The Taupo Volcanic Zone (TVZ), New Zealand, is the most productive area of explosive silicic volcanism in the world. Faulted early and middle Pleistocene volcanic products are generally concealed beneath voluminous, generally unfaulted, younger volcanic products. An exception is the southeast margin of the TVZ where the two parallel, northeast-trending Paeroa and Te Weta Fault blocks expose Quaternary volcanic products consisting predominantly of caldera-related, rhyolitic ignimbrites and lacustrine sediments. The Taupo-Reporoa Basin is situated along the eastern part of the map area, and its northernmost part underwent collapse to form Reporoa Caldera.
The Paeroa Fault block is the largest exposed fault block within the TVZ, and it encompasses early and middle Pleistocene ignimbrites and sedimentary deposits that are buried throughout the Taupo-Reporoa Basin to the east. This map displays the volcanic and sedimentary geology of ~430 km2 of the Paeroa Fault block and the adjacent Te Weta Fault block at a scale of 1:50,000. Volcanic and sedimentary rocks are divided into the Reporoa Group, Whakamaru Group, and Huka Group (from oldest to youngest), which are overlain by relatively unfaulted late Pleistocene and Holocene surficial volcanic and sedimentary deposits.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201021","usgsCitation":"Downs, D.T., Leonard, G.S., Wilson, C.J.N., and Rowland, J.V., 2020, Geologic map of the Paeroa Fault block and surrounding area, Taupo Volcanic Zone, New Zealand: U.S. Geological Survey Open-File Report 2020–1021, scale 1:50,000, https://doi.org/10.3133/ofr20201021.","productDescription":"1 Map: 50.22 x 34.79 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-107861","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":373326,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DYBBGX","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"Database for the geologic map of the Paeroa fault block and surrounding area, Taupo Volcanic Zone, New Zealand"},{"id":373324,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1021/coverthb.jpg"},{"id":373325,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1021/ofr20201021.pdf","text":"Sheet","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1021"}],"country":"New Zealand ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 165.948486328125,\n -46.72103466129568\n ],\n [\n 170.7550048828125,\n -46.72103466129568\n ],\n [\n 170.7550048828125,\n -44.15462243076732\n ],\n [\n 165.948486328125,\n -44.15462243076732\n ],\n [\n 165.948486328125,\n -46.72103466129568\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory staff
Alaska Volcano Observatory
4210 University Drive
Anchorage, AK 99508
Information concerning the availability, use, and quality of water in Union 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 4.88 million gallons per day (Mgal/d) of water were withdrawn in Union Parish: 4.70 Mgal/d from groundwater sources and 0.18 Mgal/d from surface-water sources. Withdrawals for public-supply use accounted for about 89 percent (4.36 Mgal/d) of the total water withdrawn. Other categories of use included industrial, rural domestic, livestock, rice irrigation, and general irrigation. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 2000 at about 8.89 Mgal/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203002","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Robinson, A.L., 2020, Water resources of Union Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3002, 6 p., https://doi.org/10.3133/fs20203002.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103356","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":399197,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109796.htm"},{"id":373339,"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":373338,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3002/fs20203002.pdf","text":"Report","size":"835 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3002"},{"id":373337,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3002/coverthb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Union Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-92.0647,33.0089],[-92.0641,33.0021],[-92.0717,32.997],[-92.0711,32.9875],[-92.0771,32.9861],[-92.082,32.9815],[-92.0754,32.9738],[-92.0759,32.9697],[-92.083,32.9692],[-92.0857,32.9651],[-92.0835,32.9619],[-92.0851,32.9569],[-92.0791,32.9565],[-92.0807,32.951],[-92.0779,32.9465],[-92.0767,32.9342],[-92.0728,32.9251],[-92.0739,32.916],[-92.0798,32.9091],[-92.0776,32.8968],[-92.0786,32.8877],[-92.084,32.8777],[-92.0751,32.8604],[-92.0663,32.85],[-92.0564,32.8446],[-92.0558,32.8378],[-92.0645,32.8341],[-92.0651,32.8313],[-92.0601,32.8277],[-92.0601,32.8241],[-92.0661,32.8213],[-92.0633,32.814],[-92.0572,32.805],[-92.0545,32.7981],[-92.0533,32.7941],[-92.0548,32.7722],[-92.0554,32.7699],[-92.0602,32.7663],[-92.0613,32.7617],[-92.0624,32.7576],[-92.0629,32.7558],[-92.0623,32.7508],[-92.0568,32.7481],[-92.0519,32.7435],[-92.054,32.7371],[-92.0589,32.7348],[-92.0605,32.7303],[-92.0654,32.723],[-92.0724,32.7161],[-92.0756,32.7088],[-92.0822,32.7042],[-92.0902,32.6878],[-92.0989,32.6836],[-92.1092,32.6827],[-92.1201,32.6735],[-92.1277,32.6762],[-92.1391,32.6757],[-92.1418,32.6702],[-92.1385,32.6611],[-92.19,32.6294],[-92.1964,32.6252],[-92.2187,32.6114],[-92.2214,32.611],[-92.2241,32.6073],[-92.224,32.6005],[-92.2266,32.5941],[-92.2288,32.5909],[-92.2342,32.5895],[-92.2402,32.5908],[-92.2451,32.5917],[-92.2506,32.5898],[-92.2566,32.5911],[-92.2625,32.5906],[-92.2647,32.587],[-92.2712,32.5828],[-92.2766,32.5814],[-92.2793,32.5837],[-92.2815,32.586],[-92.2843,32.585],[-92.2886,32.5836],[-92.2935,32.5841],[-92.2968,32.5845],[-92.3049,32.5831],[-92.3512,32.5832],[-92.4132,32.5845],[-92.4153,32.672],[-92.4736,32.6715],[-92.5188,32.6725],[-92.5195,32.7239],[-92.5233,32.723],[-92.5271,32.7202],[-92.5342,32.7224],[-92.5374,32.7206],[-92.5418,32.7187],[-92.5472,32.7205],[-92.5517,32.7268],[-92.5572,32.7331],[-92.5672,32.7453],[-92.5722,32.7489],[-92.5787,32.748],[-92.5852,32.7488],[-92.5913,32.7528],[-92.5968,32.7551],[-92.6044,32.7555],[-92.6099,32.7549],[-92.6147,32.7526],[-92.6191,32.7548],[-92.6279,32.7575],[-92.6312,32.7593],[-92.6361,32.7597],[-92.7256,32.7597],[-92.7252,32.8039],[-92.7339,32.8033],[-92.7341,32.8179],[-92.7254,32.818],[-92.7257,32.8758],[-92.7253,32.9209],[-92.7253,32.9222],[-92.7251,32.9445],[-92.725,33.0083],[-92.7246,33.0147],[-92.6255,33.0136],[-92.3864,33.0123],[-92.1457,33.0093],[-92.1194,33.0092],[-92.0647,33.0089]]]},\"properties\":{\"name\":\"Union\",\"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
The Red Mountain porphyry copper-molybdenum deposit (Cu-Mo deposit or PCD) is located in the northern part of the Patagonia Mountains, Santa Cruz County, Arizona. Extensive core drilling has delineated a large, deep-seated, structurally intact mineral system that extends from the present surface to depths of more than 1,765 meters. This system is hosted in a thick complex of predominantly felsic to andesitic volcanic rocks of the Cretaceous Period. This complex was intruded by scattered bodies of the Tertiary Period that are predominantly quartz monzonite porphyry; no major associated source intrusion has yet been found at depth.
