the urban edge. Here, we clarify our results by addressing that question.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201110","collaboration":"Prepared in cooperation with San Diego Association of Governments, U.S. Fish and Wildlife Service, Bureau of Land Management, and California Department of Fish and Wildlife","usgsCitation":"Tracey, J.A., Madden, M.C., Bloom, P.H., and Fisher, R.N., 2020, A clarification on the effects of urbanization on Golden Eagle (Aquila chrysaetos) habitat selection: U.S. Geological Survey Open-File Report 2020–1110, 7 p., https://doi.org/10.3133/ofr20201110.","productDescription":"iv, 7 p.","numberOfPages":"7","onlineOnly":"Y","ipdsId":"IP-121710","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":379081,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1110/covrthb.jpg"},{"id":379082,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1110/ofr20201110.pdf","text":"Report","size":"1.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":379083,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181067","text":"Open-File Report 2018-1067","linkHelpText":"- Golden eagle (Aquila chrysaetos) habitat selection as a function of land use and terrain, San Diego County, California"}],"contact":"

Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2020-10-06","noUsgsAuthors":false,"publicationDate":"2020-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Tracey, Jeff A. 0000-0002-1619-1054 jatracey@usgs.gov","orcid":"https://orcid.org/0000-0002-1619-1054","contributorId":5780,"corporation":false,"usgs":true,"family":"Tracey","given":"Jeff","email":"jatracey@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":800593,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Madden, Melanie C. 0000-0003-4147-7254 mmadden@usgs.gov","orcid":"https://orcid.org/0000-0003-4147-7254","contributorId":229684,"corporation":false,"usgs":true,"family":"Madden","given":"Melanie","email":"mmadden@usgs.gov","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":800594,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bloom, Peter H.","contributorId":242659,"corporation":false,"usgs":true,"family":"Bloom","given":"Peter","email":"","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":800595,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":800596,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215018,"text":"70215018 - 2020 - Identifying the greatest earthquakes of the past 2000 years at the Nehalem River Estuary, Northern Oregon Coast, USA","interactions":[],"lastModifiedDate":"2020-10-06T16:25:31.548543","indexId":"70215018","displayToPublicDate":"2020-10-06T11:16:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7136,"text":"Open Quaternary","active":true,"publicationSubtype":{"id":10}},"title":"Identifying the greatest earthquakes of the past 2000 years at the Nehalem River Estuary, Northern Oregon Coast, USA","docAbstract":"

We infer a history of three great megathrust earthquakes during the past 2000 years at the Nehalem River estuary based on the lateral extent of sharp (≤3 mm) peat-mud stratigraphic contacts in cores and outcrops, coseismic subsidence as interpreted from fossil diatom assemblages and reconstructed with foraminiferal assemblages using a Bayesian transfer function, and regional correlation of 14C-modeled ages for the times of subsidence. A subsidence contact from 1700 CE (contact A), sometimes overlain by tsunami-deposited sand, can be traced over distances of 7 km. Contacts B and D, which record subsidence during two earlier megathrust earthquakes, are much less extensive but are traced across a 700-m by 270-m tidal marsh. Although some other Cascadia studies report evidence for an earthquake between contacts B and D, our lack of extensive evidence for such an earthquake may result from the complexities of preserving identifiable evidence of it in the rapidly shifting shoreline environments of the lower river and bay. Ages (95% intervals) and subsidence for contacts are: A, 1700 CE (1.1 ± 0.5 m); B, 942–764 cal a BP (0.7 ± 0.4 m and 1.0 m ± 0.4 m); and D, 1568–1361 cal a BP (1.0 m ± 0.4 m). Comparisons of contact subsidence and the degree of overlap of their modeled ages with ages for other Cascadia sites are consistent with megathrust ruptures many hundreds of kilometers long. But these data cannot conclusively distinguish among different types or lengths of ruptures recorded by the three great earthquake contacts at the Nehalem River estuary.

","language":"English","publisher":"Ubiquity Press","doi":"10.5334/oq.70","usgsCitation":"Nelson, A.R., Hawkes, A.D., Sawai, Y., Engelhart, S.E., Witter, R., Grant-Walter, W.C., Bradley, L., Dura, T., Cahill, N., and Horton, B.P., 2020, Identifying the greatest earthquakes of the past 2000 years at the Nehalem River Estuary, Northern Oregon Coast, USA: Open Quaternary, v. 6, no. 2, p. 1-30, https://doi.org/10.5334/oq.70.","productDescription":"30 p.","startPage":"1","endPage":"30","ipdsId":"IP-112611","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":455108,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5334/oq.70","text":"Publisher Index Page"},{"id":379088,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","state":"California, Oregon, Washington, British Columbia","otherGeospatial":"Cascadia subduction zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.81591796875,\n 40.64730356252251\n ],\n [\n -123.77197265625,\n 43.723474896114794\n ],\n [\n -123.662109375,\n 45.82879925192134\n ],\n [\n -123.46435546875,\n 47.84265762816538\n ],\n [\n -122.93701171874999,\n 49.1242192485914\n ],\n [\n -126.7822265625,\n 51.0275763378024\n ],\n [\n -128.56201171875,\n 50.86144411058924\n ],\n [\n -127.28759765624999,\n 49.31079887964633\n ],\n [\n -124.45312499999999,\n 46.619261036171515\n ],\n [\n -124.67285156250001,\n 42.66628070564928\n ],\n [\n -123.81591796875,\n 40.64730356252251\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nelson, Alan R. 0000-0001-7117-7098 anelson@usgs.gov","orcid":"https://orcid.org/0000-0001-7117-7098","contributorId":812,"corporation":false,"usgs":true,"family":"Nelson","given":"Alan","email":"anelson@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":800608,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hawkes, Andrea D.","contributorId":192811,"corporation":false,"usgs":false,"family":"Hawkes","given":"Andrea","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":800555,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sawai, Yuki","contributorId":127509,"corporation":false,"usgs":false,"family":"Sawai","given":"Yuki","email":"","affiliations":[{"id":6981,"text":"National Institute of Advanced Industrial Science and Technology, AIST, Japan","active":true,"usgs":false}],"preferred":false,"id":800556,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Engelhart, Simon E.","contributorId":60104,"corporation":false,"usgs":false,"family":"Engelhart","given":"Simon","email":"","middleInitial":"E.","affiliations":[{"id":6923,"text":"University of Rhode Island, Kingston, RI","active":true,"usgs":false}],"preferred":false,"id":800557,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Witter, Robert C. 0000-0002-1721-254X rwitter@usgs.gov","orcid":"https://orcid.org/0000-0002-1721-254X","contributorId":4528,"corporation":false,"usgs":true,"family":"Witter","given":"Robert C.","email":"rwitter@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":800558,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grant-Walter, Wendy C.","contributorId":242632,"corporation":false,"usgs":false,"family":"Grant-Walter","given":"Wendy","email":"","middleInitial":"C.","affiliations":[{"id":48492,"text":"P.O. Box 800, Harwich Port, MA 02646 USA","active":true,"usgs":false}],"preferred":false,"id":800559,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bradley, Lee-Ann","contributorId":193406,"corporation":false,"usgs":false,"family":"Bradley","given":"Lee-Ann","affiliations":[],"preferred":false,"id":800560,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dura, Tina","contributorId":195530,"corporation":false,"usgs":false,"family":"Dura","given":"Tina","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":800561,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cahill, Niamh","contributorId":150754,"corporation":false,"usgs":false,"family":"Cahill","given":"Niamh","email":"","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false},{"id":18091,"text":"University College Dublin","active":true,"usgs":false}],"preferred":false,"id":800562,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Horton, Benajamin P.","contributorId":192918,"corporation":false,"usgs":false,"family":"Horton","given":"Benajamin","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":800563,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70215443,"text":"70215443 - 2020 - The eruptive history, magmatic evolution, and influence of glacial ice at long-lived Akutan volcano, eastern Aleutian Islands, Alaska, USA","interactions":[],"lastModifiedDate":"2020-10-20T13:57:30.629194","indexId":"70215443","displayToPublicDate":"2020-10-06T08:49:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"The eruptive history, magmatic evolution, and influence of glacial ice at long-lived Akutan volcano, eastern Aleutian Islands, Alaska, USA","docAbstract":"

