The Permian Basin, in west Texas and southeastern New Mexico is one of the largest conventional oil and gas reservoirs in the United States and is becoming one of the world’s largest continuous oil and gas (COG) reservoirs. Advances in technology have enabled oil and gas to be extracted from reservoirs that historically were developed using conventional, or vertical, well drilling techniques. Conventional oil and gas reservoirs have discrete deposits that are well defined and are typically trapped by an overlying geologic formation or caprock, whereas COG reservoirs contain deposits that are distributed evenly throughout the geologic formation, typically have much lower permeability (the capacity of a porous rock to transmit a fluid) than the conventional deposits, and require specialized horizontal extraction techniques. The methods to extract the oil from the two different reservoirs require differing amounts of water, and the horizontal extraction methods typically require substantially more water. In 2015, the U.S. Geological Survey started a topical study to quantify water used during COG development. The Permian Basin, which contains both types of reservoirs (continuous and conventional), was the second basin in the United States in the U.S. Geological Survey’s topical study to quantify water used during COG development.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213053","usgsCitation":"Houston, N.A., Ball, G.P., Galanter, A.E., Valder, J.F., McShane, R.R., Thamke, J.N., and McDowell, J.S., Estimates of Water Use Associated with Continuous Oil and Gas Development in the Permian Basin, Texas and New Mexico, 2010–2019, with Comparisons to the Williston Basin, North Dakota and Montana: U.S. Geological Survey Fact Sheet 2021–3053, 4 p., https://doi.org/10.3133/fs20213053.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-124923","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":390943,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2021/3053/images"},{"id":390942,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2021/3053/fs20213053.xml","text":"Report","size":"37.7 kB","linkFileType":{"id":8,"text":"xml"},"description":"FS 2021–3053 xml"},{"id":390941,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3053/fs20213053.pdf","text":"Report","size":"4.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3053"},{"id":390940,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3053/coverthb.jpg"}],"country":"United States","state":"Montana, New Mexico, North Dakota, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.9296875,\n 48.980216985374994\n ],\n [\n -108.720703125,\n 48.922499263758255\n ],\n [\n -108.017578125,\n 48.28319289548349\n ],\n [\n -107.57812499999999,\n 47.81315451752768\n ],\n [\n -108.369140625,\n 47.45780853075031\n ],\n [\n -108.369140625,\n 47.040182144806664\n ],\n [\n -107.05078125,\n 46.86019101567027\n ],\n [\n -105.556640625,\n 46.07323062540835\n ],\n [\n -103.0078125,\n 45.02695045318546\n ],\n [\n -100.81054687499999,\n 45.089035564831036\n ],\n [\n -99.755859375,\n 47.100044694025215\n ],\n [\n -100.107421875,\n 49.03786794532644\n ],\n [\n -107.9296875,\n 48.980216985374994\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.095703125,\n 34.252676117101515\n ],\n [\n -103.64501953125,\n 33.90689555128866\n ],\n [\n -104.56787109374999,\n 33.578014746143985\n ],\n [\n -105.0732421875,\n 32.879587173066305\n ],\n [\n -105.1171875,\n 32.02670629333614\n ],\n [\n -104.56787109374999,\n 31.42866311735861\n ],\n [\n -104.150390625,\n 30.713503990354965\n ],\n [\n -103.16162109375,\n 30.44867367928756\n ],\n [\n -102.23876953125,\n 30.391830328088137\n ],\n [\n -101.40380859375,\n 30.183121842195515\n ],\n [\n -100.5908203125,\n 29.878755346037977\n ],\n [\n -100.2392578125,\n 29.592565403314087\n ],\n [\n -99.77783203125,\n 29.82158272057499\n ],\n [\n -99.66796875,\n 30.80791068136646\n ],\n [\n -99.73388671874999,\n 31.353636941500987\n ],\n [\n -100.283203125,\n 31.615965936476076\n ],\n [\n -100.34912109375,\n 31.914867503276223\n ],\n [\n -100.61279296875,\n 32.491230287947594\n ],\n [\n -100.32714843749999,\n 32.91648534731439\n ],\n [\n -100.107421875,\n 33.706062655101206\n ],\n [\n -100.45898437499999,\n 34.19817309627726\n ],\n [\n -100.8544921875,\n 34.32529192442733\n ],\n [\n -102.12890625,\n 34.488447837809304\n ],\n [\n -102.72216796875,\n 34.397844946449865\n ],\n [\n -103.095703125,\n 34.252676117101515\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
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1505 Ferguson Lane
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The U.S. Geological Survey installed 10 rain gages and 12 calibrated H-flumes to measure rainfall and runoff volumes at 10 locations in Ohio Department of Transportation highway median-strip catchments. Data were collected to facilitate comparisons of rainfall and runoff volumes at study sites before and after stormwater best management practices (BMPs) were installed and between sites with different BMPs. The BMP treatments comprised removing the top layer of the existing soil, rototilling the remaining soil to a 6-inch depth, mixing the soils with one of two soil amendments (compost with sand or shale) at one of two thicknesses (4 inches or 6 inches), topping with a compost blanket, seeding, and installing erosion control matting. The overall treatment used at a given study site is referred to as “BMP.” At two locations where soil amendments were installed, a second “control” site was installed to measure runoff from an adjacent catchment in the same median strip where no soil amendment was installed. This no-treatment option (no soil amendment) was considered its own class of BMP.