A total of 818 samples of core were analyzed for as many as 44 elements. The abundances and distributions at depth of at least 17 of these elements (silver [Ag], arsenic [As], gold [Au], boron [B], bismuth [Bi], copper [Cu], mercury [Hg], potassium [K], molybdenum [Mo], lead [Pb], sulfur [S], antimony [Sb], tin [Sn], tellurium [Te], thallium [Tl], tungsten [W], and zinc [Zn]) are related mostly to events that generated the Red Mountain system. Many of these same samples were also analyzed by X-ray diffraction for a suite of minerals. The multielement and mineralogical analyses of the core samples provide important information about the concentrations, associations, and distributions of select elements and minerals, including zoning patterns that may not be apparent from visual examination of core samples. The distributions of selected elements and minerals in these samples reveal an unusually complete mineral system that extends from a typical PCD with potassic alteration at depth to peripheral zones of phyllic and advanced argillic alteration as well as a copper-rich supergene enriched zone and the remnants of a leached cap.
R-mode factor analysis was run with 34 elements for a set of samples from the deep part of the hypogene Cu-Mo deposit and another set from the part of the supergene zone with the highest copper enrichment. For the hypogene zone dataset, five factors are related to the PCD: (1) Ag, Cu, Mo, S, and Te; (2) As, B, Hg, and Sb; (3) Au and sodium (Na); (4) manganese (Mn), Pb, and Zn; and (5) K and Tl. For the supergene dataset, the deposit-related factors include (1) Cu, Mo, S, and Te; (2) Ag, As, Hg, Pb, Sb, and Tl; (3) Au and Na; and (4) K and rubidium (Rb). The changes in element associations between the two datasets indicate that some of these new associations are a result of formation of several suites of hypogene minerals in the deep part of the deposit and different hypogene mineral suites in the peripheral part of the deposit. Some changes may be because of the effects of supergene processes.
Zones containing deposit-related elements and minerals common to many PCDs are present at Red Mountain. These zones include a crude, inverted cup-shaped shell containing anomalous copper accompanied by high concentrations of Ag, Au, K, Mo, total S, sulfate S, Sb, Te, and Tl, as well as local concentrations of As, B, Hg, Pb, and Zn. Hydrothermal minerals spatially associated with the deep hypogene Cu-Mo deposit include chalcopyrite, molybdenite, pyrite, plagioclase, orthoclase, biotite, magnetite, calcite, quartz, and anhydrite.
Many of the hydrothermally deposited elements that are spatially related to the deposit are also concentrated in zones above the deep part of the deposit, including Ag, As, K, Pb, Sb, Te, Tl, and Zn. These elements are concentrated either (1) in generally wide, flat zones present in the upper part of the system or (2) in crudely arcuate peripheral zones found mainly in the middle part of the system and surrounding the deep part of the deposit. Near-surface, restricted hypogene anomalies are present for bismuth, mercury, tin, and tungsten.
The upper part of the deposit has been subjected to supergene enrichment and weathering. Deposit-related elements that remain anomalous in this area include Ag, As, Au, B, Bi, cobalt (Co), Cu, Hg, Mo, Pb, S, Sb, Sn, Te, Tl, uranium (U), and W. These positive concentrations indicate that, with the exception of copper and possibly mercury and uranium, these elements had relatively low chemical mobilities in the supergene enrichment and later weathering environments at Red Mountain. Most may have been deposited during one or more hypogene events and then redistributed locally during later events. Zinc is the only deposit-related element that has clearly been depleted as a result of supergene and (or) weathering events. Minerals that are common in the unweathered upper part of the system include chalcocite, pyrite, quartz, sericite, alunite, and pyrophyllite, as well as less common covellite, enargite, tennantite, tourmaline, barite, anglesite, and other sulfide or sulfate minerals.
Subsequent to formation of the Red Mountain Cu-Mo deposit and supergene enrichment, chemical weathering produced an area of pervasive hematite and other iron oxides in the near-surface part of the deposit to form a leached cap. These iron-rich minerals formed primarily as a result of the oxidation of pyrite. This event was accompanied by losses of cobalt, mercury, magnesium, and zinc, as well as destruction of sericite, plagioclase, pyrite, clay minerals, and pyrophyllite.
A total of 122 rock samples, 119 soil samples, and samples of three plant species (57 mesquite, 108 oak, and 68 juniper) were collected over and around Red Mountain. For the rock and soil samples, the distributions of anomalous Ag, As, Bi, Cu, Fe, Mo, Pb, Sb, Te, and Tl best delineated the exposed part of the deposit. The highest concentrations of many of these elements are centered on one or both of two main areas with exposures of quartz monzonite porphyry. The high concentrations of arsenic in the deposit area (as much as 390 parts per million (ppm) in rock and 1,500 ppm in soil) and of lead (as much as 2,370 ppm in rock and 1,490 ppm in soil) are particularly noteworthy.
The concentrations of various elements in the plant ash vary widely among the three species and are species dependent. Many of the deposit-related elements are either nonessential for plant growth or are considered toxic at certain concentration ranges. In spite of this, the distributions of potentially toxic Ag, As, Bi, Cd, Cu, Mo, Pb, Sb, selenium (Se), and Zn produce deposit-related anomalies for one or more of the three species.
Vegetation sampling offered no advantage over rock or soil sampling as an exploration tool. From an environmental standpoint, however, the plant analyses provide baseline data for both essential and nonessential elements that might be useful, for example, for selecting native plant species for revegetating mine waste areas.
The exposed part of the Red Mountain deposit has not been greatly disturbed as a result of mining and other activities. However, some of the rock, soil, and plant samples that were collected near the Harshaw Creek and Alum Gulch drainages, which are peripheral to Red Mountain, are also anomalous for various deposit-related elements. These anomalies are probably the result of dispersion of stream sediments contaminated with material from past mining.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195077","usgsCitation":"Chaffee, M.A., 2020, Geochemical and mineralogical study of the Red Mountain porphyry copper-molybdenum deposit and vicinity, Santa Cruz County, Arizona: U.S. Geological Survey Scientific Investigations Report 2019–5077, 164 p., https://doi.org/10.3133/sir20195077.","productDescription":"Report: x, 164 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-085267","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":373304,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BS56JZ","text":"USGS data release","linkHelpText":"Data to accompany U.S. Geological Survey Scientific Investigations Report 2019-5077: Geochemical and mineralogical study of the Red Mountain porphyry copper-molybdenum deposit and vicinity, Santa Cruz County, Arizona"},{"id":399536,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109795.htm"},{"id":373303,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5077/sir20195077.pdf","text":"Report","size":"22.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5077"},{"id":373302,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5077/coverthb.jpg"}],"country":"United States","state":"Arizona","county":"Santa Cruz County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-111.364,31.4234],[-111.3654,31.5211],[-111.2983,31.5216],[-111.2634,31.5218],[-111.1608,31.522],[-111.1595,31.5403],[-111.1616,31.5508],[-111.1612,31.6389],[-111.1614,31.7242],[-111.0036,31.7247],[-110.9557,31.7247],[-110.8906,31.7255],[-110.8712,31.7257],[-110.8518,31.7255],[-110.8523,31.731],[-110.7941,31.7309],[-110.7042,31.7308],[-110.6902,31.7306],[-110.6838,31.7305],[-110.6692,31.7308],[-110.6644,31.7303],[-110.617,31.7306],[-110.5341,31.7309],[-110.4485,31.7307],[-110.4485,31.702],[-110.4482,31.6883],[-110.4483,31.6536],[-110.448,31.6157],[-110.4561,31.6154],[-110.4558,31.6017],[-110.4555,31.5871],[-110.4562,31.4684],[-110.4561,31.3328],[-110.4611,31.3328],[-110.4888,31.3328],[-110.5574,31.3324],[-110.6259,31.3323],[-110.6645,31.3321],[-110.7229,31.3318],[-110.7915,31.3315],[-110.8238,31.3313],[-110.8261,31.3312],[-110.8351,31.3312],[-110.8659,31.3309],[-110.8787,31.3308],[-110.9721,31.3301],[-111.0496,31.3294],[-111.0664,31.3292],[-111.0728,31.3292],[-111.1604,31.3577],[-111.1676,31.3601],[-111.1705,31.361],[-111.1725,31.3617],[-111.1746,31.3624],[-111.2218,31.3778],[-111.2843,31.3978],[-111.364,31.4234]]]},\"properties\":{\"name\":\"Santa Cruz\",\"state\":\"AZ\"}}]}","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046
Due to extensive land conversion over the last century, much of the native prairie pothole ecosystem has been converted to agricultural or other human uses. The prairie pothole ecosystem is found in the northern plains of Iowa, Minnesota, South Dakota, North Dakota, and Montana. Because most of the land in this region is privately owned and used for agricultural production, most impacts to wildlife habitat are the result of decisions by individual landowners. Landowner trust in natural resource management agencies is important for agencies to effectively accomplish their mission. We measured the nature (competence and fairness) and level of trust that western Minnesota landowners have in the Minnesota Department of Natural Resources (MnDNR) and landowners’ wildlife value orientations (WVO). Landowners rated MnDNR slightly higher in competence than fairness; however, these two dimensions were strongly correlated. We developed a MnDNR trust scale (six items) and a three-cluster model dividing landowners along the MnDNR trust scale, which we named Negative (28%), Neutral (43%), and Positive (29%). We provide evidence supporting the salient values similarity (SVS) model that states people have trust in agencies holding similar values; landowners reporting greater importance for wildlife consideration when making land-use decisions also reported greater trust in the MnDNR. In addition, mutualist landowners had the highest trust in the MnDNR and utilitarian landowners the lowest level of trust, which is opposite of the trust relationship reported for the general public with state wildlife agencies. Based on the SVS model, our results suggest that mutualist landowners perceive greater congruence with MnDNR goals related to wildlife habitat compared to utilitarian landowners.