New 40Ar/39Ar and whole-rock geochemical data are used to develop a detailed eruptive chronology for Akutan volcano, Akutan Island, Alaska, USA, in the eastern Aleutian island arc. Akutan Island (166°W, 54.1°N) is the site of long-lived volcanism and the entire island comprises volcanic rocks as old as 3.3 Ma. Our current study is on the 225 km2 western half of the island, where our results show that the focus of volcanism has shifted over the last ∼700 k.y., and that on occasion, multiple volcanic centers have been active over the same period, including within the Holocene. Incremental heating experiments resulted in 56 40Ar/39Ar plateau ages and span 2.3 Ma to 9.2 ka.

Eruptive products of all units are primarily tholeiitic and medium-K, and range from basalt to dacite. Rare calc-alkaline lavas show evidence suggesting their formation via mixing of mafic and evolved magmas, not via crystallization-derived differentiation through the calc-alkaline trend. Earliest lavas are broadly dispersed and are almost exclusively mafic with high and variable La/Yb ratios that are likely the result of low degrees of partial mantle melting. Holocene lavas all fall along a single tholeiitic, basalt-to-dacite evolutionary trend and have among the lowest La/Yb ratios, which favors higher degrees of mantle melting and is consistent with the increased magma flux during this time. A suite of xenoliths, spanning a wide range of compositions, are found in the deposits of the 1.6 ka caldera-forming eruption. They are interpreted to represent completely crystallized liquids or the crystal residuum from tholeiitic fractional crystallization of the active Akutan magma system.

The new geochronologic and geochemical data are used along with existing geodetic and seismic interpretations from the island to develop a conceptual model of the active Akutan magma system. Collectively, these data are consistent with hot, dry magmas that are likely stored at 5−10 km depth prior to eruption. The prolonged eruptive activity at Akutan has also allowed us to evaluate patterns in lava-ice interactions through time as our new data and observations suggest that the influence of glaciation on eruptive activity, and possible magma composition, is more pronounced at Akutan than has been observed for other well-studied Aleutian volcanoes to the west.

","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35667.1","usgsCitation":"Coombs, M.L., and Brian Jicha, 2020, The eruptive history, magmatic evolution, and influence of glacial ice at long-lived Akutan volcano, eastern Aleutian Islands, Alaska, USA: GSA Bulletin, 29 p., https://doi.org/10.1130/B35667.1.","productDescription":"29 p.","ipdsId":"IP-116599","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":379541,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Aleutian Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -169.62890625,\n 50.62507306341435\n ],\n [\n -152.9296875,\n 50.62507306341435\n ],\n [\n -152.9296875,\n 58.90464570302001\n ],\n [\n -169.62890625,\n 58.90464570302001\n ],\n [\n -169.62890625,\n 50.62507306341435\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2020-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Coombs, Michelle L. 0000-0002-6002-6806 mcoombs@usgs.gov","orcid":"https://orcid.org/0000-0002-6002-6806","contributorId":2809,"corporation":false,"usgs":true,"family":"Coombs","given":"Michelle","email":"mcoombs@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":802217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brian Jicha","contributorId":243421,"corporation":false,"usgs":false,"family":"Brian Jicha","affiliations":[{"id":34113,"text":"University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":802218,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216401,"text":"70216401 - 2020 - Decontamination of Ceratocystis pathogens responsible for rapid ʻŌhiʻa Death","interactions":[],"lastModifiedDate":"2020-11-17T12:42:52.924016","indexId":"70216401","displayToPublicDate":"2020-10-06T08:07:32","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7355,"text":"Plant Health Progress","active":true,"publicationSubtype":{"id":10}},"title":"Decontamination of Ceratocystis pathogens responsible for rapid ʻŌhiʻa Death","docAbstract":"

Rapid ʻōhiʻa death (ROD) is caused by two recently described species of Ceratocystis, C. lukuohia and C. huliohia. These fungi are decimating ʻōhiʻa lehua (Metrosideros polymorpha), the keystone native tree species of Hawaiʻi. Viable Ceratocystis propagules can persist in ambrosia beetle frass (Coleoptera: Scolytinae), and movement of the frass may play a key role in the spread of the disease. In order to prevent the spread of ROD, we developed effective and practical surface (e.g., tools and shoes) decontamination methods to be used by researchers, managers, and the public alike. We first tested different household and laboratory disinfectants on the Ceratocystis fungi in culture, and then we applied the effective culture disinfectants to contaminated ambrosia beetle frass. Laboratory-grade ethanol (70, 80, and 95%), Clorox bleach (10%, 0.825% active ingredient [a.i.]), and isopropanol (70 and 91%), were all equally effective at decontaminating cultured C. lukuohia and C. huliohia. Although all concentrations of isopropanol (50, 70, and 90%) and ethanol (50, 70, and 90%) were effective disinfectants of Ceratocystis-contaminated frass, treatments of frass with up to 20% Clorox bleach (1.2% a.i.) were not completely adequate at killing the fungus. These data reveal that bleach is not a sufficient ROD disinfectant when frass is present, and isopropanol or ethanol are the more reliable options.

","language":"English","publisher":"American Phytopathological Society","doi":"10.1094/PHP-06-20-0051-RS","usgsCitation":"Roy, K., Jaenecke, K., Bjontegard, N., Mikros, D., Dunkle, E., Yanger, C., Sugiyama, L.S., Keith, L.M., and Peck, R., 2020, Decontamination of Ceratocystis pathogens responsible for rapid ʻŌhiʻa Death: Plant Health Progress, v. 21, p. 301-305, https://doi.org/10.1094/PHP-06-20-0051-RS.","productDescription":"5 p.","startPage":"301","endPage":"305","ipdsId":"IP-119927","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":436763,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JOOSP8","text":"USGS data release","linkHelpText":"Hawai'i Rapid 'Ohi'a Death Decontamination 2019-2020"},{"id":380527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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0000-0001-9974-1818","orcid":"https://orcid.org/0000-0001-9974-1818","contributorId":244900,"corporation":false,"usgs":false,"family":"Keith","given":"Lisa","email":"","middleInitial":"M.","affiliations":[{"id":49012,"text":"USDA Agricultural Research Service, DKI-PBARC","active":true,"usgs":false}],"preferred":false,"id":804901,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Peck, Robert W. 0000-0002-8739-9493","orcid":"https://orcid.org/0000-0002-8739-9493","contributorId":193088,"corporation":false,"usgs":false,"family":"Peck","given":"Robert W.","affiliations":[],"preferred":false,"id":804902,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70215375,"text":"70215375 - 2020 - Water balance as an indicator of natural resource condition: Case studies from Great Sand Dunes National Park and Preserve","interactions":[],"lastModifiedDate":"2020-10-16T12:56:39.896421","indexId":"70215375","displayToPublicDate":"2020-10-06T07:53:01","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Water balance as an indicator of natural resource condition: Case studies from Great Sand Dunes National Park and Preserve","docAbstract":"