Rainfall and runoff data were collected during periods when air temperatures were above freezing (including all months except January, February, and parts of December and March) from 2018 to 2020. The data collection period for each study site was divided into “pre-BMP” and “post-BMP” periods. Equipment to measure rainfall and runoff was installed and data were collected from April to December 2018 before installation of soil amendments (the pre-BMP period). The post-BMP period started between April and May of 2019 at the first measured rainfall after soil amendments were installed. Rainfall and runoff monitoring continued through September 2020. For control sites, the post-BMP periods were assigned to start with the first measured rainfall in the 2019 data collection season.
A rainfall-runoff “event” was defined as beginning at the time of the first measured rainfall and ending when rainfall and runoff (if any) ceased and remained ceased for at least 3 hours. A value referred to as “event runoff percentage,” defined as the total volume of runoff during an event expressed as a percentage of the total volume of rainfall falling over the catchment, was computed for each event. The distribution of rainfall totals associated with events was similar between the pre-BMP and post-BMP periods; however, there were appreciable between-site differences in the distribution of event runoff percentages during the pre-BMP and post-BMP periods.
Empirical distribution function (EDF) tests were performed with and without data from events that resulted in no runoff to determine whether the distribution of event runoff percentages changed from the pre-BMP period to the post-BMP period. The null hypothesis that the EDFs of event runoff percentages were equal in the pre-BMP and post-BMP periods was rejected (α=0.05) in at least one of the two tests for four sites (one site with a shale amendment and three sites with sand amendments). Mean event runoff percentages at each of those four sites decreased from the pre-BMP period to the post-BMP period. The null hypothesis that the EDFs of event runoff percentages were equal was not rejected for the other six sites’ draining catchments with soil amendments or the two control sites. EDF tests performed on event rainfall totals indicated no statistically significant changes between the pre-BMP and post-BMP period distributions for any of the sites.
Double-mass analyses of cumulative runoff were performed for two pairs of closely spaced sites (each pair located in a common median strip): one site in each pair drained a catchment where soil amendments were installed, and the other (a control) drained a catchment without soil amendments. Those double-mass analyses indicated a small reduction in runoff from the pre-BMP to post-BMP period at the site whose catchment received the sand and compost amendment, but no perceptible reduction in runoff at the site whose catchment received the shale and compost amendment.
Regression analyses indicated that (a) three rainfall factors (event rainfall totals, total rainfall for the previous 7 days, and a cross product of the factors) and the intercept term were the four most important factors explaining event runoff percentages, (b) the effect of amendment type on event runoff percentage was small in comparison to the rainfall and intercept terms, (c) event runoff percentages tended to be lower for sites with shale amendments than sites with sand amendments; however, event runoff percentages tended to be lower for control sites than for sites with shale or sand amendments, and (d) event runoff percentages increased with increasing amendment thickness. The counterintuitive results that event runoff percentages increased with increasing amendment thickness and that control sites tended to have lower event runoff percentages than sites draining soil-amended catchments likely reflects unmeasured factors that existed at the sites before BMPs were installed rather than the effect of the BMP treatments.
