In 2018, the U.S. Geological Survey published an assessment of technically recoverable continuous oil and gas resources of the Eagle Ford Group and associated Cenomanian–Turonian strata in the U.S. Gulf Coast of Texas. Estimated ultimate recoveries (EURs) were calculated with production data from IHS MarkitTM using DeclinePlus software in the Harmony interface. These EURs were a major component of the aforementioned quantitative resource assessment fact sheet. The calculated mean EURs for each oil assessment unit (AU) ranged from 113,000 barrels of oil in the Cenomanian–Turonian Mudstone Continuous Oil AU to 223,000 barrels of oil in the Submarine Plateau-Karnes Trough Continuous Oil AU. The calculated mean EURs for each gas AU ranged from 2.261 billion cubic feet of gas in the Submarine Plateau-Karnes Trough Continuous Gas AU to 3.116 billion cubic feet of gas in the Eagle Ford Marl Continuous Gas AU.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205077","usgsCitation":"Leathers-Miller, H.M., 2020, Steps taken for calculating estimated ultimate recoveries of wells in the Eagle Ford Group and associated Cenomanian–Turonian strata, U.S. Gulf Coast, Texas, 2018: U.S. Geological Survey Scientific Investigations Report 2020–5077, 5 p., https://doi.org/10.3133/sir20205077.","productDescription":"5 p.","numberOfPages":"5","onlineOnly":"Y","ipdsId":"IP-102505","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":377009,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5077/sir20205077.pdf","text":"Report","size":"5.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5077"},{"id":377008,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5077/coverthb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.2626953125,\n 32.80574473290688\n ],\n [\n -98.85498046875,\n 30.35391637229704\n ],\n [\n -100.72265625,\n 29.22889003019423\n ],\n [\n -100.2392578125,\n 28.246327971048842\n ],\n [\n -99.51416015625,\n 27.527758206861886\n ],\n [\n -99.31640625,\n 27.078691552927534\n ],\n [\n -96.85546875,\n 28.70986084394286\n ],\n [\n -93.8232421875,\n 30.543338954230222\n ],\n [\n -92.35107421874999,\n 31.147006308556566\n ],\n [\n -94.2626953125,\n 32.80574473290688\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, Mail Stop 939
Denver, CO 80225
Hibernation is a strategy many species employ to survive periods of thermal stress or resource shortage (e.g., harsh thermal conditions, food limitations) and habitat requirements of hibernating species may differ between summer (the active season) and winter (during hibernation). Accounting for seasonal differences in habitat affinities will help ensure that management actions are more beneficial and land-use policies are more appropriate. The northern Idaho ground squirrel (Urocitellus brunneus) is a federally listed threatened species that is in decline and hibernates for approximately 8 months per year. We collared northern Idaho ground squirrels in Adams County, Idaho from 2013–2017. The majority of northern Idaho ground squirrels we collared selected hibernacula outside of the areas they used during the active season. Furthermore, habitat features of hibernacula locations differed from habitat features of active-season areas. Hibernacula locations had greater canopy closure compared to active-season locations (36.9% and 7.0% canopy closure, respectively) and hibernaculum habitat features (particularly distance to nearest log) influenced overwinter survival. Our results suggest that recovery efforts for northern Idaho ground squirrels should include protection and management for the full range of habitat conditions used throughout summer and winter. More broadly, we emphasize the need to identify and protect habitat during all seasons because habitat requirements can differ substantially during different portions of an animal's annual cycle and effective conservation will require management of year-round habitat needs.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21936","usgsCitation":"Goldberg, A.R., Conway, C.J., Mack, D.E., and Burak, G.S., 2020, Winter versus summer habitat selection in a threatened ground squirrel: The Wildlife Professional, v. 84, no. 8, p. 1548-1559, https://doi.org/10.1002/jwmg.21936.","productDescription":"12 p.","startPage":"1548","endPage":"1559","ipdsId":"IP-102406","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":497502,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","county":"Adams County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.37553141044718,\n 45.34398367526143\n ],\n [\n -117.37553141044718,\n 44.05501390269339\n ],\n [\n -116.10109120802274,\n 44.05501390269339\n ],\n [\n -116.10109120802274,\n 45.34398367526143\n ],\n [\n -117.37553141044718,\n 45.34398367526143\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"84","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Goldberg, Amanda R.","contributorId":363896,"corporation":false,"usgs":false,"family":"Goldberg","given":"Amanda","middleInitial":"R.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":952092,"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":952095,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mack, Diane Evans","contributorId":363902,"corporation":false,"usgs":false,"family":"Mack","given":"Diane","middleInitial":"Evans","affiliations":[{"id":56023,"text":"idfg","active":true,"usgs":false}],"preferred":false,"id":952094,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burak, Greg S.","contributorId":363893,"corporation":false,"usgs":false,"family":"Burak","given":"Greg","middleInitial":"S.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":952091,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216993,"text":"70216993 - 2020 - Detection of SARS-CoV-2 by RNAscope® in situ hybridization and immunohistochemistry techniques","interactions":[],"lastModifiedDate":"2020-12-28T14:14:10.993732","indexId":"70216993","displayToPublicDate":"2020-08-05T11:03:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":892,"text":"Archives of Virology","active":true,"publicationSubtype":{"id":10}},"title":"Detection of SARS-CoV-2 by RNAscope® in situ hybridization and immunohistochemistry techniques","docAbstract":"In situ hybridization (ISH) and immunohistochemistry (IHC) are essential tools to characterize SARS-CoV-2 infection and tropism in naturally and experimentally infected animals and also for diagnostic purposes. Here, we describe three RNAscope®-based ISH assays targeting the ORF1ab, spike, and nucleocapsid genes and IHC assays targeting the spike and nucleocapsid proteins of SARS-CoV-2.