Managing climate impacts to natural resources in protected areas can be hampered by lack of monitoring data, poor understanding of natural resource responses to climate, or lack of timely condition assessments that can inform management actions. Here we demonstrate the utility of water balance as a tool for understanding natural resource responses to climate by developing case studies focused on stream flow, vegetation production, and wildfire ignition at Great Sand Dunes National Park and Preserve (GSDNP), U.S.A. The efficacy of water balance to predict these responses stems from the explicit integration of climate with site conditions that modify the effects of climate. This in turn results in estimates of water availability, water use, and water need that are proximal drivers of aquatic and terrestrial natural resource conditions. The water balance model successfully forecasted stream flow (r2 = 0.69, P < 0.001); determined the critical water needs for maintaining annual vegetation production in different vegetation types spanning a large environmental gradient (r2 = 0.18–0.71); and predicted proportion of historic wildfire ignitions in forest (r2 = 0.96–0.99) and non-forest (r2 = 0.96–0.97) vegetation types. Collectively, these case studies demonstrate practical approaches to translate climate data into assessments of natural resource condition that inform long-term planning and near-term strategic actions needed for conservation of protected areas.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2020.e01300","usgsCitation":"Thoma, D.P., Tercek, M.T., Schweiger, E.W., Munson, S.M., Gross, J.E., and Olliff, S.T., 2020, Water balance as an indicator of natural resource condition: Case studies from Great Sand Dunes National Park and Preserve: Global Ecology and Conservation, v. 24, e01300, 17 p., https://doi.org/10.1016/j.gecco.2020.e01300.","productDescription":"e01300, 17 p.","ipdsId":"IP-121269","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":455118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2020.e01300","text":"Publisher Index Page"},{"id":379456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Sand Dunes National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.87249755859375,\n 37.568528265476075\n ],\n [\n -105.46051025390625,\n 37.48793540168987\n ],\n [\n -105.26275634765625,\n 37.63163475580643\n ],\n [\n -105.42755126953125,\n 37.88569271818349\n ],\n [\n -105.7269287109375,\n 38.05457952821193\n ],\n [\n -106.0235595703125,\n 38.035112420612975\n ],\n [\n -105.87249755859375,\n 37.568528265476075\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thoma, David P.","contributorId":197256,"corporation":false,"usgs":false,"family":"Thoma","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":801891,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tercek, Michael T.","contributorId":197257,"corporation":false,"usgs":false,"family":"Tercek","given":"Michael","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":801892,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schweiger, E. William","contributorId":243260,"corporation":false,"usgs":false,"family":"Schweiger","given":"E.","email":"","middleInitial":"William","affiliations":[{"id":48669,"text":"National Park Service Inventory and Monitoring Program, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":801893,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":801894,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gross, John E.","contributorId":106777,"corporation":false,"usgs":false,"family":"Gross","given":"John","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":801895,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Olliff, S. Tom","contributorId":243261,"corporation":false,"usgs":false,"family":"Olliff","given":"S.","email":"","middleInitial":"Tom","affiliations":[{"id":48671,"text":"National Park Service Climate Change Response Program, Bozeman, Montana","active":true,"usgs":false}],"preferred":false,"id":801896,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216497,"text":"70216497 - 2020 - Using movement to inform conservation corridor design for Mojave desert tortoise","interactions":[],"lastModifiedDate":"2020-11-24T13:54:06.084531","indexId":"70216497","displayToPublicDate":"2020-10-06T07:47:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Using movement to inform conservation corridor design for Mojave desert tortoise","docAbstract":"

Background

Preserving corridors for movement and gene flow among populations can assist in the recovery of threatened and endangered species. As human activity continues to fragment habitats, characterizing natural corridors is important in establishing and maintaining connectivity corridors within the anthropogenic development matrix. The Mojave desert tortoise (Gopherus agassizii) is a threatened species occupying a variety of habitats in the Mojave and Colorado Deserts. Desert tortoises have been referred to as corridor-dwellers, and understanding how they move within suitable habitat can be crucial to defining corridors that will sustain sufficient gene flow to maintain connections among populations amidst the increases in human development.

Methods

To elucidate how tortoises traverse available habitat and interact with potentially inhospitable terrain and human infrastructure, we used GPS dataloggers to document fine-scale movement of individuals and estimate home ranges at ten study sites along the California/Nevada border. Our sites encompass a variety of habitats, including mountain passes that serve as important natural corridors connecting neighboring valleys, and are impacted by a variety of linear anthropogenic features. We used path selection functions to quantify tortoise movements and develop resistance surfaces based on landscape characteristics including natural features, anthropogenic alterations, and estimated home ranges with autocorrelated kernel density methods. Using the best supported path selection models and estimated home ranges, we determined characteristics of known natural corridors and compared them to mitigation corridors (remnant habitat patches) that have been integrated into land management decisions in the Ivanpah Valley.

Results

Tortoises avoided areas of high slope and low perennial vegetation cover, avoided moving near low-density roads, and traveled along linear barriers (fences and flood control berms).

Conclusions

We found that mitigation corridors designated between solar facilities should be wide enough to retain home ranges and maintain function. Differences in home range size and movement resistance between our two natural mountain pass corridors align with differences in genetic connectivity, suggesting that not all natural corridors provide the same functionality. Furthermore, creation of mitigation corridors with fences may have unintended consequences and may function differently than natural corridors. Understanding characteristics of corridors with different functionality will help future managers ensure that connectivity is maintained among Mojave desert tortoise populations.

","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-020-00224-8","usgsCitation":"Hromada, S.J., Esque, T., Vandergast, A.G., Dutcher, K.E., Mitchell, C.I., Gray, M.E., Chang, T., Dickson, B.G., and Nussear, K.E., 2020, Using movement to inform conservation corridor design for Mojave desert tortoise: Movement Ecology, v. 8, 38, 18 p., https://doi.org/10.1186/s40462-020-00224-8.","productDescription":"38, 18 p.","ipdsId":"IP-122372","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":455120,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-020-00224-8","text":"Publisher Index Page"},{"id":380740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.49877929687499,\n 35.41591492345623\n ],\n [\n -114.3896484375,\n 35.41591492345623\n ],\n [\n -114.3896484375,\n 36.932330061503144\n ],\n [\n -117.49877929687499,\n 36.932330061503144\n ],\n [\n -117.49877929687499,\n 35.41591492345623\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Hromada, Steven J.","contributorId":245147,"corporation":false,"usgs":false,"family":"Hromada","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":805444,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":805445,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vandergast, Amy G. 0000-0002-7835-6571 avandergast@usgs.gov","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":3963,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"avandergast@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":805446,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dutcher, Kirsten E.","contributorId":221063,"corporation":false,"usgs":false,"family":"Dutcher","given":"Kirsten","email":"","middleInitial":"E.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":805447,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mitchell, Corey I","contributorId":245149,"corporation":false,"usgs":false,"family":"Mitchell","given":"Corey","email":"","middleInitial":"I","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":805448,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gray, Miranda E","contributorId":221848,"corporation":false,"usgs":false,"family":"Gray","given":"Miranda","email":"","middleInitial":"E","affiliations":[{"id":40441,"text":"Conservation Science Partners, Truckee, CA","active":true,"usgs":false}],"preferred":false,"id":805449,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chang, Tony","contributorId":191992,"corporation":false,"usgs":false,"family":"Chang","given":"Tony","email":"","affiliations":[],"preferred":false,"id":805450,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dickson, Brett G.","contributorId":221849,"corporation":false,"usgs":false,"family":"Dickson","given":"Brett","email":"","middleInitial":"G.","affiliations":[{"id":40442,"text":"Conservation Science Partners, Truckee, CA; Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":805451,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nussear, Kenneth E.","contributorId":117361,"corporation":false,"usgs":false,"family":"Nussear","given":"Kenneth","email":"","middleInitial":"E.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":805452,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70215190,"text":"70215190 - 2020 - Spatially explicit reconstruction of post-megafire forest recovery through landscape modeling","interactions":[],"lastModifiedDate":"2020-10-10T12:58:01.214192","indexId":"70215190","displayToPublicDate":"2020-10-06T07:44:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Spatially explicit reconstruction of post-megafire forest recovery through landscape modeling","docAbstract":"