Although not definitive, some support for the conclusion that the sand amendment was generally more effective at reducing runoff than the shale amendment was provided by results from the EDF tests, double-mass analyses, and runoff statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215114","collaboration":"Prepared in cooperation with ms consultants","usgsCitation":"Whitehead, M.T., and Koltun, G.F., 2021, Assessment of runoff volume reduction associated with soil amendments added to portions of highway median-strip catchments in Ohio, 2018–20 (ver. 1.1, December 2021): U.S. Geological Survey Scientific Investigations Report 2021–5114, 27 p., https://doi.org/10.3133/sir20215114.","productDescription":"Report: vii, 27 p.; Data Release; Version History","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-118944","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":390957,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P945PKJ7","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Dataset for analyses in assessment of runoff volume reduction associated with soil amendments added to portions of highway median-strip catchments in Ohio"},{"id":390955,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5114/coverthb2.jpg"},{"id":392682,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5114/sir20215114.pdf","text":"Report","size":"5.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5114"},{"id":390958,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5114/sir20215114.XML","text":"Report","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5114 xml"},{"id":392683,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5114/versionHist.txt","text":"Version History","size":"3.07 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":390959,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5114/images"}],"country":"United States","state":"Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.6279296875,\n 39.639537564366684\n ],\n [\n -80.947265625,\n 39.639537564366684\n ],\n [\n -80.947265625,\n 41.261291493919884\n ],\n [\n -83.6279296875,\n 41.261291493919884\n ],\n [\n -83.6279296875,\n 39.639537564366684\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: October 2021; Version 1.1: December 2021","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd.
Ste 100
Columbus, OH 43229–1737
Identifying the physical habitat characteristics associated with riverine freshwater mussel assemblages is challenging but crucial for understanding the causes of mussel declines. The occurrence of mussels in multispecies beds suggests that common physical factors influence or limit their occurrence. Fine-scale geomorphic and hydraulic factors (e.g., scour, bed stability) are predictive of mussel-bed occurrence, but they are computationally challenging to represent at intermediate or riverscape scales. We used maximum entropy (MaxEnt) modeling to evaluate associations between riverscape-scale hydrogeomorphic variables and mussel-bed presence along 530 river km of the Meramec River basin, USA, to identify river reaches that are fundamentally suitable for mussels as well as those that are not. We obtained the locations of mussel beds from an existing, multiyear dataset, and we derived river variables from high-resolution, open-source datasets of aerial imagery and topography. Mussel beds occurred almost exclusively in reaches identified by our model as suitable; these were characterized by laterally stable channels, absence of adjacent bluffs, proximity to gravel bars, higher stream power, and larger areas of contiguous water (a proxy for drought vulnerability). We validated our model findings based on model sensitivity using a set of mussel-bed locations not used in model development. These findings can inform how resource managers allocate survey, monitoring, and conservation efforts.
","language":"English","publisher":"Freshwater Mollusk Conservation Society","doi":"10.31931/fmbc-d-20-00002","usgsCitation":"Keymanesh, K., Rosenberger, A.E., Lindner, G., Bouska, K.L., and McMurray, S.E., 2021, Riverscape-scale modeling of fundamentally suitable habitat for mussel assemblages in an Ozark River system, Missouri: Freshwater Mollusk Biology and Conservation, v. 24, no. 2, p. 43-58, https://doi.org/10.31931/fmbc-d-20-00002.","productDescription":"16 p.","startPage":"43","endPage":"58","ipdsId":"IP-113472","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":450336,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31931/fmbc-d-20-00002","text":"Publisher Index Page"},{"id":433559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Meramec River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.23145192205784,\n 38.57316922340365\n ],\n [\n -91.80686253958456,\n 38.57316922340365\n ],\n [\n -91.80686253958456,\n 37.62945983446684\n ],\n [\n -90.23145192205784,\n 37.62945983446684\n ],\n [\n -90.23145192205784,\n 38.57316922340365\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Keymanesh, K.","contributorId":317234,"corporation":false,"usgs":false,"family":"Keymanesh","given":"K.","email":"","affiliations":[],"preferred":false,"id":908903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberger, Amanda E. 0000-0002-5520-8349 arosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5520-8349","contributorId":5581,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Amanda","email":"arosenberger@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":908904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindner, G.","contributorId":341798,"corporation":false,"usgs":false,"family":"Lindner","given":"G.","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908905,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":908906,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McMurray, Stephen E.","contributorId":206918,"corporation":false,"usgs":false,"family":"McMurray","given":"Stephen","email":"","middleInitial":"E.