","language":"English","publisher":"Springer","doi":"10.1007/s10669-020-09766-z","usgsCitation":"Gigliotti, L.M., Sweikert, L., Cornicelli, L., and Fulton, D.C., 2020, Minnesota landowners’ trust in their department of natural resources, salient values similarity and wildlife value orientations: Environment Systems and Decisions, v. 40, p. 577-587, https://doi.org/10.1007/s10669-020-09766-z.","productDescription":"11 p.","startPage":"577","endPage":"587","ipdsId":"IP-103189","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":388554,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"40","noUsgsAuthors":false,"publicationDate":"2020-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Gigliotti, Larry M. 0000-0002-1693-5113 lgigliotti@usgs.gov","orcid":"https://orcid.org/0000-0002-1693-5113","contributorId":3906,"corporation":false,"usgs":true,"family":"Gigliotti","given":"Larry","email":"lgigliotti@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":822031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sweikert, Lily A.","contributorId":264825,"corporation":false,"usgs":false,"family":"Sweikert","given":"Lily A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":822032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornicelli, Louis","contributorId":264826,"corporation":false,"usgs":false,"family":"Cornicelli","given":"Louis","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":822033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fulton, David C. 0000-0001-5763-7887 dcf@usgs.gov","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":2208,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"dcf@usgs.gov","middleInitial":"C.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":822034,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210858,"text":"70210858 - 2020 - Deglacial temperature controls on no-analog community establishment in the Great Lakes Region","interactions":[],"lastModifiedDate":"2020-06-30T13:37:00.272631","indexId":"70210858","displayToPublicDate":"2020-03-18T08:30:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Deglacial temperature controls on no-analog community establishment in the Great Lakes Region","docAbstract":"Understanding the drivers of vegetation dynamics and no-analog communities in eastern North America is hampered by a scarcity of independent temperature indicators. We present a new branched glycerol dialkyl glycerol tetraether (brGDGT) temperature record from Bonnet Lake, Ohio (18 to 8 ka) and report uncertainty estimates based on Bayesian linear regression and bootstrapping. We also reanalyze a previously published brGDGT record from Silver Lake, Ohio, using improved chromatographic methods. All pollen- and brGDGT-based temperature reconstructions showed qualitatively similar deglacial trends but varying magnitudes. Separating 5- and 6- methyl brGDGTs resulted in substantially lower estimates of deglacial temperature variations (6.4 °C) than inferred from earlier brGDGT methods and pollen (11.8 °C, 12.0 °C respectively). Similar trends among proxies suggest good fidelity of brGDGTs to temperature, despite calibration uncertainties. At both sites, the rise and decline of no-analog communities closely track brGDGT-inferred temperatures, with a lag of 0 to 150 years. The timing of temperature and ecological events varies between Bonnet and Silver Lakes, likely due to age model uncertainties. Climate sensitivity analyses indicate a linear sensitivity of vegetation composition to temperature variations, albeit noisy and significant only with a 500-year bin. The formation of no-analog plant communities in the upper Midwest is closely linked to late-glacial warming, but other factors, such as temperature seasonality or end-Pleistocene megafaunal extinctions, remain viable.","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2020.106245","usgsCitation":"Fastovich, D., Russell, J.M., Jackson, S., and Williams, J.W., 2020, Deglacial temperature controls on no-analog community establishment in the Great Lakes Region: Quaternary Science Reviews, v. 234, 106245, 16 p., https://doi.org/10.1016/j.quascirev.2020.106245.","productDescription":"106245, 16 p.","ipdsId":"IP-107598","costCenters":[{"id":41166,"text":"Southwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":457336,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2020.106245","text":"Publisher Index Page"},{"id":376014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Lakes Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.869140625,\n 37.71859032558816\n ],\n [\n -79.7607421875,\n 37.71859032558816\n ],\n [\n -79.7607421875,\n 41.902277040963696\n ],\n [\n -85.869140625,\n 41.902277040963696\n ],\n [\n -85.869140625,\n 37.71859032558816\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"234","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fastovich, David","contributorId":225614,"corporation":false,"usgs":false,"family":"Fastovich","given":"David","email":"","affiliations":[],"preferred":false,"id":791886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, James M.","contributorId":174740,"corporation":false,"usgs":false,"family":"Russell","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":27506,"text":"Department of Earth, Environmental and Planetary Sciences, Brown University, Providence RI 02912 USA","active":true,"usgs":false}],"preferred":false,"id":791887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, Stephen 0000-0002-1487-4652","orcid":"https://orcid.org/0000-0002-1487-4652","contributorId":219995,"corporation":false,"usgs":true,"family":"Jackson","given":"Stephen","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":791749,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, John W.","contributorId":16761,"corporation":false,"usgs":true,"family":"Williams","given":"John","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":791888,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216012,"text":"70216012 - 2020 - Relocated aftershocks and background seismicity in eastern Indonesia shed light on the 2018 Lombok and Palu earthquake sequences","interactions":[],"lastModifiedDate":"2020-11-03T13:29:28.447413","indexId":"70216012","displayToPublicDate":"2020-03-18T07:22:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Relocated aftershocks and background seismicity in eastern Indonesia shed light on the 2018 Lombok and Palu earthquake sequences","docAbstract":"High seismicity rates in eastern Indonesia occur due to the complex interaction of several tectonic plates which resulted in two deadly, destructive earthquake sequences that occurred in Lombok Island and the city of Palu, Sulawesi in 2018. The first sequence began in July with an Mw 6.4 event near Lombok, culminating in an Mw 7.0 event 8 d later. This was then followed by a nearby Mw 6.9 event 12 d later. Approximately 1000 km to the northeast, a separate sequence began several weeks later near Palu where an Mw 7.5 event occurred that triggered a tsunami. In this study, we present hypocentre relocations for both earthquake sequences as well as all other regional earthquakes in eastern Indonesia. The relocations were performed using a teleseismic double-difference relocation method and arrival times for P and S waves from stations at local, regional, and teleseismic distances. The catalogue and phase data were taken from the Agency for Meteorology, Climatology and Geophysics (BMKG) of Indonesia and the International Seismological Centre (ISC) for the period of April 2009 through November 2018. The relocated catalogue provides an improved view of seismicity in eastern Indonesia over the study period, sharpening locations and interpretations of seismogenic features throughout the region. In the Lombok area, the relocated earthquakes clearly show a backarc thrust to the north of the Sunda-Banda Arc transition zone. The relocated aftershocks show that the destructive Mw 7.0 and Mw 6.9 earthquakes of the Lombok sequence ruptured two different regions: The Mw 7.0 earthquake propagated westward, whereas the Mw 6.9 earthquake propagated eastward. The entire sequence of Lombok earthquakes was most likely started by the Mw 6.4 event as the initial event or foreshock, which then triggered backarc thrusts on both sides. Several weeks later and far to the northeast, the Mw 7.5 Palu earthquake occurred along the Palu-Koro Fault, filling a seismic gap that had not ruptured in an Mw 6.0 event or larger since at least 1900. The distribution of aftershocks indicates that the northern part of the Palu-Koro Fault has lower relative seismicity rates than the southern part at shallow depths, and that off fault aftershocks are mostly located to the east of the Palu-Koro Fault.