Agassiz’s desert tortoise (Gopherus agassizii), a threatened species of the southwestern United States, has severely declined to the point where 76% of populations in critical habitat (Tortoise Conservation Areas) are below viability. The potential for rapid recovery of wild populations is low because females require 12–20 years to reach reproductive maturity and produce few eggs annually. We report on a 34‐year mark‐recapture study of tortoises initiated in 1979 at the Desert Tortoise Research Natural Area in the western Mojave Desert, California, USA, and provide substantive data on challenges faced by the species. In 1980, the United States Congress designated the Research Natural Area and protected the land from recreational vehicles, livestock grazing, and mining with a wildlife‐permeable fence. The 7.77‐km2 study area, centered on interpretive facilities, included land both within the Natural Area and outside the fence. We expected greater benefits to accrue to the tortoises and habitat inside compared to outside. Our objectives were to conduct a demographic study, analyze and model changes in the tortoise population and habitat, and compare the effectiveness of fencing to protect populations and habitat inside the fence versus outside, where populations and habitat were unprotected. We conducted surveys in spring in each of 7 survey years from 1979, when the fence was under construction, through 2012. We compared populations inside to those outside the fence by survey year for changes in distribution, structure by size and relative age, sex ratios, death rates of adults, and causes of death for all sizes of tortoises. We used a Bayesian implementation of a Jolly Seber model for mark‐recapture data. We modeled detection, density, growth and transition of tortoises to larger size‐age classes, movements from inside the protective fence to outside and vice versa, and survival. After the second and subsequent survey years, we added surveys to monitor vegetation and habitat changes, conduct health assessments, and collect data on counts of predators and predator sign. At the beginning of the study, counts and densities for all sizes of tortoises were high, but densities were approximately 24% higher inside the fence than outside. By 2002, the low point in densities, densities had declined 90% inside the fence and 95% outside. Between 2002 and 2012, the population inside the fence showed signs of improving with a 54% increase in density. Outside the fence, densities remained low. At the end of the study, when we considered the initial differences in location, densities inside the fence were roughly 2.5 times higher than outside. The pattern of densities was similar for male and female adults. When evaluating survival by blocks of years, survivorship was higher in 1979–1989 than in 1989–2002 (the low point) and highest from 2002 to 2012. Recruitment and survival of adult females into the population was important for growing the population, but survival of all sizes, including juveniles, was also critical.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wmon.1052","usgsCitation":"Berry, K.H., Yee, J.L., Shields, T.A., and Stockton, L., 2020, The catastrophic decline of tortoises at a fenced natural area: Wildlife Monographs, v. 205, no. 1, p. 1-53, https://doi.org/10.1002/wmon.1052.","productDescription":"53 p.","startPage":"1","endPage":"53","ipdsId":"IP-114548","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":455757,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wmon.1052","text":"Publisher Index Page"},{"id":436835,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BY0HVH","text":"USGS data release","linkHelpText":"Demography and Habitat of Desert Tortoises at the Desert Tortoise Research Natural Area, Western Mojave Desert, California (1978 - 2014)"},{"id":436836,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BY0HVH","text":"USGS data release","linkHelpText":"Demography and Habitat of Desert Tortoises at the Desert Tortoise Research Natural Area, Western Mojave Desert, California (1978 - 2014)"},{"id":385754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Desert Tortoise Research Natural Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.1634521484375,\n 34.97150033361733\n ],\n [\n -117.3504638671875,\n 34.97150033361733\n ],\n [\n -117.3504638671875,\n 35.48527461007853\n ],\n [\n -118.1634521484375,\n 35.48527461007853\n ],\n [\n -118.1634521484375,\n 34.97150033361733\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"205","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Berry, Kristin H. 0000-0003-1591-8394 kristin_berry@usgs.gov","orcid":"https://orcid.org/0000-0003-1591-8394","contributorId":437,"corporation":false,"usgs":true,"family":"Berry","given":"Kristin","email":"kristin_berry@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815990,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815991,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shields, Timothy A.","contributorId":190759,"corporation":false,"usgs":false,"family":"Shields","given":"Timothy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":815992,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stockton, Laura","contributorId":258217,"corporation":false,"usgs":false,"family":"Stockton","given":"Laura","email":"","affiliations":[{"id":52242,"text":"Bakersfield, CA","active":true,"usgs":false}],"preferred":false,"id":815993,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225726,"text":"70225726 - 2020 - Channel cross-section analysis for automated stream head identification","interactions":[],"lastModifiedDate":"2021-11-05T11:52:59.985895","indexId":"70225726","displayToPublicDate":"2020-08-05T06:50:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"Channel cross-section analysis for automated stream head identification","docAbstract":"Headwater streams account for more than half of the streams in the United States by length. The substantial occurrence and susceptibility to change of headwater streams makes regular updating of related maps vital to the accuracy of associated analysis and display. Here we present work testing new methods of completely automated remote headwater stream identification using metrics derived from channel Digital Elevation Model (DEM) cross-sections. A jump in standard deviation of curvature (sK) is found to correlate with the presence of stream heads. Field and remotely validated stream and channel initiation points from 4 diverse study areas in North Carolina as well as a simulated surface are used to test the sK findings. The sK value within individual catchments equal to 0.5*Tukey's upper inner fence is found to be a reliable threshold for identifying the upslope extent of channels in varied landscapes.
Mount Baker is the prominent andesitic stratocone that forms the youngest volcanic center in the Mount Baker volcanic field. Its heavily glaciated cone, rising to 3,286 meters, is an international landmark, dominating the skyline of Vancouver, British Columbia, even though the volcano is located 25 kilometers south of the international border. Mount Baker caught the attention of scientists and the public alike in 1975–76 during a period of increased steaming, thermal output, and near-vent lithic tephra falls. Although a magmatic eruption did not ensue, it awoke the populace to the possibility of renewed volcanic activity in the Cascade Range (the first since the 1914–17 eruptions of Lassen Peak, Calif.)—a possibility fulfilled just five short years later with the 1980 eruption of Mount St. Helens in southwest Washington. The 1980 Mount St. Helens eruption, with its dramatic edifice collapse, extraordinary pyroclastic density current, and catastrophic lahars, invigorated the scientific community into studying these then little-known processes. It also highlighted the need to better understand eruptive histories at other Cascade Range volcanoes in order to prepare for future eruptions.