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":908907,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70225582,"text":"sir20215020 - 2021 - Geologic and hydrogeologic characteristics of the White River Formation, Lance Formation, and Fox Hills Sandstone, northern greater Denver Basin, southeastern Laramie County, Wyoming","interactions":[],"lastModifiedDate":"2022-06-16T19:45:30.631881","indexId":"sir20215020","displayToPublicDate":"2021-10-27T10:00:17","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5020","displayTitle":"Geologic and Hydrogeologic Characteristics of the White River Formation, Lance Formation, and Fox Hills Sandstone, Northern Greater Denver Basin, Southeastern Laramie County, Wyoming","title":"Geologic and hydrogeologic characteristics of the White River Formation, Lance Formation, and Fox Hills Sandstone, northern greater Denver Basin, southeastern Laramie County, Wyoming","docAbstract":"In cooperation with the Wyoming State Engineer’s Office, the U.S. Geological Survey studied the geologic and hydrogeologic characteristics of Cenozoic and Upper Cretaceous strata at a location in southeastern Laramie County within the Wyoming part of the Cheyenne Basin, the northern subbasin of the greater Denver Basin. The study aimed to improve understanding of the aquifers/aquifer systems in these strata, motivated in part by declining groundwater levels and interest in exploring future groundwater supplies. Based on detailed geologic characterization using information obtained by drilling and coring a 960-foot-(ft) deep exploratory borehole, and comparisons with previously published descriptions, identified Cenozoic lithostratigraphic units included 40 ft of Quaternary older alluvial fan deposits consisting of an unconsolidated mixture of sand and gravel with lesser quantities of silt and clay in varying proportions and the underlying 407.3-ft-thick White River Formation of late Eocene-Oligocene age consisting largely of mudrocks with sparse thin beds of sandstone, muddy gravel, and conglomeratic mudrocks. Identified Upper Cretaceous lithostratigraphic units included the 351.6-ft-thick Lance Formation, consisting of terrestrial sedimentary rocks including mudrocks (muddy shale and silty and sandy shale, siltstone, claystone, and mudstone) interbedded with much smaller quantities of very fine- to medium-grained muddy and silty sandstone and coal; the 79.6-ft-thick Fox Hills Sandstone, consisting of a transitional marine sequence of muddy or silty sandstone present in five individual beds; and 86.7 ft of the upper transition member of the Pierre Shale, consisting largely of marine sedimentary rocks such as muddy shale. Beds of the upper and lower Fox Hills Sandstone were separated by tongues of the Lance Formation and upper transition member of the Pierre Shale, respectively.
The White River hydrogeologic unit, consisting of the entire White River Formation or Group at the study site, did not contain any substantial secondary permeability features in the mudrocks that composed almost all the unit. A monitoring well (BR–1) was completed in the White River aquifer with the well screen open to the only coarse-grained unit (muddy sandstone) that had sufficient thickness and permeability to be considered as an aquifer. Sampling of the well for a broad suite of constituents indicated groundwater generally was of excellent quality except dissolved arsenic was detected at a concentration greater than the U.S. Environmental Protection Agency (EPA) Maximum Contaminant Level, and dissolved sodium was measured at a concentration greater than several EPA Drinking Water Advisory Levels (DWAs) for the constituent. Well development, well purging for groundwater sampling, and calculated aquifer properties indicated the sandstone aquifer screened by monitoring well BR–1 was not very productive. Analysis of the well water-level responses in BR–1 to atmospheric loading and Earth tides indicated the responses were consistent with a confined-aquifer response with wellbore-storage effects. Hydraulic properties estimated based on these responses yielded values of hydraulic conductivity (K, 0.057 foot per day [ft/d]), specific storage (Ss, 1.6×10−6 per foot [ft−1]) and porosity (n, 0.43). Water levels filtered to remove the effects of atmospheric loading and Earth tides indicated an upward trend (+1.13 foot per year [ft/yr]) during the period analyzed, September 5, 2014, to September 30, 2017.
Lithologic characteristics of the Lance hydrogeologic unit, consisting of the entire Lance Formation at the study site, indicated a potential aquifer in a “sandy” interval in the upper part of the unit. Most of the Lance hydrogeologic unit below the “sandy” interval consisted of various low-permeability lithologies unlikely to yield substantial quantities of water. This lower part of the hydrogeologic unit likely functions as a confining unit separating the underlying Lance-Fox Hills aquifer. A geologic cross section constructed for this study indicated fine-grained sediments composed most of the Lance Formation/hydrogeologic unit not only at the study location, but also throughout southern Laramie County along the line of section and throughout the Wyoming and Colorado parts of the Cheyenne Basin. A monitoring well (LN–1) completed in a sandstone bed in the “sandy” interval of the Lance hydrogeologic unit produced a mean of about 23 gallons per minute (gal/min) during well development, indicating sandstone beds can form moderately productive confined subaquifers in this part of the hydrogeologic unit. Analysis of the well water-level responses in well LN–1 to atmospheric loading and Earth tides indicated the responses were consistent with a confined-aquifer response. Hydraulic properties estimated based on these responses yielded values for a lower bounding K of 0.60 ft/d, Ss of 1.6×10−6 ft−1, and n of 0.38. Water levels filtered to remove the effects of atmospheric loading and Earth tides indicated a downward trend (−0.86 ft/yr) during the period analyzed (November 8, 2014, to September 30, 2017). Analyses for a broad suite of constituents in samples from well LN–1 indicated groundwater quality generally was excellent, although dissolved sodium was measured at a concentration greater than two EPA DWA levels for the constituent.