Megafires are large wildfires that occur under extreme weather conditions and produce mixed burn severities across diverse environmental gradients. Assessing megafire effects requires data covering large spatiotemporal extents, which are difficult to collect from field inventories. Remote sensing provides an alternative but is limited in revealing post-fire recovery trajectories and the underlying processes that drive the recovery. We developed a novel framework to spatially reconstruct the post-fire time-series of forest conditions after the 1987 Black Dragon fire of China by integrating a forest landscape model (LANDIS) with remote sensing and inventory data. We derived pre-fire (1985) forest composition and the megafire perimeter and severity using remote sensing and inventory data. We simulated the megafire and the post-megafire forest recovery from 1985 to 2015 using the LANDIS model. We demonstrated that the framework was effective in reconstructing the post-fire stand dynamics and that it is applicable to other types of disturbances.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2020.104884","usgsCitation":"Xu, W., He, H., Fraser, J.S., Hawbaker, T., Henne, P., Duan, S., and Zhu, Z., 2020, Spatially explicit reconstruction of post-megafire forest recovery through landscape modeling: Environmental Modelling and Software, v. 134, 104884, 10 p., https://doi.org/10.1016/j.envsoft.2020.104884.","productDescription":"104884, 10 p.","ipdsId":"IP-119940","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":455122,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2020.104884","text":"Publisher Index Page"},{"id":436764,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HRHBXZ","text":"USGS data release","linkHelpText":"Data release for: Spatially explicit reconstruction of post-megafire forest recovery through landscape modeling"},{"id":379290,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 117.0703125,\n 46.558860303117164\n ],\n [\n 133.59375,\n 46.558860303117164\n ],\n [\n 133.59375,\n 54.57206165565852\n ],\n [\n 117.0703125,\n 54.57206165565852\n ],\n [\n 117.0703125,\n 46.558860303117164\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"134","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, Wenru","contributorId":222616,"corporation":false,"usgs":false,"family":"Xu","given":"Wenru","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":801112,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"He, Hong","contributorId":242923,"corporation":false,"usgs":false,"family":"He","given":"Hong","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":801113,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fraser, Jacob S.","contributorId":206005,"corporation":false,"usgs":false,"family":"Fraser","given":"Jacob","email":"","middleInitial":"S.","affiliations":[{"id":37214,"text":"University of Missouri – Columbia","active":true,"usgs":false}],"preferred":false,"id":801114,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":801115,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Henne, Paul D. 0000-0003-1211-5545 phenne@usgs.gov","orcid":"https://orcid.org/0000-0003-1211-5545","contributorId":169166,"corporation":false,"usgs":true,"family":"Henne","given":"Paul D.","email":"phenne@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":801116,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duan, Shengwu","contributorId":242925,"corporation":false,"usgs":false,"family":"Duan","given":"Shengwu","email":"","affiliations":[{"id":36845,"text":"School of Natural Resources, University of Missouri","active":true,"usgs":false}],"preferred":false,"id":801117,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":801118,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70217187,"text":"70217187 - 2020 - High site fidelity does not equate to population genetic structure for common goldeneye and Barrow's goldeneye in North America","interactions":[],"lastModifiedDate":"2021-01-11T16:38:49.270248","indexId":"70217187","displayToPublicDate":"2020-10-05T10:12:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2190,"text":"Journal of Avian Biology","active":true,"publicationSubtype":{"id":10}},"title":"High site fidelity does not equate to population genetic structure for common goldeneye and Barrow's goldeneye in North America","docAbstract":"

Delineation of population structure provides valuable information for conservation and management of species, as levels of demographic and genetic connectivity not only affect population dynamics but also have important implications for adaptability and resiliency of populations and species. Here, we measure population genetic structure and connectivity across the ranges of two sister species of sea ducks: Barrow's goldeneye Bucephala islandica and common goldeneye B. clangula. We use two different marker types: 7–8 nuclear microsatellite loci assayed across 229 samples and 3678 double digest restriction‐site associated DNA sequencing (ddRAD‐seq) loci assayed across 61 samples. First, both datasets found no evidence of genetic structure within common or Barrow's goldeneye, including between North American and European samples of common goldeneye. These results are in contrast with previous mitochondrial DNA, band recovery and telemetry data which suggest that goldeneyes are structured across their range. We posit that the discordance between autosomal genetic markers and other data types suggests that males, possibly subadult males, may be maintaining genetic connectivity across each species' respective ranges. Next, although mate choice consequences resulting from inter‐specific brood parasitism was hypothesized to cause some level of gene flow between goldeneye species, we only identified a single F1 hybrid with no further evidence of contemporary or historical gene flow. Despite ddRAD‐seq demographic analyses which recovered an optimum evolutionary model of split‐with‐migration (i.e. secondary contact), estimates of gene flow were <<1 migrant per generation in both directions. Together, we conclude that either strong ecological barriers or assortative mating are likely playing a role in preventing further backcrossing. Finally, demographic analyses estimated a relatively deep divergence time between Barrow's goldeneye and common goldeneye of ~1.6 million years before present and suggests that the genomes of both species have been under similar evolutionary constraints.

","language":"English","publisher":"Wiley","doi":"10.1111/jav.02600","usgsCitation":"Brown, J.I., Lavretsky, P., Wilson, R.E., Haughey, C., Boyd, W., Esler, D., Talbot, S.L., and Sonsthagen, S.A., 2020, High site fidelity does not equate to population genetic structure for common goldeneye and Barrow's goldeneye in North America: Journal of Avian Biology, v. 51, no. 12, e02600, 12 p., https://doi.org/10.1111/jav.02600.","productDescription":"e02600, 12 p.","ipdsId":"IP-118941","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":436766,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D8CN8M","text":"USGS data release","linkHelpText":"Genetic Data from Barrow's Goldeneye and Common Goldeneye"},{"id":382061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Denmark, Mexico, United States","volume":"51","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Brown, Joshua I.","contributorId":247561,"corporation":false,"usgs":false,"family":"Brown","given":"Joshua","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":807900,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lavretsky, Philip","contributorId":60542,"corporation":false,"usgs":true,"family":"Lavretsky","given":"Philip","email":"","affiliations":[],"preferred":false,"id":807901,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, Robert E. 0000-0003-1800-0183 rewilson@usgs.gov","orcid":"https://orcid.org/0000-0003-1800-0183","contributorId":5718,"corporation":false,"usgs":true,"family":"Wilson","given":"Robert","email":"rewilson@usgs.gov","middleInitial":"E.","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":807902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haughey, Christy 0000-0002-4846-6008","orcid":"https://orcid.org/0000-0002-4846-6008","contributorId":220547,"corporation":false,"usgs":true,"family":"Haughey","given":"Christy","email":"","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":807903,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boyd, W. Sean","contributorId":241002,"corporation":false,"usgs":false,"family":"Boyd","given":"W. Sean","affiliations":[{"id":48188,"text":"Environment Canada","active":true,"usgs":false}],"preferred":false,"id":807904,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Esler, Daniel 0000-0001-5501-4555 desler@usgs.gov","orcid":"https://orcid.org/0000-0001-5501-4555","contributorId":5465,"corporation":false,"usgs":true,"family":"Esler","given":"Daniel","email":"desler@usgs.gov","affiliations":[{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":807905,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":807906,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":807907,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70215059,"text":"70215059 - 2020 - Examination of inertinite within immature Eagle Ford Shale at the nanometer-scale using atomic force microscopy-based infrared spectroscopy","interactions":[],"lastModifiedDate":"2020-10-29T15:10:52.865686","indexId":"70215059","displayToPublicDate":"2020-10-05T08:24:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Examination of inertinite within immature Eagle Ford Shale at the nanometer-scale using atomic force microscopy-based infrared spectroscopy","docAbstract":"