","language":"English","publisher":"Royal Astronomical Society","doi":"10.1093/gji/ggaa118","usgsCitation":"Supendi, P., Nugraha, A.D., Widiyantoro, S., Pesicek, J.D., Thurber, C., Abdullah, C., Daryono, D., Wiyono, S., Shiddiqi, H., and Rosalia, S., 2020, Relocated aftershocks and background seismicity in eastern Indonesia shed light on the 2018 Lombok and Palu earthquake sequences: Geophysical Journal International, v. 221, no. 3, p. 1845-1855, https://doi.org/10.1093/gji/ggaa118.","productDescription":"11 p.","startPage":"1845","endPage":"1855","ipdsId":"IP-114885","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":457340,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggaa118","text":"Publisher Index Page"},{"id":380066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Lombok, Palu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 119.14672851562499,\n -1.4720060101903352\n ],\n [\n 120.574951171875,\n -1.4720060101903352\n ],\n [\n 120.574951171875,\n -0.15380840901698828\n ],\n [\n 119.14672851562499,\n -0.15380840901698828\n ],\n [\n 119.14672851562499,\n -1.4720060101903352\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 115.7904052734375,\n -9.091248585779377\n ],\n [\n 116.77917480468749,\n -9.091248585779377\n ],\n [\n 116.77917480468749,\n -8.07554603328031\n ],\n [\n 115.7904052734375,\n -8.07554603328031\n ],\n [\n 115.7904052734375,\n -9.091248585779377\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"221","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Supendi, Pepen","contributorId":244317,"corporation":false,"usgs":false,"family":"Supendi","given":"Pepen","email":"","affiliations":[{"id":48884,"text":"Bandung Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":803750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nugraha, Andri Dian","contributorId":202043,"corporation":false,"usgs":false,"family":"Nugraha","given":"Andri","email":"","middleInitial":"Dian","affiliations":[{"id":36333,"text":"Institut Teknologi Bandung","active":true,"usgs":false}],"preferred":false,"id":803751,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Widiyantoro, Sri","contributorId":202045,"corporation":false,"usgs":false,"family":"Widiyantoro","given":"Sri","email":"","affiliations":[{"id":36333,"text":"Institut Teknologi Bandung","active":true,"usgs":false}],"preferred":false,"id":803752,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pesicek, Jeremy D. 0000-0001-7964-5845","orcid":"https://orcid.org/0000-0001-7964-5845","contributorId":202042,"corporation":false,"usgs":true,"family":"Pesicek","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":803753,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thurber, C.H.","contributorId":244318,"corporation":false,"usgs":false,"family":"Thurber","given":"C.H.","affiliations":[{"id":13451,"text":"Univ. of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":803754,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Abdullah, C.I.","contributorId":244319,"corporation":false,"usgs":false,"family":"Abdullah","given":"C.I.","email":"","affiliations":[{"id":48884,"text":"Bandung Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":803755,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Daryono, D.","contributorId":244320,"corporation":false,"usgs":false,"family":"Daryono","given":"D.","email":"","affiliations":[{"id":48887,"text":"BMKG, Indonesia","active":true,"usgs":false}],"preferred":false,"id":803756,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wiyono, S.H.","contributorId":244321,"corporation":false,"usgs":false,"family":"Wiyono","given":"S.H.","affiliations":[{"id":48887,"text":"BMKG, Indonesia","active":true,"usgs":false}],"preferred":false,"id":803757,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Shiddiqi, H.A.","contributorId":244322,"corporation":false,"usgs":false,"family":"Shiddiqi","given":"H.A.","affiliations":[{"id":48888,"text":"Univ. of Bergen","active":true,"usgs":false}],"preferred":false,"id":803758,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rosalia, S.","contributorId":244323,"corporation":false,"usgs":false,"family":"Rosalia","given":"S.","email":"","affiliations":[{"id":48884,"text":"Bandung Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":803759,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70259110,"text":"70259110 - 2020 - Linking landscape-scale conservation to regional and continental outcomes for a migratory species","interactions":[],"lastModifiedDate":"2024-10-03T16:17:29.344521","indexId":"70259110","displayToPublicDate":"2020-03-18T07:03:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Linking landscape-scale conservation to regional and continental outcomes for a migratory species","docAbstract":"Land-use intensification on arable land is expanding and posing a threat to biodiversity and ecosystem services worldwide. We develop methods to link funding for avian breeding habitat conservation and management at landscape scales to equilibrium abundance of a migratory species at the continental scale. We apply this novel approach to a harvested bird valued by birders and hunters in North America, the northern pintail duck (Anas acuta), a species well below its population goal. Based on empirical observations from 2007–2016, habitat conservation investments for waterfowl cost $313 M and affected less than 2% of the pintail’s primary breeding area in the Prairie Pothole Region of Canada. Realistic scenarios for harvest and habitat conservation costing an estimated $588 M (2016 USD) led to predicted pintail population sizes less than 3 M when assuming average parameter values. Accounting for parameter uncertainty, converting 70–100% of these croplands to idle grassland (cost: $35.7B–50B) is required to achieve the continental population goal of 4 M individuals under the current harvest policy. Using our work as a starting point, we propose continued development of modeling approaches that link conservation funding, habitat delivery, and population response to better integrate conservation efforts and harvest management of economically important migratory species.
Many models in population ecology, including spatial capture–recapture (SCR) models, assume that individuals are distributed and detected independently of one another. In reality, this is rarely the case – both antagonistic and gregarious relationships lead to non-independent spatial configurations, with territorial exclusion at one end of the spectrum and group-living at the other. Previous simulation studies suggest that grouping has limited impact on the outcome of SCR analyses. However, group associations entail not only spatial clustering of activity centers but also coordinated space use by group members, potentially impacting both ecological and observation processes underlying SCR analysis. We simulated SCR scenarios with different strengths of aggregation (clustering of individuals into groups with shared activity centers) and cohesion (synchronization of detection patterns of members of a group). We then fit SCR models to the simulated data sets and evaluated the effect of aggregation and cohesion on parameter estimates. Low to moderate aggregation and cohesion did not impact the bias and precision of estimates of density and the scale parameter of the detection function. However, non-independence between individuals led to high levels of overdispersion. Overdispersion strongly decreased the coverage of confidence intervals around parameter estimates, thereby increasing the probability of erroneous predictions. Our results indicate that SCR models are robust to moderate levels of aggregation and cohesion. Nonetheless, spatial dependence between individuals can lead to false inference. We recommend that practitioners 1) test for the presence of overdispersion in SCR data caused by aggregation and cohesion, and, if necessary, 2) correct their variance estimates using the overdispersion factor ĉ . Approaches for doing both are described in this paper. We also urge the development of SCR models that incorporate spatial associations between individuals not only to account for overdispersion but also to obtain quantitative information about social aspects of study populations.