The 1975 unrest also spawned one of the earliest volcano hazard assessments in the Cascade Range, which recognized the rich history of postglacial events at Mount Baker and identified the risk posed by volcanic mudflows, or lahars. The focus of this study is to more fully describe the late-glacial to present surficial geology, to better constrain the timing of events (including 19th-century floods), and to dovetail this history with Hildreth and others’ (2003) bedrock study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1865","usgsCitation":"Scott, K.M., Tucker, D.S., Riedel, J.L., Gardner, C.A., and McGeehin, J.P., 2020, Latest Pleistocene to present geology of Mount Baker Volcano, northern Cascade Range, Washington: U.S. Geological Survey Professional Paper 1865,\n170 p., https://doi.org/10.3133/pp1865.","productDescription":"xi, 170 p.","onlineOnly":"Y","ipdsId":"IP-068471","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":377020,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1865/coverthb.jpg"},{"id":377021,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1865/pp1865.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1865"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Baker","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.92214965820311,\n 48.70455661164196\n ],\n [\n -121.72233581542967,\n 48.70455661164196\n ],\n [\n -121.72233581542967,\n 48.84302835299516\n ],\n [\n -121.92214965820311,\n 48.84302835299516\n ],\n [\n -121.92214965820311,\n 48.70455661164196\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center
Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA, 98683
The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained solely from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project.
An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. As of 2019, the network consisted of 120 wells and piezometers. (A piezometer is a specialized well open to a specific depth in the aquifer, often of small diameter and nested with other piezometers screened at different depths.) The USGS, in cooperation with the ABCWUA, the New Mexico Office of the State Engineer, and Bernalillo County, measures water levels from the 120 wells and piezometers in the network; this report, prepared in cooperation with the ABCWUA, presents water-level data collected by USGS personnel at those 120 sites through water year 2019 (October 1, 2018, through September 30, 2019). Water levels that were collected from those discontinued wells in previous water years were published in previous USGS reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1129","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Beman, J.E., 2020, Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2019: U.S. Geological Survey Data Series 1129, 40 p., https://doi.org/10.3133/ds1129.","productDescription":"iii, 40 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-120239","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":377000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1129/coverthb.jpg"},{"id":377001,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1129/ds1129.pdf","text":"Report","size":"5.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1129"}],"country":"United States","state":"New Mexico","city":"Albuquerque","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.611083984375,\n 33.797408767572485\n ],\n [\n -105.941162109375,\n 33.797408767572485\n ],\n [\n -105.941162109375,\n 36.06686213257888\n ],\n [\n -107.611083984375,\n 36.06686213257888\n ],\n [\n -107.611083984375,\n 33.797408767572485\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Simple biometric data of fish aid fishery management tasks such as monitoring the structure of fish populations and regulating recreational harvest. While these data are foundational to fishery research and management, the collection of length and weight data through physical handling of the fish is challenging as it is time consuming for personnel and can be stressful for the fish. Recent advances in imaging technology and machine learning now offer alternatives for capturing biometric data. To investigate the potential of deep convolutional neural networks to predict biometric data, several regressors were trained and evaluated on data stemming from the FishL™ Recognition System and manual measurements of length, girth, and weight. The dataset consisted of 694 fish from 22 different species common to Laurentian Great Lakes. Even with such a diverse dataset and variety of presentations by the fish, the regressors proved to be robust and achieved competitive mean percent errors in the range of 5.5 to 7.6% for length and girth on an evaluation dataset. Potential applications of this work could increase the efficiency and accuracy of routine survey work by fishery professionals and provide a means for longer‐term automated collection of fish biometric data.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.6618","usgsCitation":"Bravata, N., Kelly, D., Eickholt, J., Bryan, J., Miehls, S.M., and Zielinski, D., 2020, Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish: Ecology and Evolution, v. 10, no. 17, p. 9313-9325, https://doi.org/10.1002/ece3.6618.","productDescription":"13 p.","startPage":"9313","endPage":"9325","ipdsId":"IP-114453","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":455761,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.6618","text":"Publisher Index Page"},{"id":436838,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90BIDOL","text":"USGS data release","linkHelpText":"Image and biometric data for fish from Great Lakes tributaries collected during spring 2019"},{"id":436837,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90BIDOL","text":"USGS data release","linkHelpText":"Image and biometric data for fish from Great Lakes tributaries collected during spring 2019"},{"id":381446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Michigan, Ohio","otherGeospatial":"Black Mallard River, Cheboygan River, Illinois River, Little Manistee River, Menominee River, Muskegon River, Ocqueoc River, Sandusky River, Tittabawassee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.428955078125,\n 40.588928169693745\n ],\n [\n -82.529296875,\n 40.588928169693745\n ],\n [\n -82.529296875,\n 46.210249600187225\n ],\n [\n -88.428955078125,\n 46.210249600187225\n ],\n [\n -88.428955078125,\n 40.588928169693745\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"17","noUsgsAuthors":false,"publicationDate":"2020-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Bravata, Nicholas","contributorId":245794,"corporation":false,"usgs":false,"family":"Bravata","given":"Nicholas","email":"","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":807042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelly, Dylan","contributorId":245795,"corporation":false,"usgs":false,"family":"Kelly","given":"Dylan","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":807043,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eickholt, Jesse","contributorId":245796,"corporation":false,"usgs":false,"family":"Eickholt","given":"Jesse","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":807044,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bryan, Janine","contributorId":245797,"corporation":false,"usgs":false,"family":"Bryan","given":"Janine","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":807045,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":807046,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zielinski, Daniel","contributorId":245798,"corporation":false,"usgs":false,"family":"Zielinski","given":"Daniel","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":807047,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211586,"text":"ofr20201053 - 2020 - Adjusted geomagnetic data—Theoretical basis and validation","interactions":[],"lastModifiedDate":"2020-08-04T20:32:20.375465","indexId":"ofr20201053","displayToPublicDate":"2020-08-04T12:30:00","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":"2020-1053","displayTitle":"Adjusted Geomagnetic Data—Theoretical Basis and Validation","title":"Adjusted geomagnetic data—Theoretical basis and validation","docAbstract":"Adjusted geomagnetic data are magnetometer measurements with provisional correction factors applied such that vector quantities are oriented in a local Cartesian frame in which the X axis points north, the Y axis points east, and the Z axis points down. These correction factors are determined from so-called absolute measurements, which are “ground truth” observations made in the field using specialized magnetometers and survey equipment that are (nearly) colocated with the automated and continuously running magnetic measurement instrumentation. Correction factors can be substantial, up to hundreds of nanoTeslas, depending on the geologic and geomagnetic characteristics of the observatory site. They also tend to evolve over time because of instrument response instability and changing site