The nanoscale molecular composition of sedimentary organic matter is challenging to characterize in situ given the limited tools available that can adequately interrogate its complex chemical structure. This is a particularly relevant issue in source rocks, as kerogen composition will strongly impact its reactivity and so is critical to understanding petroleum generation processes during catagenesis. The recent advent of tip-enhanced analytical methods, such as atomic force microscopy-based infrared spectroscopy (AFM-IR), has allowed for the major compositional features of kerogen and other types of in situ organic matter to be elucidated at spatial resolutions at or below 50 nm. Here AFM-IR was applied to examine inertinite, an important organic matter type, present in a thermally immature Eagle Ford calcareous mudstone. The data show that the nanoscale molecular composition of the examined inertinite is (i) less heterogeneous than solid bitumen in more thermally mature Eagle Ford samples and (ii) more hydrogen- and oxygen-rich than inertinite examined in the New Albany Shale.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2020.103608","usgsCitation":"Jubb, A., Hackley, P.C., Birdwell, J.E., Hatcherian, J.J., and Qu, J., 2020, Examination of inertinite within immature Eagle Ford Shale at the nanometer-scale using atomic force microscopy-based infrared spectroscopy: International Journal of Coal Geology, v. 231, 103608, 4 p., https://doi.org/10.1016/j.coal.2020.103608.","productDescription":"103608, 4 p.","ipdsId":"IP-120851","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":455138,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coal.2020.103608","text":"Publisher Index Page"},{"id":379165,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Eagle Ford Shale, Bechtel Well","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.14773559570312,\n 28.810986808864513\n ],\n [\n -97.88955688476562,\n 28.810986808864513\n ],\n [\n -97.88955688476562,\n 29.012944302424863\n ],\n [\n -98.14773559570312,\n 29.012944302424863\n ],\n [\n -98.14773559570312,\n 28.810986808864513\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"231","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":800664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":800665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":800666,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":800667,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Qu, Jing","contributorId":242671,"corporation":false,"usgs":false,"family":"Qu","given":"Jing","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":800668,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209315,"text":"sir20205023 - 2020 - Distribution of selected hydrogeologic characteristics of the upper glacial and Magothy aquifers, Long Island, New York","interactions":[],"lastModifiedDate":"2020-10-01T19:44:37.604527","indexId":"sir20205023","displayToPublicDate":"2020-10-01T14:05:00","publicationYear":"2020","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":"2020-5023","displayTitle":"Distribution of Selected Hydrogeologic Characteristics of the Upper Glacial and Magothy Aquifers, Long Island, New York","title":"Distribution of selected hydrogeologic characteristics of the upper glacial and Magothy aquifers, Long Island, New York","docAbstract":"

The Pleistocene- and Cretaceous-age sediments underlying Long Island, New York, compose an important sole-source aquifer system that is nearly 2,000 feet thick in some areas. Sediment characteristics of importance for water supply include water-transmitting properties—horizontal and vertical hydraulic conductivity—and the distribution of lignite, which provides an important control on oxygen-reduction (redox) conditions and water quality, in Cretaceous-age aquifers. Several decades of urbanization and the associated need to meet water demand have generated abundant data on the lithology of the aquifer sediments and the potential for an improved regional-scale understanding of this aquifer system. There is a range in the source and quality of the information, but large amounts of data, even of lesser quality, can yield insight into important aquifer characteristics.

The distribution of the horizontal and vertical hydraulic conductivity and the probability of occurrence of lignite and clay in the aquifer were developed for this study from a database of drilling records and geophysical logs. Lithologic descriptions were categorized into a set of standardized codes, which in turn, were aggregated into a set of general codes for the Pleistocene-age upper glacial and Cretaceous-age Magothy aquifers. General values of hydraulic conductivity were assigned to each code from published estimates on Long Island and analogous hydrogeologic environments on Cape Cod, Massachusetts. A binary value of 1 or 0 was assigned to each coded interval to indicate the presence or absence of lignite or based on keywords in the lithologic descriptions. This information was assembled into a geographic information system database that was queried sequentially and used to develop gridded values of each aquifer characteristic by use of ordinary kriging for a set of grids, each representing 10-foot-thick planar slices for the entire vertical thickness of each aquifer. These sets of grids, taken as a whole, represent a quasi-three-dimensional representation of each aquifer characteristic in both the upper glacial and Magothy aquifers.

The analysis of hydraulic conductivity shows patterns that generally reflect known depositional features of each unit and are consistent with the current understanding of the geology of the aquifers. Spatial patterns in the upper glacial aquifer show contrasts in estimated hydraulic conductivity: lower values occur in inland areas and likely are associated with glacial moraines; higher values generally occur to the south in association with glacial outwash. Higher values of hydraulic conductivity in the Magothy aquifer, which resulted from deltaic deposition, generally occur in the basal parts of the unit, are associated with channel-lag deposits and are found in parts of the aquifer known for large well yields. Lower values of hydraulic conductivity generally occur in middle parts of the aquifer associated with deposition in overbank and wetland environments. The probability of lignite occurrence is highest in this same vertical zone of the Magothy aquifer, consistent with deposition in wetland environments. The probability of lignite occurrence generally is highest along the southern shore of the island. Lignite occurrence generally is consistent with water-quality patterns; water quality in these same areas indicate chemically reducing conditions and redox-related iron biofouling commonly occurs.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205023","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Walter, D.A., and Finkelstein, J.S., 2020, Distribution of selected hydrogeologic characteristics of the upper glacial and Magothy aquifers, Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2020–5023, 21 p., https://doi.org/10.3133/sir20205023.","productDescription":"Report: iv, 21 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-111547","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":377889,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P954DLLC","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Aquifer texture data describing the Long Island aquifer system"},{"id":377890,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5023/coverthb.jpg"},{"id":377891,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5023/sir20205023.pdf","text":"Report","size":"8.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5023"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.11926269531249,\n 40.49291502689579\n ],\n [\n -71.85058593749999,\n 40.49291502689579\n ],\n [\n -71.7681884765625,\n 41.269549502842565\n ],\n [\n -74.11926269531249,\n 41.10832999732831\n ],\n [\n -74.11926269531249,\n 40.49291502689579\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2020-10-01","noUsgsAuthors":false,"publicationDate":"2020-10-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Walter, Donald A. 0000-0003-0879-4477 dawalter@usgs.gov","orcid":"https://orcid.org/0000-0003-0879-4477","contributorId":1101,"corporation":false,"usgs":true,"family":"Walter","given":"Donald","email":"dawalter@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finkelstein, Jason S. 0000-0002-7496-7236","orcid":"https://orcid.org/0000-0002-7496-7236","contributorId":202452,"corporation":false,"usgs":true,"family":"Finkelstein","given":"Jason S.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786028,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70228502,"text":"70228502 - 2020 - Fort Peck paddlefish population survival and abundance in the Missouri River","interactions":[],"lastModifiedDate":"2022-02-11T17:08:46.71916","indexId":"70228502","displayToPublicDate":"2020-10-01T11:01:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2166,"text":"Journal of Applied Ichthyology","active":true,"publicationSubtype":{"id":10}},"title":"Fort Peck paddlefish population survival and abundance in the Missouri River","docAbstract":"