","language":"English","publisher":"BioOne","doi":"10.2981/wlb.00649","usgsCitation":"Bischof, R., Dupont, P., Milleret, C., Chipperfield, J., and Royle, J.A., 2020, Consequences of ignoring group association in spatial capture-recapture analysis: Wildlife Biology, v. 2020, no. 1, wlb.00649, 11 p., https://doi.org/10.2981/wlb.00649.","productDescription":"wlb.00649, 11 p.","ipdsId":"IP-113777","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457343,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2981/wlb.00649","text":"Publisher Index Page"},{"id":377200,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2020","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bischof, Richard","contributorId":237793,"corporation":false,"usgs":false,"family":"Bischof","given":"Richard","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":795324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dupont, Pierre","contributorId":237794,"corporation":false,"usgs":false,"family":"Dupont","given":"Pierre","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":795325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milleret, Cyril","contributorId":237795,"corporation":false,"usgs":false,"family":"Milleret","given":"Cyril","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":795326,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chipperfield, Joseph","contributorId":237796,"corporation":false,"usgs":false,"family":"Chipperfield","given":"Joseph","email":"","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":795327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795328,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209364,"text":"70209364 - 2020 - Earthquakes, ShakeCast","interactions":[],"lastModifiedDate":"2020-04-03T14:37:10.136529","indexId":"70209364","displayToPublicDate":"2020-03-17T09:32:45","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Earthquakes, ShakeCast","docAbstract":"ShakeCast® – short for ShakeMap Broadcast – is a fully automated software system for delivering specific ShakeMap products to critical users and for triggering established post-earthquake response protocols. ShakeCast is a freely available, postearthquake situational awareness software application that automatically retrieves earthquake shaking data from ShakeMap to compare ground shaking intensity measures against users’ facilities (Lin and Wald 2008). 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IWAA is intended to explore, test, and refine systems and processes for assessing water availability for human and ecological uses and understanding their underlying controls. Water quality plays an important role in supporting ecological health and determining the suitability of water for human consumption, recreation, agriculture, and industry. Understanding how water quality has changed over time in response to natural and human-induced changes in landscape and climate identifies potential challenges in safeguarding water for all uses. The USGS has evaluated water-quality trends across the Nation, and 22 of the evaluated sites are in the Delaware River Basin. These 22 sites are in the Appalachian Plateau, Valley and Ridge, Piedmont, and Coastal Plain Physiographic Provinces. Data from these sites indicate decadal to multidecadal changes in water quality and provide an initial look at how nutrient concentrations, such as total phosphorous, total nitrogen, and nitrate, and salinity indicators, such as specific conductance, sulfate, and chloride, have varied over time in the basin. The time period of the evaluation ranged from 1972 to 2012.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203007","collaboration":"Integrated Water Availability Assessments Program","usgsCitation":"Murphy, J.C., and Shoda, M.E., 2020, A historical look at changing water quality in the Delaware River basin: U.S. Geological Survey Fact Sheet 2020–3007, 2 p., https://doi.org/10.3133/fs20203007.","productDescription":"Report: 2 p.; Data Release","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-113624","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":373184,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TQ5ZS3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality trends and trend component estimates for the Nation's rivers and streams using Weighted Regressions on Time, Discharge, and Season (WRTDS) models and generalized flow normalization, 1972–2012"},{"id":373182,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3007/coverthb2.jpg"},{"id":373183,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3007/fs20203007.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3007"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.5,\n 38.625\n ],\n [\n -74.5,\n 38.625\n ],\n [\n -74.5,\n 43\n ],\n [\n -76.5,\n 43\n ],\n [\n -76.5,\n 38.625\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Program Coordinator, Water Availability and Use Science Program
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Email: wausp-info@usgs.gov
","tableOfContents":"The evolution of male signals and female preferences remains a central question in the study of animal communication. The sensory trap model suggests males evolve signals that mimic cues used in nonsexual contexts and thus manipulate female behavior to generate mating opportunities. Much evidence supports the sensory trap model, but how females glean reliable information from both mimetic signals and their model cues remains unknown. We discovered a mechanism whereby a manipulative male signal guides reliable communication in sea lamprey (Petromyzon marinus). Migratory sea lamprey follow a larval cue into spawning streams; once sexually mature, males release a pheromone that mimics the larval cue and attracts females. Females conceivably benefit from the mimetic pheromone during mate search but must discriminate against the model cue to avoid orienting toward larvae in nearby nursery habitats. We tested the hypothesis that spawning females respond to petromyzonol sulfate (PZS) as a behavioral antagonist to avoid attraction to the larval cue while tracking the male pheromone despite each containing attractive 3-keto petromyzonol sulfate (3kPZS). We found 1) PZS inhibited electrophysiological responses to 3kPZS and abated preferences for 3kPZS when mixed at the same or greater concentrations, 2) larvae released more PZS than 3kPZS whereas males released more 3kPZS than PZS, and 3) mixtures of 3kPZS and PZS applied at ratios measured in larval and male odorants resulted in the discrimination observed between the natural odors. Our study elucidates how communication systems that arise via deception can facilitate reliable communication.
Landslides in Puerto Rico range from nuisances to deadly events. Centuries of agricultural and urban modification of the landscape have perturbed many already unstable hillsides on the tropical island. One of the main triggers of mass wasting on the island is the high-intensity rainfall that is associated with tropical atmospheric systems. Puerto Rico’s geographic position and rugged topography render millions of residents vulnerable to widespread landslide events. In this study, a high-resolution (5 meters), high-intensity rainfall-induced landslide susceptibility model was produced using the frequency-ratio method. Datasets utilized in the model included a complete-island landslide inventory created from imagery obtained after Hurricanes Irma and María impacted the island during September 2017, slope inclination, land-surface curvature, soil type, geologic terrane, mean annual precipitation, land use, soil moisture, and distance to roadways and streams. The final data product (plate 1) is a statistically viable representation of where landslides are likely to initiate during or soon after intense rainfall, with a robust receiver operating characteristic area-under-curve value of 0.87. The model output raster pixel values were binned into 100 equal-area quantiles and then classified into Low, Moderate, High, Very High, and Extremely High classes of susceptibility. The Extremely High susceptibility classification represents the most vulnerable 1 percent of the island, whereas Very High, High, Moderate, and Low classifications cover 9, 20, 30, and 40 percent of the island, respectively. The susceptibility map is intended to assist in planning future development, mitigation measures, and post-event emergency response; however, it is not a substitute for site-specific, slope-stability assessments performed by licensed geologists and engineers. Additionally, the map does not portray locations where landslide material may travel after mobilization, and which may be at extreme risk; nor does it necessarily portray where landslides may occur during earthquakes or mass wasting triggered by prolonged, relatively low-intensity rainfall.