characteristics. Historically, correction factors were determined offline, up to 1 year or more post-measurement, and applied to raw measurements to produce “Definitive” data for scientific analysis. Growing demand for corrected real-time geomagnetic data to better support space weather operations motivated development of an “Adjusted” geomagnetic data product. Modern computational tools, and some notable practical concerns, dictated a transition to affine transformations in lieu of more traditional baseline corrections, as well as a calibration parameter estimation algorithm that is more robust and statistically optimal, and therefore better suited for automated and unsupervised execution. A theoretical basis for this algorithm is presented, along with a demonstration and validation based on a comparison of results obtained with traditional techniques. Discrepancies between Definitive corrected data and near real-time Adjusted data obtained using affine transformations are minimal, generally much less than 5 nanoTeslas per vector component, and less than 1 nanoTesla for the total field magnitude, which satisfies International Real-Time Magnetic Observatory Network (INTERMAGNET) standards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201053","usgsCitation":"Rigler, E.J., and Claycomb, A.E., 2020, Adjusted geomagnetic data—Theoretical basis and validation: U.S. Geological Survey Open-File Report 2020–1053, 19 p., https://doi.org/10.3133/ofr20201053.","productDescription":"iv, 19 p.","onlineOnly":"Y","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":376988,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1053/coverthb.jpg"},{"id":376989,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1053/ofr20201053.pdf","text":"Report","size":"2.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1053"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
Methane hydrate was synthesized from pure water ice and flash frozen seawater, with varying amounts of sand or silt added. Electrical conductivity was determined by impedance spectroscopy, using equivalent circuit modeling to separate the effects of electrodes and to gain insight into conduction mechanisms. Silt and sand increase the conductivity of pure hydrate, we infer by contaminant NaCl contributing to conduction in hydrate, to values in agreement with resistivities observed in well logs through hydrate The addition of silt and sand lowers the conductivity of hydrate synthesized from seawater by an amount consistent with Archie's Law. All samples were characterized using cryogenic scanning electron microscopy and energy dispersive spectroscopy, which show good connectivity of salt and brine phases. Electrical conductivity measurements of pure hydrate and hydrate mixed with silt during pressure‐induced dissociation supports previous conclusions that sediment increases dissociation rate.
There is a growing body of tools available for science support for determining the fate and behavior of industrial and agricultural chemicals that are rapidly injected (“spilled”) into aquatic environments. A 2-day roundtable-style workshop was held by the U.S. Geological Survey (USGS) in Middleton, Wisconsin, in December 2017 to describe and explore existing Federal science support for spill fate and behavior tools used for inland spills, ongoing and new fate and behavior studies, and science gaps in planning and response tools as part of the USGS Midcontinent Region’s efforts to include spill response as part of its strategic plans. A total of 28 attendees representing a variety of Federal, State, and regional entities presented on programs and tools used in various aspects of spill response. Most programs and tools discussed were for spills in riverine environments but tools and applications for spills in lakes, on land surfaces, in urban storm sewer networks, and groundwater also were discussed. A primary workshop focus was to facilitate communication and increase potential for future collaboration among agencies for inland spill science support. The role and need for more USGS science support within the inland spill community was discussed. Enhanced communication is needed within the USGS and the U.S. Department of the Interior science programs, as well as within and among other agencies that do emergency planning and response. A main conclusion of the workshop was that there are untapped resources of the USGS outlined in the agency’s science strategy that could strengthen science support for fate and behavior tools in inland areas, especially in the Upper Mississippi River, Ohio River, and Great Lakes Basins where large freshwater resources overlap with dense corridors of oil and hazardous substances, with transportation networks, and with large populations centers.
Fate and behavior tools are being developed quickly for inland spill response by multiple Federal agencies in partnership with local and regional entities. Applicability of these tools ranges from planning and preparedness, to the early stages of spill response for protection of human life and property, and to the application of monitoring and models to assess the long-term consequences of spills. Key findings from the workshop, with an emphasis on potential further development of USGS science support, include the following:
•The national and regional response to spills occurs within an established system that must be respected by all parties involved in spill response. The USGS’s role is to support spill responders who are physically working at a spill scene, deploying booms and using other efforts to contain and recover spilled materials.
•The USGS has tools that have been used throughout spill response operations, from early response to recovery and restoration. Developing a more formal role for the USGS to participate in science support for inland spills on a consistent basis is a desired outcome. This will require the USGS to improve internal and external communication and would be best accomplished by assigning one or more coordinator positions within the agency to plan and oversee USGS spill-response efforts. More involvement of the USGS on National and Regional Response Teams, especially in the realm of the Science and Technology Subcommittees, will gofar in increasing external communication and integration of fate and behavior tools.
•Rapid response to spills requires modeling and mapping of plumes and associated time-of-travel estimation for a range of stream sizes across the United States. Many existing models use USGS streamgage data and the USGS National Hydrography Dataset. Nearly all existing models would benefit from updated linkages to USGS StreamStats and its soon-to-be released time-of-travel estimates,real-time velocity, stream morphology, and slope data. Integrating USGS tools with those from other agencies could be done to better serve the larger spill response community.
• A problem is that existing models to rapidly predict plume extent, as well as more followup/longer-term fate and transport models, can be unknown or unavailable to spill responders. Thus, creating and strengthening linkages among USGS scientists skilled at using these tools is needed to support spill response with the on-scene responders.
• Research for inland spill fate and behavior done outside of an immediate spill response can assist with spill planning and preparedness by (1) revealing sites likely to experience spills in the future (high-risk sites) and (2) understanding how a spilled substance might behave under a range of environmental conditions. However, USGS research on this topic has been scarce and subject to funding availability. Examples include the 2010 Line 6B Spill release into the Kalamazoo River in Michigan, where the USGS provided science support for a variety of fate and behavior tools for stream and impoundment environments. A long-term research site in Bemidji, Minnesota, provides important insights into transformations and longevity of spilled oil in groundwater and groundwater-surface water interactions.
• Linking stream models to other components of this inland environment, including groundwater, overland flow, and karst, is needed. Stream network data can be linked to underground conduits such as storm sewers and karst groundwater systems. Stream models can also be linked with geospatial data such as that contained in U.S. Environmental Protection Agency’s
interactive mapping tools.
• The USGS is uniquely qualified to collect water-quality data during spills in the United States because of its many geographically dispersed water science centers, its knowledge and preparedness for flood measurement and documentation, and its cadre of skilled water-quality employees. Rapid-deployment gages, used for floods, could also be used for spills if they included spill-specific sensors. Coordinated expertise at USGS water and environmental science centers can be used for monitoring spill effects and for assessing risk to water quality and ecological communities.