Because of the absence of any overlying or intertonguing sandstone beds belonging to the lower/basal part of the Lance Formation, the Lance-Fox Hills aquifer at the study site consisted only of the five sandstone beds of the Fox Hills Sandstone. The cross section constructed for this study illustrated how the Fox Hills Sandstone, and thus, most of the Lance-Fox Hills aquifer, consists of a series of sandstone bodies that overlap (shingle) upward to the east across southern Laramie County. These bodies collectively form a fairly continuous body of sandstone, thus potentially forming an areally extensive aquifer across southern Laramie County, and by extension, throughout most of the formation’s extent in the Wyoming part of the Cheyenne Basin, as is the case in the Colorado part of the basin. A monitoring well (FH–1) completed in part of the thickest sandstone bed of the Lance-Fox Hills aquifer was moderately to highly productive and easily produced 25 to 30 gal/min after development. Substantially larger water production rates likely could be obtained by penetrating the full thickness of this bed and by completing a well open to the other overlying and underlying sandstone beds of the aquifer. Analysis of the water-level responses in well FH–1 to atmospheric loading and Earth tides indicated the responses were consistent with a confined-aquifer response. Hydraulic properties computed based on these responses yielded values for a lower bounding estimate for K of 0.26 ft/d, for Ss of 1.0×10−6 ft−1, and for n of 0.41. Water levels filtered to remove the effects of atmospheric loading and Earth tides indicated a downward trend (−1.74 ft/yr) during the period analyzed, December 19, 2014, to September 30, 2017. Sampling of monitoring well FH–1 and two production wells completed in the Fox Hills Sandstone in other parts of Laramie County indicated groundwater quality generally is excellent, although pH exceeded a recommended EPA aesthetic drinking-water standard (Secondary Maximum Contaminant Level) in two of three sampled wells, total dissolved solids concentrations exceeded the Secondary Maximum Contaminant Level in one of the two sampled production wells, and dissolved sodium was measured in all three sampled wells at a concentration greater than two EPA DWA levels for the constituent. The Wyoming Class II agricultural (irrigation) sodium adsorption ratio standard of 8 was exceeded in all three sampled wells, indicating these waters are not suitable for irrigation use.
Computed vertical hydraulic gradients indicated a strong potential for downward flow throughout the groundwater system at the study site, including from the low-yielding aquifer in the upper White River Formation/hydrogeologic unit (monitoring well BR–1) to the sandstone subaquifer in the Lance Formation/hydrogeologic unit (monitoring well LN–1), and from the Lance subaquifer (monitoring well LN–1) to the sandstone bed/aquifer that composes much of the Lance-Fox Hills aquifer thickness at the study site (monitoring well FH–1). However, large hydraulic-head differences between wells indicated high resistance to vertical flow attributable to the low vertical hydraulic conductivity of intervening strata, which consisted almost entirely of low-permeability mudrocks. The confined nature of the sandstone aquifers monitored by the various wells coupled with dissimilarities between groundwater-level fluctuations and trends in groundwater levels indicated downward flow through the intervening strata (primarily mudrocks in the various lithostratigraphic/hydrogeologic units) between the examined sets of wells likely was small.