","language":"English, Spanish","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20201022","collaboration":"Prepared in cooperation with the University of Puerto Rico at Mayagüez","usgsCitation":"Hughes, K.S., and Schulz, W.H., 2020, Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico: U.S. Geological Survey Open-File Report 2020–1022, 91 p., 1 plate, scale 1:150,000, https://doi.org/10.3133/ofr20201022.","productDescription":"Report: viii, 91 pages; 2 Sheets: 49.11 x 33.86 inches; Application Sites; Data Release; Read Me","onlineOnly":"Y","ipdsId":"IP-116377","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":373245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_pamphlet.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1022 pamphlet","linkHelpText":"English 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Box 25046, MS-966
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Life history theory examines how characteristics of organisms, such as age and size at maturity, may vary through natural selection as evolutionary responses that optimize fitness. Here we ask how predictions of age and size at maturity differ for the three classical fitness functions–intrinsic rate of natural increase r, net reproductive rate R0, and reproductive value Vx−for semelparous species. We show that different choices of fitness functions can lead to very different predictions of species behavior. In one’s efforts to understand an organism’s behavior and to develop effective conservation and management policies, the choice of fitness function matters. The central ingredient of our approach is the maturation reaction norm (MRN), which describes how optimal age and size at maturation vary with growth rate or mortality rate. We develop a practical geometric construction of MRNs that allows us to include different growth functions (linear growth and nonlinear von Bertalanffy growth in length) and develop two-dimensional MRNs useful for quantifying growth-mortality trade-offs. We relate our approach to Beverton-Holt life history invariants and to the Stearns-Koella categorization of MRNs. We conclude with a detailed discussion of life history parameters for Great Lakes Chinook Salmon and demonstrate that age and size at maturity are consistent with predictions using R0 (but not r or Vx) as the underlying fitness function.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0228990","usgsCitation":"Breck, J.E., Simon, C.P., Rutherford, E.S., Low, B.S., Lamberson, P.J., and Rogers, M.W., 2020, The geometry of reaction norms yields insights on classical fitness functions for Great Lakes salmon: PLoS ONE, v. 15, no. 3, p. 1-35, https://doi.org/10.1371/journal.pone.0228990.","productDescription":"e0228990, 35 p.","startPage":"1","endPage":"35","ipdsId":"IP-111222","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457359,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0228990","text":"Publisher Index Page"},{"id":395682,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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A.","contributorId":171740,"corporation":false,"usgs":false,"family":"Hovel","given":"Rachel","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":834064,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Breck, James E.","contributorId":171518,"corporation":false,"usgs":false,"family":"Breck","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":834011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simon, Carl P.","contributorId":275339,"corporation":false,"usgs":false,"family":"Simon","given":"Carl","email":"","middleInitial":"P.","affiliations":[{"id":56762,"text":"The University of Michigan","active":true,"usgs":false}],"preferred":false,"id":834012,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rutherford, Edward S.","contributorId":54161,"corporation":false,"usgs":true,"family":"Rutherford","given":"Edward","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":834013,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Low, Bobbi S.","contributorId":189289,"corporation":false,"usgs":false,"family":"Low","given":"Bobbi","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":834014,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lamberson, P. J.","contributorId":275342,"corporation":false,"usgs":false,"family":"Lamberson","given":"P.","email":"","middleInitial":"J.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":834015,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rogers, Mark W. 0000-0001-7205-5623","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":245525,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834016,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212618,"text":"70212618 - 2020 - Pavement alters delivery of sediment and fallout radionuclides to urbanstreams","interactions":[],"lastModifiedDate":"2020-08-24T15:59:04.913792","indexId":"70212618","displayToPublicDate":"2020-03-16T09:48:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Pavement alters delivery of sediment and fallout radionuclides to urbanstreams","docAbstract":"Sediment from urban impervious surfaces has the potential to be an important vector for contaminants, particularly where stormwater culverts and other buried channels draining large impervious areas exit from underground pipes into open channels. To better understand urban sediment sources and their relation to fallout radionuclides, we collected samples of rainfall, urban sediment (pavement sediment, topsoil), streambank sediment, and fluvial sediment (suspended sediment and bed sediment) for 7Be, 210Pbex, and 137Cs analysis. The results indicate that each rainfall event tags pavement sediment with elevated activities of 7Be and 210Pbex such that runoff from impervious surfaces in the buried channel part of the stream network contains the highest activities. Pavement sediment, because it is characteristically a thin veneer, has a small mass to rainwater ratio resulting in a greater tagging of 7Be and 210Pbex activity than does topsoil on a per gram basis. An unmixing model indicated that suspended-sediment samples collected at the culvert outlet from the buried-channel network are from pavement sediment sources (45 ± 25%) with a smaller component of topsoil (22 ± 19%), and a component from streambanks (32 ± 35%) that we infer to be older channel material and subsoil eroded from within the culvert system. Downstream from the culvert, suspended sediment collected from the open-channel parts of the stream had 7Be and 210Pbex activities that were substantially reduced by the contribution of sediment from streambanks (57 ± 15%), with pavement contributions decreasing to 15 (±9%) and topsoil contributing 28 (±7%). The results highlight the utility of 7Be, 210Pbex, and 137Cs as tracers of urban sediment sources, resulting in a unique radionuclide signature for urban watersheds compared to other sediment-source settings.
Mercury (Hg) is a global pollutant that affects biota in remote settings due to atmospheric deposition of inorganic Hg, and its conversion to methylmercury (MeHg), the bioaccumulating and toxic form. Characterizing biotic MeHg is important for evaluating aquatic ecosystem responses to changes in Hg inputs. Aquatic insects possess many qualities desired for MeHg biomonitoring, but are not widely used, largely because of limited information regarding percentages of total mercury (THg) composed of MeHg (i.e., MeHg%) in various taxa. Here, we examine taxonomic, spatial, and seasonal variation in MeHg% of stream-dwelling predator and primary-consumer insects from nine streams in the Adirondack region (NY, USA). Predator MeHg% was high (median 94%) and did not differ significantly among five taxa. MeHg% in selected dragonflies (the most abundant predators, Odonata: Aeshnidae and Libellulidae) exhibited little seasonal and spatial variation, and THg concentration was strongly correlated with aqueous (filtered) MeHg (FMeHg; rs = 0.76). In contrast, MeHg% in primary consumers—shredders (northern caddisflies [Trichoptera: Limnephilidae]) and scrapers (flathead mayflies [Ephemeroptera: Heptageniidae]), were lower (medians 52% and 35%, respectively), and differed significantly between taxa, among sites, and seasonally. Correlations of THg with FMeHg were weak (shredders, rs = 0.45, p = 0.09) or not significant (scrapers, p = 0.89). The higher MeHg% of predators corresponded with their higher trophic positions (indicated by nitrogen stable isotopes). Results suggest obligate predators hold the most promise for the use of THg as a surrogate for MeHg biomonitoring with aquatic insects within the Adirondack region.
","language":"English","publisher":"Springer","doi":"10.1007/s10646-020-02191-7","usgsCitation":"Riva-Murray, K., Bradley, P., and Brigham, M.E., 2020, Methylmercury-Total mercury ratios in predator and primary consumer insects from Adirondack streams (New York, USA): Ecotoxicology, v. 29, https://doi.org/10.1007/s10646-020-02191-7.","productDescription":"15 p.","startPage":"1658","ipdsId":"IP-086907","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":373331,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Adirondack Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.35498046875,\n 42.827638636242284\n ],\n [\n -73.2568359375,\n 42.827638636242284\n ],\n [\n -73.2568359375,\n 45.24395342262324\n ],\n [\n -76.35498046875,\n 45.24395342262324\n ],\n [\n -76.35498046875,\n 42.827638636242284\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","edition":"1644","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Riva-Murray, Karen 0000-0001-6683-2238 krmurray@usgs.gov","orcid":"https://orcid.org/0000-0001-6683-2238","contributorId":168876,"corporation":false,"usgs":true,"family":"Riva-Murray","given":"Karen","email":"krmurray@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brigham, Mark E. 0000-0001-7412-6800 mbrigham@usgs.gov","orcid":"https://orcid.org/0000-0001-7412-6800","contributorId":1840,"corporation":false,"usgs":true,"family":"Brigham","given":"Mark","email":"mbrigham@usgs.gov","middleInitial":"E.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785017,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228662,"text":"70228662 - 2020 - The effects of swimming exercise and dissolved oxygen on growth performance, fin condition and survival of rainbow trout Oncorhynchus mykiss","interactions":[],"lastModifiedDate":"2022-03-11T16:34:08.740184","indexId":"70228662","displayToPublicDate":"2020-03-15T11:24:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":857,"text":"Aquaculture Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The effects of swimming exercise and dissolved oxygen on growth performance, fin condition and survival of rainbow trout Oncorhynchus mykiss","title":"The effects of swimming exercise and dissolved oxygen on growth performance, fin condition and survival of rainbow trout Oncorhynchus mykiss","docAbstract":"Swimming exercise and dissolved oxygen (DO) are important parameters to consider when operating intensive salmonid aquaculture facilities. While previous research has focused on each of these two variables in rainbow trout Oncorhynchus mykiss, studies examining both variables in combination, and their potential interaction, are absent from the scientific literature. Both swimming exercise (usually measured in body lengths per second, or BL/s) and DO can be readily controlled in modern aquaculture systems; therefore, we sought to evaluate the effects of these variables, separately and combined, on several outcomes in rainbow trout including growth performance, fin health and survival. Rainbow trout fry (18 g) were stocked into 12 circular 0.5 m3 tanks, provided with either high (1.5–2 BL/s) or low (approximately 0.5 BL/s) swimming exercise and high (100% saturation) or low (70% saturation) DO, and grown to approximately 1 kg. By the conclusion of the study, higher DO was independently associated with significantly (p < .05) increased growth performance. Significant differences were not noted in other outcomes, namely feed conversion, condition factor and mortality, although caudal and right pectoral fin damage was associated with low oxygen and low swimming exercise treatments respectively. Cardiosomatic index was significantly higher among exercised fish. These results suggest that swimming exercise and DO at saturation during the culture of rainbow trout can be beneficial to producers through improved growth performance and cardiac health.