• Scientists at the USGS have proven capable of providing science coordination and technical assistance within the Incident Command Structure at the request of the lead on-scene coordinator. This external coordination, as well as internal communication within USGS Water, Hazards, and Ecosystems Mission Areas, could be improved by establishing and naming a USGS spills coordinator. Scott Morlock, Jo Ellen Hinck, and Faith Fitzpatrick are currently (2017) serving in informal coordination roles in addition to their traditional duties.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201063","usgsCitation":"Sullivan, D.J., and Fitzpatrick, F.A., 2020, Fate and behavior tools related to inland spill response—Workshop on the U.S. Geological Survey’s role in Federal science support: U.S. Geological Survey Open-File Report 2020–1063, 22 p., https://doi.org/10.3133/ofr20201063.","productDescription":"v, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-111089","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1063/ofr20201063.pdf","text":"Report","size":"8.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1063"},{"id":376919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1063/coverthb.jpg"}],"contact":"Director, Upper Midwest Water Science Center
U.S Geological Survey
8505 Research Way
Middleton, WI 53562
Aquatic insects exhibit complex life cycles that include egg, larval, adult, and, in some instances, pupal stages. Disturbances at any of these life stages can affect overall population dynamics. Yet, efforts to understand the effects of disturbances, such as hydrologic alterations, overwhelmingly focus on the larval life stage of aquatic insects. We evaluated the potential for load-following flows associated with hydroelectric power production to act as a population bottleneck for aquatic insects via reductions in the availability and temporal persistence of optimal oviposition habitats. Specifically, we quantified the oviposition habitat selectivity of Baetis spp. (Baetidae), Brachycentrus occidentalis (Brachycentridae), Chironomidae (Diptera), and Hydropsyche occidentalis (Hydropsychidae) downstream of Flaming Gorge Dam, Utah, USA. We found that all taxa except H. occidentalis preferentially laid eggs on large emergent substrates located along the river edge. Peak discharge associated with load-following flows substantially reduced the number of emergent substrates available for oviposition, and daily low flows exposed eggs in these habitats to desiccation and drying. When subjected to experimental drying, both Baetis and H. occidentalis eggs experienced nearly 100% mortality after 2 h, whereas most B. occidentalis remained viable after 8 h. Our paired field and experimental results are consistent with the hypothesis that load-following flows from hydroelectric dams produce a population bottleneck for aquatic insects by short circuiting recruitment processes. Environmental flows that seek to improve the health of tailwater aquatic insect populations would benefit from consideration of habitat requirements for all life stages of aquatic insects.
","language":"English","publisher":"University of Chicago Press Journals","doi":"10.1086/710237","usgsCitation":"Miller, S.W., Schroer, M., Fleri, J.R., and Kennedy, T.A., 2020, Macroinvertebrate oviposition habitat selectivity and egg-mass desiccation tolerances: Implications for population dynamics in large regulated rivers: Freshwater Science, v. 39, no. 3, p. 584-599, https://doi.org/10.1086/710237.","productDescription":"16 p.","startPage":"584","endPage":"599","onlineOnly":"N","ipdsId":"IP-112469","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":455768,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1086/710237","text":"Publisher Index Page"},{"id":377107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Green River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.52407836914062,\n 40.84706035607122\n ],\n [\n -109.10659790039062,\n 40.84706035607122\n ],\n [\n -109.10659790039062,\n 40.93841495689795\n ],\n [\n -109.52407836914062,\n 40.93841495689795\n ],\n [\n -109.52407836914062,\n 40.84706035607122\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Scott W.","contributorId":237002,"corporation":false,"usgs":false,"family":"Miller","given":"Scott","email":"","middleInitial":"W.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":794962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schroer, Matt","contributorId":237003,"corporation":false,"usgs":false,"family":"Schroer","given":"Matt","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":794963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fleri, Jesse R.","contributorId":237004,"corporation":false,"usgs":false,"family":"Fleri","given":"Jesse","email":"","middleInitial":"R.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":794964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Theodore A. 0000-0003-3477-3629 tkennedy@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":167537,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","email":"tkennedy@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":794965,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209146,"text":"fs20203020 - 2020 - Water resources of Evangeline Parish, Louisiana","interactions":[],"lastModifiedDate":"2020-08-04T20:20:14.724487","indexId":"fs20203020","displayToPublicDate":"2020-08-04T09:02:52","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3020","displayTitle":"Water Resources of Evangeline Parish, Louisiana","title":"Water resources of Evangeline Parish, Louisiana","docAbstract":"Information concerning the availability, use, and quality of water in Evangeline Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 282.66 million gallons per day (Mgal/d) of water were withdrawn in Evangeline Parish, including about 122.05 Mgal/d from groundwater sources and 160.61 Mgal/d from surface-water sources. Withdrawals for agricultural use, composed of aquaculture, general irrigation, livestock, and rice irrigation, accounted for 45 percent (126.86 Mgal/d) of the total water withdrawn. Withdrawals for power-generation use accounted for about 52 percent (146.33 Mgal/d) of the total water withdrawn. Other categories of use included public supply, industry, and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 1980.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203020","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Murphy, C.J., and White, V.E., 2020, Water resources of Evangeline Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3020, 6 p., https://doi.org/10.3133/fs20203020.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103346","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":376884,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"},{"id":376882,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3020/coverthb.jpg"},{"id":376883,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3020/fs20203020.pdf","text":"Report","size":"1.