Excessive fishing pressure can induce population declines or complete collapse of fisheries. Unless commercial and recreational fisheries for K-selected fishes, or those with slow growth and late maturation, are carefully managed, declines in abundance or fishery collapse is probable. Paddlefish Polyodon spathula,are a K-selected species that experienced historical declines in abundance as a result of habitat degradation and overfishing. Mark-recapture studies are well-suited for long-lived fishes by providing information on population density and vital rates. For sustainable commercial or recreational fisheries targeting species such as the paddlefish, managers require accurate estimates of population vital rates including survival, abundance, and exploitation. We used a Montana Fish, Wildlife & Parks (MFWP) mark-recapture dataset and modified Jolly-Seber (POPAN) models to estimate survival, recapture, probability of entry, and abundance of 8,518 tagged paddlefish over a 25-year period. With many supporting estimates including stable survival (0.92 for females, mean of 0.82 for males), low exploitation rates (means of 2.6% for females and 2.9% for males), and stable abundance estimates (25-year mean of 12,309 individuals for both sexes), the Fort Peck paddlefish population appears to be stable and well-managed over the past 25 years. Presently, this is the only study focused on paddlefish in North America that has estimated survival and abundance for both male and female paddlefish using contemporary analyses. This research provided a unique opportunity to highlight that the effort exerted by management agencies to collect long-term field data is extremely useful to our understanding of fish populations and management.

","language":"English","publisher":"Wiley","doi":"10.1111/jai.14067","usgsCitation":"Glassic, H., Guy, C.S., Rotella, J.J., Nagel, C.J., Schmetterling, D.A., and Dalbey, S.R., 2020, Fort Peck paddlefish population survival and abundance in the Missouri River: Journal of Applied Ichthyology, v. 36, no. 5, p. 559-567, https://doi.org/10.1111/jai.14067.","productDescription":"9 p.","startPage":"559","endPage":"567","ipdsId":"IP-117176","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":455154,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jai.14067","text":"Publisher Index Page"},{"id":395852,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Fort Peck Reservoir, Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.39984130859374,\n 48.03401915864286\n ],\n [\n -106.51519775390625,\n 48.026672195436014\n ],\n [\n -106.66900634765625,\n 47.964180715412276\n ],\n [\n -106.67449951171875,\n 47.868459093342956\n ],\n [\n -106.78985595703124,\n 47.80577611936809\n ],\n [\n -107.00958251953125,\n 47.75779097897638\n ],\n [\n -107.0123291015625,\n 47.70976154266637\n ],\n [\n -107.26226806640625,\n 47.69867153529717\n ],\n [\n -107.3583984375,\n 47.74486433470359\n ],\n [\n -107.4627685546875,\n 47.700520033704954\n ],\n [\n -107.46551513671875,\n 47.645036570200226\n ],\n [\n -107.51220703125,\n 47.66723703450518\n ],\n [\n -107.74841308593749,\n 47.68573021131587\n ],\n [\n -107.8912353515625,\n 47.5394554474239\n ],\n [\n -107.91046142578125,\n 47.58023129789275\n ],\n [\n -108.05877685546875,\n 47.622826666563675\n ],\n [\n -108.424072265625,\n 47.633932798340716\n ],\n [\n -108.687744140625,\n 47.64133557512159\n ],\n [\n -108.7042236328125,\n 47.61727271567975\n ],\n [\n -108.39385986328125,\n 47.56355410390806\n ],\n [\n -108.1988525390625,\n 47.56911375866714\n ],\n [\n -108.050537109375,\n 47.56540738772852\n ],\n [\n -107.9571533203125,\n 47.53389264528655\n ],\n [\n -108.0010986328125,\n 47.454094290400015\n ],\n [\n -107.90771484375,\n 47.37417465983494\n ],\n [\n -107.81982421874999,\n 47.46337939935778\n ],\n [\n -107.8363037109375,\n 47.51349065484327\n ],\n [\n -107.74841308593749,\n 47.52461999690651\n ],\n [\n -107.68524169921875,\n 47.59690318115471\n ],\n [\n -107.56439208984375,\n 47.62467785241324\n ],\n [\n -107.41333007812499,\n 47.570966845786124\n ],\n [\n -107.38311767578124,\n 47.646886969413\n ],\n [\n -107.2869873046875,\n 47.633932798340716\n ],\n [\n -107.0892333984375,\n 47.59875528481801\n ],\n [\n -106.9244384765625,\n 47.56540738772852\n ],\n [\n -106.7266845703125,\n 47.645036570200226\n ],\n [\n -106.55914306640625,\n 47.700520033704954\n ],\n [\n -106.47125244140625,\n 47.85740289465826\n ],\n [\n -106.3751220703125,\n 47.73747623919626\n ],\n [\n -106.32843017578125,\n 47.62467785241324\n ],\n [\n -106.3201904296875,\n 47.5264746577327\n ],\n [\n -106.270751953125,\n 47.50050343862717\n ],\n [\n -106.19384765625,\n 47.54501765940571\n ],\n [\n -106.16912841796875,\n 47.60245929546312\n ],\n [\n -106.1993408203125,\n 47.80393135603966\n ],\n [\n -106.2652587890625,\n 47.938426929481054\n ],\n [\n -106.31469726562499,\n 48.011975126709956\n ],\n [\n -106.34765625,\n 48.03769224746972\n ],\n [\n -106.39984130859374,\n 48.03401915864286\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-06-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Glassic, Hayley C.","contributorId":243051,"corporation":false,"usgs":false,"family":"Glassic","given":"Hayley C.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":834454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true}],"preferred":true,"id":834453,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rotella, Jay J.","contributorId":37271,"corporation":false,"usgs":false,"family":"Rotella","given":"Jay","email":"","middleInitial":"J.","affiliations":[{"id":5098,"text":"Department of Ecology, Montana State University","active":true,"usgs":false}],"preferred":false,"id":834455,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nagel, Cody J.","contributorId":275985,"corporation":false,"usgs":false,"family":"Nagel","given":"Cody","email":"","middleInitial":"J.","affiliations":[{"id":39047,"text":"Montana Fish, Wildlife, and Parks","active":true,"usgs":false}],"preferred":false,"id":834456,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmetterling, David A.","contributorId":20223,"corporation":false,"usgs":true,"family":"Schmetterling","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":834457,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dalbey, Steven R.","contributorId":275988,"corporation":false,"usgs":false,"family":"Dalbey","given":"Steven","email":"","middleInitial":"R.","affiliations":[{"id":39047,"text":"Montana Fish, Wildlife, and Parks","active":true,"usgs":false}],"preferred":false,"id":834458,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228250,"text":"70228250 - 2020 - Estimating nitrogen removal services of eastern oyster (Crassostrea virginica) in Mobile Bay, Alabama","interactions":[],"lastModifiedDate":"2022-02-08T17:08:16.269993","indexId":"70228250","displayToPublicDate":"2020-10-01T10:46:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Estimating nitrogen removal services of eastern oyster (Crassostrea virginica) in Mobile Bay, Alabama","title":"Estimating nitrogen removal services of eastern oyster (Crassostrea virginica) in Mobile Bay, Alabama","docAbstract":"