","language":"English","publisher":"Wiley","doi":"10.1111/are.14600","usgsCitation":"Waldrop, T., Summerfelt, S., Mazik, P.M., Kenney, P.B., and Good, C., 2020, The effects of swimming exercise and dissolved oxygen on growth performance, fin condition and survival of rainbow trout Oncorhynchus mykiss: Aquaculture Research, v. 51, no. 6, p. 2582-2589, https://doi.org/10.1111/are.14600.","productDescription":"8 p.","startPage":"2582","endPage":"2589","ipdsId":"IP-113465","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":457365,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/are.14600","text":"Publisher Index Page"},{"id":397025,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Waldrop, Thomas","contributorId":279449,"corporation":false,"usgs":false,"family":"Waldrop","given":"Thomas","affiliations":[{"id":33606,"text":"The Conservation Fund Freshwater Institute","active":true,"usgs":false}],"preferred":false,"id":834953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Summerfelt, Steven","contributorId":279450,"corporation":false,"usgs":false,"family":"Summerfelt","given":"Steven","affiliations":[{"id":33606,"text":"The Conservation Fund Freshwater Institute","active":true,"usgs":false}],"preferred":false,"id":834954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kenney, P. Brett","contributorId":279452,"corporation":false,"usgs":false,"family":"Kenney","given":"P.","email":"","middleInitial":"Brett","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":834955,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Good, Christopher","contributorId":279454,"corporation":false,"usgs":false,"family":"Good","given":"Christopher","affiliations":[{"id":33606,"text":"The Conservation Fund Freshwater Institute","active":true,"usgs":false}],"preferred":false,"id":834956,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210136,"text":"70210136 - 2020 - North Carolina State climate report","interactions":[],"lastModifiedDate":"2020-05-15T14:30:20.518142","indexId":"70210136","displayToPublicDate":"2020-03-15T09:27:17","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"North Carolina State climate report","docAbstract":"Our scientific understanding of the climate system strongly supports the conclusion that North Carolina’s climate has changed in recent decades and the expectation that large changes—much larger than at any time in the state’s history—will occur if current trends in greenhouse gas concentrations continue. Even under a scenario where emissions peak around 2050 and decline thereafter, North Carolina will experience substantial changes in climate. The projected changes with the highest level of scientific confidence include increases in temperature, increases in summer absolute humidity, increases in sea level, and increases in extreme precipitation. It is also likely that there will be increases in the intensity of the strongest hurricanes. \nA full appreciation for past and future changes in North Carolina’s climate requires a global perspective. Earth’s climate has warmed substantially since the late 19th century, with most of that warming occurring in the last 50 years. This warming trend is clear from global temperature records and many other indicators, including rising global sea levels and rapid decreases in arctic sea ice cover. Scientists have very high confidence that this warming is largely due to human activities that have significantly increased atmospheric concentrations of carbon dioxide (CO2) and other greenhouse gases. Exhaustive research has examined other potential causes of this warming, and the increase in greenhouse gas concentrations is the only plausible cause that is consistent with the observed data and the physics that governs the climate system.","language":"English","publisher":"NCICS","collaboration":"North Carolina State University, NC Department of Environmental Quality","usgsCitation":"Kunkel, K.E., Easterling, D.R., Ballinger, A., Bililign, S., Champion, S., Corbett, D.R., Dello, K., Dissen, J., Kossin, J.P., Lackmann, G., Luettich, R., Perry, B., Robinson, W., Stevens, L.E., Stewart, B.C., and Terando, A., 2020, North Carolina State climate report, 236 p,.","productDescription":"236 p,","ipdsId":"IP-115496","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":374872,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":374858,"type":{"id":15,"text":"Index 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wildfires","interactions":[],"lastModifiedDate":"2022-02-14T15:33:01.949286","indexId":"70228573","displayToPublicDate":"2020-03-15T09:19:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires","title":"Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires","docAbstract":"The combined effects of long-term fire suppression, logging, and overgrazing have negatively impacted many southwestern U.S. forests, resulting in decreased habitat quality for wildlife, and more frequent and severe wildfires. In response, land management agencies are implementing large-scale forest restoration treatments, but data on how wildlife respond to restoration treatments and wildfires are often limited. We investigated bed and den site selection of American black bears (Ursus americanus) using GPS location data and a use/available study design to assess the influence of habitat characteristics, including wildfires, prescribed burns, and thinning treatments on bed and den site selection in the Jemez Mountains, New Mexico. The most supported models suggested that black bears were more likely to select bed sites with a combination of low horizontal visibility (β = −0.007, SE = 0.002; P = 0.002) and high stand basal area (β = 0.013, SE = 0.005; P = 0.004). The highest-ranking model for den site selection indicated that black bears were more likely to select den sites with low horizontal visibility (β = −0.0102, SE = 0.004; P = 0.006). Black bears used all disturbed sites to varying degrees (45% of study area), although 48% of bed sites were located in undisturbed habitat (55% of study area) while only 11% and 2% of bed sites were located in thinned and prescribed burn sites, respectively. Thirty-nine percent of bed sites were located in previous wildfire locations; however, 67% of these sites were in areas with low burn severity. Thirty-eight percent of den sites were located in previously disturbed habitat, 8 of these sites were burned by wildfires. In order to develop effective management plans for black bears, it is essential to understand responses to landscape-scale habitat disturbances due to wildfires and restoration activities, all of which are becoming more prevalent and widespread across southwestern forests. Accounting for the timing, size, and proximity of future restoration efforts would aid in mitigating potential short-term negative effects on black bears.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2020.117904","usgsCitation":"Bard, S.M., and Cain, J.W., 2020, Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires: Forest Ecology and Management, v. 460, p. 1-11, https://doi.org/10.1016/j.foreco.2020.117904.","productDescription":"117904, 11 p.","startPage":"1","endPage":"11","ipdsId":"IP-112372","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":457367,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1595500","text":"Publisher Index Page"},{"id":395886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Collaborative Forest Landscape Restoration Program area, Jemez Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.67587280273438,\n 35.65004306288284\n ],\n [\n -106.67587280273438,\n 35.622698214535184\n ],\n [\n -106.62506103515625,\n 35.623256366178964\n ],\n [\n -106.42936706542969,\n 35.85455268869835\n ],\n [\n -106.39503479003906,\n 35.85343961959182\n ],\n [\n -106.39022827148438,\n 36.00800626603582\n ],\n [\n -106.62368774414062,\n 36.00911716117325\n ],\n [\n -106.68960571289062,\n 35.884043325566886\n ],\n [\n -106.86882019042969,\n 35.879592612012026\n ],\n [\n -106.86744689941405,\n 35.821153818963175\n ],\n [\n -106.85714721679688,\n 35.8217105820067\n ],\n [\n -106.85302734374999,\n 35.649485098277204\n ],\n [\n -106.67587280273438,\n 35.65004306288284\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"460","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bard, Susan M.","contributorId":264967,"corporation":false,"usgs":false,"family":"Bard","given":"Susan","email":"","middleInitial":"M.