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3020"}],"country":"United States","state":"Louisiana","county":"Evangeline Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-92.2809,30.9653],[-92.2811,30.9365],[-92.2381,30.8924],[-92.2377,30.8486],[-92.2132,30.8487],[-92.2127,30.7948],[-92.2079,30.7889],[-92.2073,30.7848],[-92.1977,30.7798],[-92.1918,30.7785],[-92.187,30.7758],[-92.1816,30.7694],[-92.1774,30.7685],[-92.1694,30.7677],[-92.1729,30.6758],[-92.175,30.6762],[-92.1797,30.6661],[-92.1861,30.667],[-92.1887,30.6647],[-92.1945,30.6596],[-92.2009,30.6564],[-92.2008,30.6477],[-92.205,30.639],[-92.2034,30.6372],[-92.2055,30.6353],[-92.2044,30.6331],[-92.206,30.6299],[-92.2038,30.6257],[-92.2064,30.6216],[-92.2107,30.6198],[-92.2117,30.6129],[-92.2113,30.569],[-92.2622,30.5682],[-92.263,30.5385],[-92.2795,30.5388],[-92.4148,30.5405],[-92.4227,30.5386],[-92.4285,30.5363],[-92.4397,30.5362],[-92.4508,30.532],[-92.4592,30.5246],[-92.4622,30.5163],[-92.4659,30.5108],[-92.4637,30.5008],[-92.4657,30.4967],[-92.471,30.4939],[-92.4805,30.4924],[-92.4874,30.4878],[-92.4942,30.4818],[-92.6304,30.4827],[-92.6305,30.4859],[-92.6284,30.4896],[-92.6237,30.4929],[-92.6232,30.4974],[-92.618,30.5021],[-92.6165,30.5067],[-92.6176,30.5135],[-92.6246,30.5185],[-92.6241,30.5208],[-92.6162,30.5259],[-92.6051,30.531],[-92.6,30.5434],[-92.5958,30.5457],[-92.5948,30.5517],[-92.5932,30.5554],[-92.588,30.5559],[-92.5859,30.5618],[-92.5844,30.5683],[-92.5871,30.5719],[-92.5903,30.5732],[-92.593,30.5796],[-92.5979,30.5832],[-92.5986,30.8726],[-92.5989,30.8945],[-92.5658,30.8948],[-92.5605,30.899],[-92.5537,30.9031],[-92.5484,30.9032],[-92.5451,30.9009],[-92.5366,30.8978],[-92.5275,30.8997],[-92.5253,30.8943],[-92.5142,30.8953],[-92.4961,30.9037],[-92.484,30.9138],[-92.4798,30.9226],[-92.4861,30.9536],[-92.484,30.9559],[-92.4728,30.9587],[-92.4664,30.9574],[-92.4568,30.9589],[-92.451,30.9626],[-92.4404,30.9686],[-92.4271,30.9733],[-92.4154,30.9788],[-92.4123,30.9853],[-92.405,30.994],[-92.3948,30.9968],[-92.3869,31.0033],[-92.3784,31.0029],[-92.3746,30.9974],[-92.3676,30.9916],[-92.3606,30.9925],[-92.3601,30.9898],[-92.3611,30.988],[-92.3579,30.9848],[-92.3419,30.9817],[-92.3408,30.9794],[-92.3418,30.9758],[-92.345,30.9739],[-92.3439,30.9703],[-92.3316,30.9736],[-92.3252,30.9704],[-92.331,30.9635],[-92.3171,30.9636],[-92.315,30.9655],[-92.2809,30.9653]]]},\"properties\":{\"name\":\"Evangeline\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Information concerning the availability, use, and quality of water in Avoyelles Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 70 million gallons per day (Mgal/d) of water were withdrawn in Avoyelles Parish, including about 59.27 Mgal/d from groundwater sources and 10.95 Mgal/d from surface-water sources. Withdrawals for agricultural use—composed of aquaculture, general irrigation, livestock, and rice irrigation—accounted for 93 percent (65.59 Mgal/d) of the total water withdrawn. Other categories of use included public supply and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 2014.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203011","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., 2020, Water resources of Avoyelles Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3011, 6 p., https://doi.org/10.3133/fs20203011.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-102165","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":376881,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 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Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Passive acoustic monitoring using autonomous recording units (ARUs) is a fast-growing area of wildlife research especially for rare, cryptic species that vocalize. Northern Spotted Owl (Strix occidentalis caurina) populations have been monitored since the mid-1980s using mark–recapture methods. To evaluate an alternative survey method, we used ARUs to detect calls of Northern Spotted Owls and Barred Owls (S. varia), a congener that has expanded its range into the Pacific Northwest and threatens Northern Spotted Owl persistence. We set ARUs at 30 500-ha hexagons (150 ARU stations) with recent Northern Spotted Owl activity and high Barred Owl density within Northern Spotted Owl demographic study areas in Oregon and Washington, and set ARUs to record continuously each night from March to July, 2017. We reviewed spectrograms (visual representations of sound) and tagged target vocalizations to extract calls from ~160,000 hr of recordings. Even in a study area with low occupancy rates on historical territories (Washington’s Olympic Peninsula), the probability of detecting a Northern Spotted Owl when it was present in a hexagon exceeded 0.95 after 3 weeks of recording. Environmental noise, mainly from rain, wind, and streams, decreased detection probabilities for both species over all study areas. Using demographic information about known Northern Spotted Owls, we found that weekly detection probabilities of Northern Spotted Owls were higher when ARUs were closer to known nests and activity centers and when owls were paired, suggesting passive acoustic data alone could help locate Northern Spotted Owl pairs on the landscape. These results demonstrate that ARUs can effectively detect Northern Spotted Owls when they are present, even in a landscape with high Barred Owl density, thereby facilitating the use of passive, occupancy-based study designs to monitor Northern Spotted Owl populations.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duaa017","usgsCitation":"Duchac, L.S., Lesmeister, D., Dugger, K.M., Ruff, Z.J., and Davis, R.J., 2020, Passive acoustic monitoring effectively detects Northern Spotted Owls and Barred Owls over a range of forest conditions: Condor, v. 122, no. 3, duaa017, 22 p., https://doi.org/10.1093/condor/duaa017.","productDescription":"duaa017, 22 p.","ipdsId":"IP-113895","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":455771,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duaa017","text":"Publisher Index Page"},{"id":396168,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Klamath Mountains, Olympic Peninsula, Oregon Coast Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.51904296875,\n 47.24194882163242\n ],\n [\n -122.838134765625,\n 47.24194882163242\n ],\n [\n -122.838134765625,\n 48.23199134320962\n ],\n [\n -124.51904296875,\n 48.23199134320962\n ],\n [\n -124.51904296875,\n 47.24194882163242\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.12353515624999,\n 43.731414013769\n ],\n [\n -123.321533203125,\n 43.731414013769\n ],\n [\n -123.321533203125,\n 44.6061127451739\n ],\n [\n -124.12353515624999,\n 44.6061127451739\n ],\n [\n -124.12353515624999,\n 43.731414013769\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.64013671874999,\n 42.415346114253616\n ],\n [\n -122.64038085937499,\n 42.415346114253616\n ],\n [\n -122.64038085937499,\n 43.004647127794435\n ],\n [\n -123.64013671874999,\n 43.004647127794435\n ],\n [\n -123.64013671874999,\n 42.415346114253616\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-04-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Duchac, Leila S.","contributorId":279674,"corporation":false,"usgs":false,"family":"Duchac","given":"Leila","email":"","middleInitial":"S.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":835345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lesmeister, Damon B.","contributorId":279675,"corporation":false,"usgs":false,"family":"Lesmeister","given":"Damon B.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":835346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dugger, Katie M. 0000-0002-4148-246X","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":36037,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"","middleInitial":"M.","affiliations":[{"id":517,"text":"Oregon Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":835344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruff, Zachary J.","contributorId":279676,"corporation":false,"usgs":false,"family":"Ruff","given":"Zachary","email":"","middleInitial":"J.