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215020","collaboration":"Prepared in cooperation with the Wyoming State Engineer’s Office","usgsCitation":"Bartos, T.T., Galloway, D.L., Hallberg, L.L., Dechesne, M., Diehl, S.F., and Davidson, S.L., 2021, Geologic and hydrogeologic characteristics of the White River Formation, Lance Formation, and Fox Hills Sandstone, northern greater Denver Basin, southeastern Laramie County, Wyoming: U.S. Geological Survey Scientific Investigations Report 2021–5020, 219 p., 1 pl., https://doi.org/10.3133/sir20215020.","productDescription":"Report: xvii, 219 p.; Appendix Table; Plate: 42.00 x 63.00 inches; Data Release; Dataset","numberOfPages":"242","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-110049","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":390939,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS groundwater data for Wyoming, in USGS water data for the Nation"},{"id":390938,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PPLA74","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Atmospheric-loading frequency response functions and groundwater levels filtered for the effects of atmospheric loading and solid Earth tides for three USGS monitoring wells, southeastern Laramie County, Wyoming, 2014–2017"},{"id":390936,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5020/sir20215020_plate.pdf","text":"Plate","size":"2.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5020 Plate","linkHelpText":"— Construction of monitoring wells BR–1, LN–1, and FH–1, and geophysical logs, generalized lithology, and interpreted lithostratigraphy for exploratory borehole LC–F1, southeastern Laramie County, Wyoming"},{"id":390937,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5020/sir20215020_table1.1.pdf","text":"Table 1.1","size":"500 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5020 Appendix Table","linkHelpText":"— Description of core collected from exploratory borehole LC–F1, southeastern Laramie County, Wyoming"},{"id":390934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5020/coverthb.jpg"},{"id":390935,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5020/sir20215020.pdf","text":"Report","size":"26.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5020"}],"country":"United States","state":"Wyoming","county":"Laramie County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-104.6506,41.651],[-104.6491,41.5656],[-104.0521,41.5654],[-104.052,41.3949],[-104.0526,41.0236],[-104.0528,41.0017],[-104.1399,41.0019],[-104.4725,41.0027],[-104.4875,41.0027],[-104.5606,41.0028],[-104.5679,41.0028],[-104.6087,41.0046],[-104.6134,41.0048],[-104.6337,41.0056],[-104.6648,41.0047],[-104.6837,41.0041],[-104.7013,41.0035],[-104.83,40.9996],[-104.8341,40.9996],[-104.9385,40.9995],[-104.9425,40.9995],[-105.1109,40.9993],[-105.2763,40.9998],[-105.2774,41.6567],[-105.1706,41.6535],[-105.0575,41.6537],[-104.9419,41.6537],[-104.6506,41.651]]]},\"properties\":{\"name\":\"Laramie\",\"state\":\"WY\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Black Carp (Mylopharyngodon piceus) has invaded the Mississippi River and is a potential threat to native mollusks. During prior diet research, we discovered that the fluke Aspidogaster conchicola, a mollusk parasite, occurs regularly in the gastrointestinal tract of Black Carp. The fluke remains in fish intestines for extended periods after the fish has consumed its host. Flukes were found in 33% of the wild Black Carp examined, and numbers ranged from 1 to 802, with no pattern evident across seasons of fish capture. Treating the flukes as indicators of prior mollusk consumption, we adjusted the percent occurrence of mollusks from 26.6% to 54.1%, indicating that the previously reported incidences for bivalves (22.8%) and gastropods (16.5%) in the diet of wild Black Carp are likely to be underestimated. Based on percent occurrences in Black Carp, larger fish (>791 mm) had significantly higher fluke occurrence (42.6%) and fish captured from lentic habitats had significantly greater fluke-adjusted mollusk occurrence (87.5%). These diet-occurrence estimates, coupled with the presence of gravid A. conchicola and evidence of their continued viability in Black Carp intestines, indicate that these fish retain evidence of mollusk consumption for extended periods after evacuation of the gastrointestinal tract. Consequently, Black Carp has the potential to disperse this parasite to other mollusks.