Eastern oysters have been acknowledged for their important contribution to human well-being by providing goods and services including nitrogen removal from water bodies. In this study, we integrated daily environmental data (2008–2016) and filtration rate model parameter uncertainty to estimate nitrogen removal from denitrification and nitrogen burial services provided by the current extent of oyster (Crassostrea virginica) reefs in Mobile Bay, Alabama. Oyster landing data (2008–2016) in the Bay were also used to estimate nitrogen removal through oyster harvest. A replacement cost method using an engineering solution from wastewater treatment plants was implemented to quantify the economic benefit of the nitrogen removal. The estimated total nitrogen removal services provided by oyster reefs in Mobile Bay was 34,911 ± 5,032 kg N yr−1 (mean ± 1sd), in which 22,095 ± 3,305 kg N yr−1 from denitrification, 11,047 ± 1,652 kg N yr−1 from burial of nitrogen into sediments and 1,769 ± 876 kg N yr−1 by oyster harvest. The mean economic benefit was $76,455 ± 11,020 yr−1 which was estimated as $73.2 ± 11.5 ha−1 yr−1. This method could be used for any time period to estimate the nitrogen removal service in Mobile Bay. With proper modification of model parameters, this method could also be used elsewhere to estimate nitrogen removal services provided by oysters.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2020.106541","usgsCitation":"Lai, Q., Irwin, E.R., and Zhang, Y., 2020, Estimating nitrogen removal services of eastern oyster (Crassostrea virginica) in Mobile Bay, Alabama: Ecological Indicators, v. 117, p. 1-9, https://doi.org/10.1016/j.ecolind.2020.106541.","productDescription":"106541, 9 p.","startPage":"1","endPage":"9","ipdsId":"IP-109626","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":455156,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2020.106541","text":"Publisher Index Page"},{"id":395631,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Gulf of Mexico, Mobile Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.25042724609374,\n 30.23652704486517\n ],\n [\n -88.06228637695312,\n 30.203300547277813\n ],\n [\n -87.69012451171875,\n 30.22466172703242\n ],\n [\n -87.88375854492186,\n 30.456960567387625\n ],\n [\n -87.85491943359375,\n 30.822063696500948\n ],\n [\n -88.05130004882812,\n 30.86686781614027\n ],\n [\n -88.13232421875,\n 30.657996912582398\n ],\n [\n -88.25042724609374,\n 30.23652704486517\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"117","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lai, Quan","contributorId":204521,"corporation":false,"usgs":false,"family":"Lai","given":"Quan","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":833537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irwin, Elise R. 0000-0002-6866-4976 eirwin@usgs.gov","orcid":"https://orcid.org/0000-0002-6866-4976","contributorId":2588,"corporation":false,"usgs":true,"family":"Irwin","given":"Elise","email":"eirwin@usgs.gov","middleInitial":"R.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Yaoqi","contributorId":275164,"corporation":false,"usgs":false,"family":"Zhang","given":"Yaoqi","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":833750,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215979,"text":"70215979 - 2020 - The impacts of a changing climate to DOD coastal facilities in the tropical Pacific Ocean","interactions":[],"lastModifiedDate":"2021-01-28T16:34:47.129382","indexId":"70215979","displayToPublicDate":"2020-10-01T10:26:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2837,"text":"Natural Selections","active":true,"publicationSubtype":{"id":10}},"title":"The impacts of a changing climate to DOD coastal facilities in the tropical Pacific Ocean","docAbstract":"

The USGS, the National Oceanic and Atmospheric Administration (NOAA), Deltares, and the University of Hawaii (UH) recently completed a study investigating the impact of a changing climate and sea-level rise on Roi-Namur Island on Kwajalein Atoll in the Republic of the Marshall Islands, which is part of the Ronald Reagan Ballistic Missile Defense Test Site (RTS). The isolated location of RTS makes it uniquely suited to support realistic testing of missiles and intercept scenarios with minimal safety and environmental concerns. In addition, this unique location supports research and development for space and missile programs and space reconnaissance and surveillance operations. RTS has been a critical component of the Pacific Range, with the world’s most advanced telemetry, optics, and radar instrumentation used to collect metric and signature data on missiles.

","language":"English","publisher":"U.S. Department of Defense","usgsCitation":"Storlazzi, C., 2020, The impacts of a changing climate to DOD coastal facilities in the tropical Pacific Ocean: Natural Selections, no. Fall, p. 5-6.","productDescription":"2 p.","startPage":"5","endPage":"6","ipdsId":"IP-119241","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":382763,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":380012,"type":{"id":11,"text":"Document"},"url":"https://www.denix.osd.mil/nr/resources/newsletter/2020/fall-2020/Natural%20Selections_Fall%202020_v8_final_508.pdf"}],"country":"Republic of the Marshall Islands","otherGeospatial":"Kwajalein Atoll","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 167.69720077514648,\n 8.704062518222123\n ],\n [\n 167.7499008178711,\n 8.704062518222123\n ],\n [\n 167.7499008178711,\n 8.755812668914562\n ],\n [\n 167.69720077514648,\n 8.755812668914562\n ],\n [\n 167.69720077514648,\n 8.704062518222123\n ]\n ]\n ]\n }\n }\n ]\n}","issue":"Fall","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":244273,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":803647,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70214571,"text":"ofr20201087 - 2020 - Analyses on subpopulation abundance and annual number of maternal dens for the U.S. Fish and Wildlife Service on polar bears (Ursus maritimus) in the southern Beaufort Sea, Alaska","interactions":[],"lastModifiedDate":"2020-10-02T11:46:04.926688","indexId":"ofr20201087","displayToPublicDate":"2020-10-01T10:12:49","publicationYear":"2020","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":"2020-1087","displayTitle":"Analyses on Subpopulation Abundance and Annual Number of Maternal Dens for the U.S. Fish and Wildlife Service on Polar Bears (Ursus maritimus) in the Southern Beaufort Sea, Alaska","title":"Analyses on subpopulation abundance and annual number of maternal dens for the U.S. Fish and Wildlife Service on polar bears (Ursus maritimus) in the southern Beaufort Sea, Alaska","docAbstract":"

The long-term persistence of polar bears (Ursus maritimus) is threatened by sea-ice loss due to climate change, which is concurrently providing an opportunity in the Arctic for increased anthropogenic activities including natural resource extraction. Mitigating the risk of those activities, which can adversely affect the population dynamics of the southern Beaufort Sea (SBS) subpopulation, is an emerging challenge as polar bears become more reliant on land and come into more frequent contact with humans. The Marine Mammal Protection Act and Endangered Species Act require the U.S. Fish and Wildlife Service to determine whether industrial activities will have a negligible impact on the SBS subpopulation. Information important to making that determination includes estimates of subpopulation abundance and the number of maternal dens likely to be present in areas where industrial activities occur. We analyzed mark-recapture data collected from SBS polar bears sampled in Alaska during 2001–16 using multistate Cormack-Jolly-Seber models. Estimated survival rates were relatively high during 2001–03, lower during 2004–08, then higher during 2009–15 except for 2012. Estimated abundance in the Alaska part of the SBS was consistent with the estimated survival rates, declining from about 1,300 bears in 2003 to 525 bears in 2006 and then remaining generally stable during 2006–15. The point estimate for the Alaska part of the SBS in 2015, the last year in which abundance could be estimated, was 573 bears (95-percent credible interval = 232, 1,140 bears). To estimate the expected number of terrestrial dens likely to be present in a given region in a given year, we used a Bayesian modeling approach based on calculations derived from SBS demographic and denning data. We estimated that the entire SBS subpopulation produced 123 dens per year (median; 95-percent credible interval = 69, 198 dens), 66 (median; 95-percent credible interval = 35, 110 dens) of which were land-based. Most land-based dens were located between the Colville and Canning Rivers (which includes the Prudhoe Bay-Kuparuk industrial footprint), followed by the 1002 Area of the Arctic National Wildlife Refuge and the National Petroleum Reserve-Alaska.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201087","collaboration":"U.S. Geological Survey Wildlife Program","usgsCitation":"Atwood, T.C., Bromaghin, J.F., Patil, V.P., Durner, G.M., Douglas, D.C., and Simac, K.S., 2020, Analyses on subpopulation abundance and annual number of maternal dens for the U.S. Fish and Wildlife Service on polar bears (Ursus maritimus) in the southern Beaufort Sea, Alaska: U.S. Geological Survey Open-File Report 2020-1087, 16 p., https://doi.org/10.3133/ofr20201087.","productDescription":"Report: iv, 16 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-120083","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":378973,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds1121","text":"Data Series 1121","description":"DS 1121"},{"id":378971,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1087/coverthb.jpg"},{"id":378972,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1087/ofr20201087.pdf","text":"Report","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1087"},{"id":378974,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A9E5UP","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Multistate capture and search data from the southern Beaufort Sea polar bear subpopulation in Alaska, 2001-2016"}],"country":"United States","state":"Alaska","otherGeospatial":"Southern Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.642578125,\n 69.06856318696033\n ],\n [\n -140.9326171875,\n 69.06856318696033\n ],\n [\n -140.9326171875,\n 72.40899172812024\n ],\n [\n -158.642578125,\n 72.40899172812024\n ],\n [\n -158.642578125,\n 69.06856318696033\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508