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":834645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834644,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209101,"text":"70209101 - 2020 - Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale","interactions":[],"lastModifiedDate":"2020-03-16T16:52:49","indexId":"70209101","displayToPublicDate":"2020-03-14T16:47:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2049,"text":"International Journal of Greenhouse Gas Control","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale","title":"Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale","docAbstract":"Residual oil zones (ROZs) are increasingly gaining interest as potential reservoirs for carbon dioxide (CO2) storage. Here, we present a national- and regional-scale methodology for estimating prospective CO2 storage resources in residual oil zones. This methodology uses a volumetric equation that accounts for CO2 storage as a free phase in pore space and as a dissolved phase in oil and does not assume any oil production associated with CO2 storage. Reservoir modeling and the CO2-SCREEN tool are used to demonstrate that CO2 storage in residual oil zones will predominantly take place in the free phase (approximately 92–97%) with some storage as dissolution in oil (approximately 3–8 %). Based on this preliminary demonstration, the CO2 storage efficiency for ROZs using this national- and regional-scale method ranges from 0.61 to 7.1 %. This range indicates ROZs have a similar efficiency potential for storing CO2 as deep saline formations (0.51–5.4 %).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijggc.2020.103006","usgsCitation":"Sanguinito, S., Singh, H., Myshakin, E.M., Goodman, A.L., Dilmore, R.M., Grant, T.C., Morgan, D., Bromhal, G., Warwick, P., Brennan, S.T., Freeman, P., Karacan, C.O., Gorecki, C., Peck, W., Burton-Kelly, M., Dotzenrod, N., Frailey, S., and Pawar, R., 2020, Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale: International Journal of Greenhouse Gas Control, v. 96, 103006, 8 p., https://doi.org/10.1016/j.ijggc.2020.103006.","productDescription":"103006, 8 p.","ipdsId":"IP-108213","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":457370,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1780239","text":"Publisher Index 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Following optimization of an enrichment method using EC broth supplemented with colistin (1 µg/mL) and vancomycin (8 µg/mL), aliquots derived from 100 feral swine fecal samples were spiked with of one of five different mcr-1 positive E. coli strains (between 100 and 104 CFU/g), for a total of 1,110 samples tested. Enrichments were then screened using a simple boil-prep and a previously developed real-time PCR assay for mcr-1 detection. The sensitivity of the method was determined in swine feces, with mcr-1 E. coli inoculums of 0.1-9.99 CFU/g (n = 340), 10-49.99 CFU/g (n = 170), 50-99 CFU/g (n = 255), 100-149 CFU/g (n = 60), and 200-2,200 CFU/g (n = 175), which were detected with 32%, 72%, 88%, 95%, and 98% accuracy, respectively. Uninoculated controls (n = 100) were negative for mcr-1 following enrichment.","language":"English","publisher":"Elsevier ","doi":"10.1016/j.mimet.2020.105892","collaboration":"","usgsCitation":"Chandler, J.C., Franklin, A.B., Bevins, S.N., Bentler, K.T., Bonnedahl, J., Ahlstrom, C., Bisha, B., and Shriner, S.A., 2020, Validation of a screening method for the detection of colistin-resistant E. coli containing mcr-1 in feral swine feces: Journal of Microbiological Methods, v. 172, 105892, 5 p., https://doi.org/10.1016/j.mimet.2020.105892.","productDescription":"105892, 5 p.","ipdsId":"IP-114741","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":457373,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.mimet.2020.105892","text":"Publisher Index Page"},{"id":373833,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"172","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chandler, Jeffrey C","contributorId":223870,"corporation":false,"usgs":false,"family":"Chandler","given":"Jeffrey","email":"","middleInitial":"C","affiliations":[{"id":40781,"text":"USDA/APHIS/WS, National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":786508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Franklin, Alan B.","contributorId":101999,"corporation":false,"usgs":false,"family":"Franklin","given":"Alan","email":"","middleInitial":"B.","affiliations":[{"id":12434,"text":"USDA, Wildlife Services, National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":786509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bevins, Sarah N.","contributorId":212845,"corporation":false,"usgs":false,"family":"Bevins","given":"Sarah","email":"","middleInitial":"N.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":786510,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bentler, Kevin T","contributorId":223871,"corporation":false,"usgs":false,"family":"Bentler","given":"Kevin","email":"","middleInitial":"T","affiliations":[{"id":40781,"text":"USDA/APHIS/WS, National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":786511,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bonnedahl, Jonas","contributorId":181800,"corporation":false,"usgs":false,"family":"Bonnedahl","given":"Jonas","email":"","affiliations":[],"preferred":false,"id":786512,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ahlstrom, Christina 0000-0001-5414-8076","orcid":"https://orcid.org/0000-0001-5414-8076","contributorId":214540,"corporation":false,"usgs":true,"family":"Ahlstrom","given":"Christina","email":"","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":786513,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bisha, Bledar","contributorId":223872,"corporation":false,"usgs":false,"family":"Bisha","given":"Bledar","email":"","affiliations":[{"id":40782,"text":"Department of Animal Science, University of Wyoming,","active":true,"usgs":false}],"preferred":false,"id":786514,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shriner, Susan A.","contributorId":168690,"corporation":false,"usgs":false,"family":"Shriner","given":"Susan","email":"","middleInitial":"A.","affiliations":[{"id":13407,"text":"Colorado State Univ.","active":true,"usgs":false}],"preferred":false,"id":786515,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209158,"text":"70209158 - 2020 - A 'weight of evidence' approach to evaluating structural equation models","interactions":[],"lastModifiedDate":"2020-03-19T19:09:47","indexId":"70209158","displayToPublicDate":"2020-03-13T19:08:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5943,"text":"One Ecosystem","active":true,"publicationSubtype":{"id":10}},"title":"A 'weight of evidence' approach to evaluating structural equation models","docAbstract":"It is possible that model selection has been the most researched and most discussed topic in the history of both statistics and structural equation modeling (SEM). The reason for this is because selecting one model for interpretive use from amongst many possible models is both essential and difficult. The published protocols and advice for model evaluation and selection in SEM studies are complex and difficult to integrate with current approaches used in biology. Opposition to the use of p-values and decision thresholds has been voiced by the statistics community, yet certain phases of model evaluation have been historically tied to reliance on p-values. In this paper, I outline an approach to model evaluation, comparison and selection based on a weight-of-evidence paradigm. The details and proposed sequence of steps are illustrated using a real-world example. At the end of the paper, I briefly discuss the current state of knowledge and a possible direction for future studies.","language":"English","publisher":"Pensoft Publisher","doi":"10.3897/oneeco.5.e50452","usgsCitation":"Grace, J., 2020, A 'weight of evidence' approach to evaluating structural equation models: One Ecosystem, v. 5, e50452, https://doi.org/10.3897/oneeco.5.e50452.","productDescription":"e50452","ipdsId":"IP-115758","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":457375,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/oneeco.5.e50452","text":"Publisher Index Page"},{"id":373395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Grace, James 0000-0001-6374-4726","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":219648,"corporation":false,"usgs":true,"family":"Grace","given":"James","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":785160,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}