","language":"English","publisher":"Freshwater Mollusk Conservation Society","doi":"10.31931/fmbc-d-20-00011","usgsCitation":"Poulton, B.C., Bailey, J., Kroboth, P., George, A.E., and Chapman, D., 2021, Invasive black carp as a reservoir host for the freshwater mollusk parasite Aspidogaster conchicola: Further evidence of mollusk consumption and implications for parasite dispersal: Freshwater Mollusk Biology and Conservation, v. 24, no. 2, p. 114-123, https://doi.org/10.31931/fmbc-d-20-00011.","productDescription":"10 p.","startPage":"114","endPage":"123","ipdsId":"IP-112532","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":450340,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31931/fmbc-d-20-00011","text":"Publisher Index Page"},{"id":391161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.75830078125,\n 39.99395569397331\n ],\n [\n -89.09912109375,\n 40.68063802521456\n ],\n [\n -89.80224609374999,\n 41.11246878918088\n ],\n [\n -91.16455078125,\n 40.17887331434696\n ],\n [\n -91.5380859375,\n 39.67337039176558\n ],\n [\n -91.7138671875,\n 38.54816542304656\n ],\n [\n -90.4833984375,\n 37.59682400108367\n ],\n [\n -89.7802734375,\n 37.020098201368114\n ],\n [\n -90.2197265625,\n 36.01356058518153\n ],\n [\n -91.34033203125,\n 34.30714385628804\n ],\n [\n -91.64794921875,\n 32.676372772089834\n ],\n [\n -92.08740234375,\n 30.90222470517144\n ],\n [\n -91.42822265625,\n 30.56226095049944\n ],\n [\n -91.318359375,\n 31.052933985705163\n ],\n [\n -90.85693359375,\n 31.93351676190369\n ],\n [\n -90.72509765625,\n 33.19273094190692\n ],\n [\n -90.087890625,\n 34.63320791137959\n ],\n [\n -88.87939453125,\n 36.31512514748051\n ],\n [\n -88.30810546875,\n 36.527294814546245\n ],\n [\n -87.978515625,\n 36.721273880045004\n ],\n [\n -88.154296875,\n 37.24782120155428\n ],\n [\n -88.70361328125,\n 38.89103282648846\n ],\n [\n -89.75830078125,\n 39.99395569397331\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Poulton, Barry C. 0000-0002-7219-4911 bpoulton@usgs.gov","orcid":"https://orcid.org/0000-0002-7219-4911","contributorId":2421,"corporation":false,"usgs":true,"family":"Poulton","given":"Barry","email":"bpoulton@usgs.gov","middleInitial":"C.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":826047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bailey, Jennifer","contributorId":212231,"corporation":false,"usgs":false,"family":"Bailey","given":"Jennifer","email":"","affiliations":[{"id":38464,"text":"USFWS, LaCrosse Fish Health Center, Midwest Fisheries Center","active":true,"usgs":false}],"preferred":false,"id":826048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kroboth, Patrick 0000-0002-9447-4818","orcid":"https://orcid.org/0000-0002-9447-4818","contributorId":216578,"corporation":false,"usgs":true,"family":"Kroboth","given":"Patrick","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":826049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"George, Amy E. 0000-0003-1150-8646 ageorge@usgs.gov","orcid":"https://orcid.org/0000-0003-1150-8646","contributorId":3950,"corporation":false,"usgs":true,"family":"George","given":"Amy","email":"ageorge@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":826050,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":826051,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70225686,"text":"70225686 - 2021 - Telemetry reveals migratory drivers and disparate space use across seasons and age-groups in American horseshoe crabs","interactions":[],"lastModifiedDate":"2021-11-03T13:06:09.653159","indexId":"70225686","displayToPublicDate":"2021-10-27T08:03:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Telemetry reveals migratory drivers and disparate space use across seasons and age-groups in American horseshoe crabs","docAbstract":"Identifying mechanisms that underpin animal migration patterns and examining variability in space use within populations is crucial for understanding population dynamics and management implications. In this study, we quantified the migration rates, seasonal changes in migratory connectivity, and residency across population demographics (age and sex) to understand the proximate cues of migration timing in American horseshoe crabs (Limulus polyphemus). Juvenile (n = 25) and adult (n = 70) horseshoe crabs were tracked with acoustic telemetry techniques for a 3-yr period in Moriches Bay, NY. Connectivity metrics and residency probability were quantified through spatial network analysis and empirically derived Markov Chain models (EDMC), respectively. The migratory probability of adult horseshoe crabs between Moriches Bay and the Atlantic Ocean was estimated to be 41.0% (95% CI: 34.0–59.8); in contrast, only 8% (95% CI: 1.2–31.6) of juveniles migrated into the ocean. Migration timing was influenced by the interaction of photoperiod and temperature, revealing seasonal differences in migration timing and a 50% narrower range of photoperiod and temperature over which fall migrations occurred compared to spring. Sex-specific differences in space use and connectivity within each season were largely absent; however, centralized habitats were important for maintaining connectivity across all seasons. EDMC results revealed that when standardized to the number of horseshoe crab detections on each receiver, the centrally located habitats in Moriches Bay and Inlet accounted for >50% of the total relative residency probability within most seasons, indicating these areas may be preferred by adult horseshoe crabs. Ontogenetic differences in maximum spatial extent, space use, and connectivity were observed in the bay, as juveniles exhibited lower linkages between locations (n = 4) relative to adults (n = 13) during the same temporal period. Our work highlights the application of novel quantitative approaches for addressing the movement dynamics of horseshoe crabs that can be readily applied to other taxa in the context of wildlife conservation.