","tableOfContents":"","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2020-10-01","noUsgsAuthors":false,"publicationDate":"2020-10-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":800371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bromaghin, Jeffrey F. 0000-0002-7209-9500 jbromaghin@usgs.gov","orcid":"https://orcid.org/0000-0002-7209-9500","contributorId":139899,"corporation":false,"usgs":true,"family":"Bromaghin","given":"Jeffrey","email":"jbromaghin@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":800372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patil, Vijay P. 0000-0002-9357-194X vpatil@usgs.gov","orcid":"https://orcid.org/0000-0002-9357-194X","contributorId":203676,"corporation":false,"usgs":true,"family":"Patil","given":"Vijay","email":"vpatil@usgs.gov","middleInitial":"P.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":800373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":800374,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":800375,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Simac, Kristin S. 0000-0002-4072-1940 ksimac@usgs.gov","orcid":"https://orcid.org/0000-0002-4072-1940","contributorId":131096,"corporation":false,"usgs":true,"family":"Simac","given":"Kristin","email":"ksimac@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":800376,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228380,"text":"70228380 - 2020 - An open-sourced, web-based application to improve our ability to understand hunter and angler purchasing behavior from license data","interactions":[],"lastModifiedDate":"2022-02-09T15:54:16.569092","indexId":"70228380","displayToPublicDate":"2020-10-01T09:45:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"An open-sourced, web-based application to improve our ability to understand hunter and angler purchasing behavior from license data","docAbstract":"

State fish and wildlife agencies rely on hunters and anglers (i.e., sportspersons) to fund management actions through revenue generated from license sales and excise taxes on hunting and fishing equipment. There is a need to develop new techniques that bridge the information gap on participation and provide agencies with an understanding of sportspersons at a resolution that can more directly inform efforts to engage sportspersons. Monitoring sportsperson participation using information about their license-purchasing behavior has the potential to reveal important patterns in recruitment (first-time purchase of a hunting or fishing license), retention (continued purchase of licenses across multiple years), and reactivation (purchase a license after several years with no purchases). Providing up-to-date information on what licenses are purchased, when and by whom may prove invaluable to managers and policy makers. We present a customizable, open-source, web-based application—huntfishapp—that allows the user to query and interact with a structured query language (SQL) hunting and fishing license database. The huntfishapp serves as an informational resource and tool that provides a framework to share information on license sales across an agency, with intent of increasing understanding of (a) sportspersons and (b) how management decisions affect sportspersons. Data dashboards, like the huntfishapp, allow agencies and non-governmental organizations to become more knowledgeable of their customer base and provide a greater understanding of management-decision effects on hunting and fishing participation.

","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0226397","usgsCitation":"Price, N.B., Chizinski, C.J., Fontaine, J.J., Pope, K.L., Rahe, M., and Rawlinson, J., 2020, An open-sourced, web-based application to improve our ability to understand hunter and angler purchasing behavior from license data: PLoS ONE, v. 15, no. 10, p. 1-17, https://doi.org/10.1371/journal.pone.0226397.","productDescription":"e0226397, 17 p.","startPage":"1","endPage":"17","ipdsId":"IP-111226","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":455159,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0226397","text":"Publisher Index Page"},{"id":395669,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-10-01","publicationStatus":"PW","contributors":{"editors":[{"text":"Xin, Baogui","contributorId":275367,"corporation":false,"usgs":false,"family":"Xin","given":"Baogui","email":"","affiliations":[],"preferred":false,"id":834054,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Price, Nathaniel B.","contributorId":264316,"corporation":false,"usgs":false,"family":"Price","given":"Nathaniel","email":"","middleInitial":"B.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":834042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chizinski, Christopher J.","contributorId":7178,"corporation":false,"usgs":false,"family":"Chizinski","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":834043,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fontaine, Joseph J. 0000-0002-7639-9156 jfontaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-9156","contributorId":3820,"corporation":false,"usgs":true,"family":"Fontaine","given":"Joseph","email":"jfontaine@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834044,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pope, Kevin L. 0000-0003-1876-1687","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":270762,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":834045,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rahe, Micaela","contributorId":275362,"corporation":false,"usgs":false,"family":"Rahe","given":"Micaela","email":"","affiliations":[{"id":56765,"text":"National Wild Turkey Federation","active":true,"usgs":false}],"preferred":false,"id":834046,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rawlinson, Jeff","contributorId":275363,"corporation":false,"usgs":false,"family":"Rawlinson","given":"Jeff","email":"","affiliations":[{"id":17640,"text":"Nebraska Game and Parks Commission","active":true,"usgs":false}],"preferred":false,"id":834047,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216471,"text":"70216471 - 2020 - Improved prediction of management-relevant groundwater discharge characteristics throughout river networks","interactions":[],"lastModifiedDate":"2020-11-20T13:56:50.942422","indexId":"70216471","displayToPublicDate":"2020-10-01T07:54:15","publicationYear":"2020","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":"Improved prediction of management-relevant groundwater discharge characteristics throughout river networks","docAbstract":"

Groundwater discharge zones connect aquifers to surface water, generating baseflow and serving as ecosystem control points across aquatic ecosystems. The influence of groundwater discharge on surface flow connectivity, fate and transport of contaminants and nutrients, and thermal habitat depends strongly on hydrologic characteristics such as the spatial distribution, age, and depth of source groundwater flow paths. Groundwater models have the potential to predict spatial discharge characteristics within river networks, but models are often not evaluated against these critical characteristics and model equifinality with respect to discharge processes is a known challenge. We quantify discharge characteristics across a suite of groundwater models with commonly used frameworks and calibration data. We developed a base model (MODFLOW‐NWT) for a 1,570‐km2 watershed in the northeastern United States and varied the calibration data, control of river‐aquifer exchange directionality, and resolution. Most models (n = 11 of 12) fit similarly to calibration metrics, but patterns in discharge location, flow path depth, and subsurface travel time varied substantially. We found (1) a 15% difference in the percent of discharge going to first‐order streams, (2) threefold variations in flow path depth, and (3) sevenfold variations in the subsurface travel times among the models. We recalibrated three models using a synthetic discharge location data set. Calibration with discharge location data reduced differences in simulated discharge characteristics, suggesting an approach to improved equifinality based on widespread field‐based mapping of discharge zones. Our work quantifying variation across common modeling approaches is an important step toward characterizing and improving predictions of groundwater discharge characteristics.

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