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":835347,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davis, Raymond J.","contributorId":279677,"corporation":false,"usgs":false,"family":"Davis","given":"Raymond","email":"","middleInitial":"J.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":835348,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211424,"text":"ofr20201073 - 2020 - Ecological forecasting—21st century science for 21st century management","interactions":[],"lastModifiedDate":"2024-03-04T18:30:12.945694","indexId":"ofr20201073","displayToPublicDate":"2020-08-04T07:20:00","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":"2020-1073","displayTitle":"Ecological Forecasting—21st Century Science for 21st Century Management","title":"Ecological forecasting—21st century science for 21st century management","docAbstract":"Natural resource managers are coping with rapid changes in both environmental conditions and ecosystems. Enabled by recent advances in data collection and assimilation, short-term ecological forecasting may be a powerful tool to help resource managers anticipate impending near-term changes in ecosystem conditions or dynamics. Managers may use the information in forecasts to minimize the adverse effects of ecological stressors and optimize the effectiveness of management actions. To explore the potential for ecological forecasting to enhance natural resource management, the U.S. Geological Survey (USGS) convened a workshop titled \"Building Capacity for Applied Short-Term Ecological Forecasting\" on May 29—31, 2019, with participants from several Federal agencies, including the Bureau of Land Management, the U.S. Fish and Wildlife Service, the National Park Service, and the National Oceanic and Atmospheric Administration as well as all mission areas within the USGS.
Participants broadly agreed that short-term ecological forecasting—on the order of days to years into the future—has tremendous potential to improve the quality and timeliness of information available to guide resource management decisions. Participants considered how ecological forecasting could directly affect their agency missions and specified numerous critical tools for addressing natural resource management concerns in the 21st century that could be enhanced by ecological forecasting. Given this breadth of possible applications for forecast products, participants developed a repeatable framework for evaluating potential value of a forecast product for enhancing resource management. Applying that process to a large list of forecast ideas that were developed in a brainstorming session, participants identified a small set of promising forecast products that illustrate the value of ecological forecasting for informing resource management. Workshop outcomes also include insights about important likely obstacles and next steps. In particular, reliable production and delivery of operational ecological forecasts will require a sustained commitment by research agencies, in partnership with resource management agencies, to maintain and improve forecasting tools and capabilities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201073","usgsCitation":"Bradford, J.B., Weltzin, J.F., McCormick, M., Baron, J., Bowen, Z., Bristol, S., Carlisle, D., Crimmins, T., Cross, P., DeVivo, J., Dietze, M., Freeman, M., Goldberg, J., Hooten, M., Hsu, L., Jenni, K., Keisman, J., Kennen, J., Lee, K., Lesmes, D., Loftin, K., Miller, B.W., Murdoch, P., Newman, J., Prentice, K.L., Rangwala, I., Read, J., Sieracki, J., Sofaer, H., Thur, S., Toevs, G., Werner, F., White, C.L., White, T., and Wiltermuth, M., 2020, Ecological forecasting—21st century science for 21st century management: U.S. Geological Survey Open-File Report 2020–1073, 54 p., https://doi.org/10.3133/ofr20201073.","productDescription":"vii, 54 p.","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114740","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":433,"text":"National Phenology Network","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":376787,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1073/ofr20201073.pdf","text":"Report","size":"598 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1073"},{"id":376786,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1073/coverthb.jpg"}],"contact":"Director, Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
The accurate mapping of streams and their streamflow conditions in terms of presence or absence of surface water is important to both understanding physical, chemical, and biological processes in streams and to managing land, water, and ecological resources. This document describes a field form, FLOwPER (FLOw PERmanence), available within a mobile application (app), for standardized data collection of the presence or absence of surface flow in streams. The FLOwPER Database is a publicly available geodataset that can be used for research and management applications. This document provides instructions on how to (1) access and download the FLOwPER field form within the mobile app service, (2) use and complete a FLOwPER field form, and (3) view and download data from the FLOwPER Database.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201075","collaboration":"Prepared in cooperation with the United States Forest Service and the Bureau of Land Management","usgsCitation":"Jaeger, K.L., Burnett, J., Heaston, E.D., Wondzell, S.M., Chelgren, N., Dunham, J.B., Johnson, S., and Brown, M., 2020, FLOwPER user guide—For collection of FLOw PERmanence field observations: U.S. Geological Survey Open-File Report 2020–1075, 40 p., https://doi.org/10.3133/ofr20201075.","productDescription":"Report: vi, 40 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-118616","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":436839,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13WFKYW","text":"USGS data release","linkHelpText":"FLOwPER Database: StreamFLOw PERmanence field observations, Jan 2021 - Dec 2021"},{"id":407336,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5edea67582ce7e579c6e5845","text":"USGS data release","description":"USGS data release","linkHelpText":"FLOwPER Database: StreamFLOw PERmanence Field Observations"},{"id":376985,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1075/coverthb.jpg"},{"id":377862,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1075/ofr20201075_appendix01.pdf","text":"Appendix 1","size":"507 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1075 Appendix 1"},{"id":376986,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1075/ofr20201075.pdf","text":"Report","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1075"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The Midcontinent Rift System (MRS) of North America is one of the world’s largest continental rifts and has an age of 1.1 Ga (giga-annum). The MRS hosts a diverse suite of magmatic and hydrothermal mineral deposits in the Lake Superior region where rift rocks are exposed at or near the surface. As part of the construction of a database summarizing information on mineral deposits in the MRS, data from regional mineral deposits were downloaded from the U.S. Geological Survey (USGS) Mineral Resources Data System (MRDS), the USGS Mineral Deposit Database (USMIN), and the Ontario Ministry of Energy, Northern Development and Mines Mineral Deposit Inventory (MDI). Deposits related to MRS rocks or mineralizing events were identified and compiled into a database to develop a space/time classification for MRS-related mineral deposits. Information from MRDS, USMIN, and MDI records and from the extensive literature describing MRS mineral deposits was used to classify each entry by deposit type, host rock age and type, and estimated mineralization age. Most deposits were readily classified because of unique mineralogy, location, or well-constrained host rock. These deposits were then put into a tectonic evolutionary framework for the MRS, which showed that many deposits formed within discrete spatial and temporal stages of rift evolution.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201069","usgsCitation":"Woodruff, L.G., Schulz, K.J., Dicken, C.L., and Nicholson, S.W., 2020, Mineral resource database for deposits related to the Mesoproterozoic Midcontinent Rift System, United States and Canada: U.S. Geological Survey Open-File Report 2020–1069, 20 p., https://doi.org/10.3133/ofr20201069.","productDescription":"Report: vi, 20 p.; 2 Tables","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113694","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":436840,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HTATKY","text":"USGS data release","linkHelpText":"Database of mineral deposits related to the Mesoproterozoic Midcontinent Rift System (MRS) in the northern United States and northern Ontario, Canada"},{"id":376912,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1069/ofr20201069_table1.csv","text":"Table 1","size":"171 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Database of mineral deposits related to the Mesoproterozoic Midcontinent Rift System (MRS) in the northern United States and northern Ontario, Canada"},{"id":376911,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1069/ofr20201069_table1.xlsx","text":"Table 1","size":"124 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Database of mineral deposits related to the Mesoproterozoic Midcontinent Rift System (MRS) in the northern United States and northern Ontario, Canada"},{"id":376909,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1069/coverthb.jpg"},{"id":376910,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1069/ofr20201069.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1069"}],"country":"United States, Canada","otherGeospatial":"Mesoproterozoic Midcontinent Rift System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.2216796875,\n 40.245991504199026\n ],\n [\n -81.8701171875,\n 50.792047064406866\n ],\n [\n -96.6357421875,\n 51.23440735163459\n ],\n [\n -96.1083984375,\n 43.45291889355465\n ],\n [\n -97.5146484375,\n 43.739352079154706\n ],\n [\n -97.55859375,\n 41.541477666790286\n ],\n [\n -99.931640625,\n 41.376808565702355\n ],\n [\n -100.1513671875,\n 37.16031654673677\n ],\n [\n -94.7021484375,\n 37.09023980307208\n ],\n [\n -94.833984375,\n 39.53793974517628\n ],\n [\n -82.2216796875,\n 40.245991504199026\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Coordinator, Mineral Resources Program
U.S. Geological Survey
913 National Center
Reston, VA 20192
Impacts of land use, specifically soil disturbance, are linked to reductions of soil organic carbon (SOC) stocks. Correspondingly, ecosystem restoration is promoted to sequester SOC to mitigate anthropogenic greenhouse gas emissions, which are exacerbating global climate change. Restored wetlands have relatively high potential to sequester carbon compared to other ecosystems, but SOC accumulation rates are variable, which leads to high uncertainty in sequestration rates. To assess soil properties and carbon sequestration rates of freshwater mineral soil wetlands, we analyzed an extensive database of SOC concentrations from the Prairie Pothole Region (549 wetlands over 160,000 km2), which is considered one of the largest wetland ecosystems in North America. We demonstrate that SOC of wetland catchments varies among inner, transition, toe slope, and upland landscape positions (LSPs), as well as among land uses and soil depth segments. Soil organic carbon concentrations were greatest in the inner portion of the catchment (66 Mg ha−1) and progressively decrease towards the upland LSP (43 Mg ha−1). We also conducted a regional extrapolation based on LSP- and land-use-specific SOC stocks, and estimated that wetland and upland areas of PPR wetland catchments contain 141 and 178 Tg of SOC in the upper 15 cm of the soil profile, respectively. Regressing SOC by restoration age (years restored) showed that sequestration rates, which differ by LSP and depth, ranged from 0.35 to 1.10 Mg ha−1 year−1. Using these SOC sequestration rates, along with data from natural and cropland reference sites, we estimated that it takes 20 to 64 years for SOC levels of restored wetlands to return to natural reference conditions, depending on LSP and depth segment. Accounting for LSP reduces uncertainty and should refine future assessments of the greenhouse gas mitigation potential from wetland restoration.
Globally accelerating frequency and extent of wildfire threatens the persistence of specialist wildlife species through direct loss of habitat and indirect facilitation of exotic invasive species. Habitat specialists may be especially prone to rapidly changing environmental conditions because their ability to adapt lags behind the rate of habitat alteration. As a result, these populations may become increasingly susceptible to ecological traps by returning to suboptimal breeding habitats that were dramatically altered by disturbance. We demonstrate a multistage modeling approach that integrates habitat selection and survival during the key nesting life‐stage of a bird species of high conservation concern, the greater sage‐grouse (Centrocercus urophasianus; hereafter, sage‐grouse). We applied these spatially explicit models to a spatiotemporally robust dataset of sage‐grouse nest locations and fates across wildfire‐altered sagebrush ecosystems of the Great Basin ecoregion, western United States. Female sage‐grouse exhibited intricate habitat selection patterns that varied across regional gradients of ecological productivity among sagebrush communities, but often selected nest sites that disproportionately resulted in nest failure. For example, 23% of nests occurred in wildfire‐affected habitats characterized by reduced sagebrush cover and greater composition of invasive annual grasses. We found survival of nests was negatively associated with wildfire‐affected areas, but positively associated with higher elevations with increased ruggedness and overall shrub cover. Strong site fidelity likely drove sage‐grouse to continue nesting in habitats degraded by wildfire. Hence, increasing frequency and extent of wildfire may contribute disproportionately to reduced reproductive success by creating ecological traps that act as population sinks. Identifying such habitat mismatches between selection and survival facilitates deeper understanding of the mechanisms driving reduced geographic niche space and population decline at broad spatiotemporal scales, while guiding management actions to areas that would be most beneficial to the species.