Some pathogens sustain transmission in multiple different host species, but how this epidemiologically important feat is achieved remains enigmatic. Sarcoptes scabiei is among the most host generalist and successful of mammalian parasites. We synthesize pathogen and host traits that mediate sustained transmission and present cases illustrating three transmission mechanisms (direct, indirect, and combined). The pathogen traits that explain the success of S. scabiei include immune response modulation, on-host movement capacity, off-host seeking behaviors, and environmental persistence. Sociality and host density appear to be key for hosts in which direct transmission dominates, whereas in solitary hosts, the use of shared environments is important for indirect transmission. In social den-using species, combined direct and indirect transmission appears likely. Empirical research rarely considers the mechanisms enabling S. scabiei to become endemic in host species—more often focusing on outbreaks. Our review may illuminate parasites’ adaptation strategies to sustain transmission through varied mechanisms across host species.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/biosci/biab106","usgsCitation":"Browne, E., Driessen, M., Cross, P., Escobar, L.E., Foley, J.E., , L., Niedringhaus, K., Rossi, L., and Carver, S., 2021, Sustaining transmission in different host species: The emblematic case of Sarcoptes scabiei: BioScience, biab106, 11 p., https://doi.org/10.1093/biosci/biab106.","productDescription":"biab106, 11 p.","ipdsId":"IP-128884","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":450343,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1093/biosci/biab106","text":"External Repository"},{"id":391312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-10-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Browne, E","contributorId":268241,"corporation":false,"usgs":false,"family":"Browne","given":"E","email":"","affiliations":[{"id":16141,"text":"University of Tasmania","active":true,"usgs":false}],"preferred":false,"id":826258,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driessen, MM","contributorId":268242,"corporation":false,"usgs":false,"family":"Driessen","given":"MM","email":"","affiliations":[{"id":55606,"text":"Department of Primary Industries, Parks, Water and Environment, Tasmanian Government.","active":true,"usgs":false}],"preferred":false,"id":826259,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cross, Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":204814,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":826260,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Escobar, L. E. 0000-0001-5735-2750","orcid":"https://orcid.org/0000-0001-5735-2750","contributorId":260844,"corporation":false,"usgs":false,"family":"Escobar","given":"L.","email":"","middleInitial":"E.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":826261,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Foley, Janet E.","contributorId":148029,"corporation":false,"usgs":false,"family":"Foley","given":"Janet","email":"","middleInitial":"E.","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":826262,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":" Lopez-Olvera","contributorId":268245,"corporation":false,"usgs":false,"given":"Lopez-Olvera","email":"","affiliations":[{"id":55608,"text":"Universitat Autònoma de Barcelona","active":true,"usgs":false}],"preferred":false,"id":826263,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Niedringhaus, KD","contributorId":268246,"corporation":false,"usgs":false,"family":"Niedringhaus","given":"KD","email":"","affiliations":[{"id":39308,"text":"Southeastern Cooperative Wildlife Disease Study","active":true,"usgs":false}],"preferred":false,"id":826264,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rossi, Liza","contributorId":267849,"corporation":false,"usgs":false,"family":"Rossi","given":"Liza","email":"","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":826265,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Carver, Scott 0000-0002-3579-7588","orcid":"https://orcid.org/0000-0002-3579-7588","contributorId":197456,"corporation":false,"usgs":false,"family":"Carver","given":"Scott","email":"","affiliations":[],"preferred":false,"id":826266,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70246522,"text":"70246522 - 2021 - Diagenetic barite-pyrite-wurtzite formation and redox signatures in Triassic mudstone, Brooks Range, northern Alaska","interactions":[],"lastModifiedDate":"2023-07-10T13:20:53.475537","indexId":"70246522","displayToPublicDate":"2021-10-27T06:37:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"Diagenetic barite-pyrite-wurtzite formation and redox signatures in Triassic mudstone, Brooks Range, northern Alaska","docAbstract":"Mineralogical and geochemical studies of interbedded black and gray mudstones in the Triassic part of the Triassic-Jurassic Otuk Formation (northern Alaska) document locally abundant barite and pyrite plus diverse redox signatures. These strata, deposited in an outer shelf setting at paleolatitudes of ~45 to 60°N, show widespread sedimentological evidence for bioturbation. Barite occurs preferentially in black mudstones (TOC = 0.93–6.46 wt%), forming displacive euhedral crystals with pyrite inclusions and rims, and late albite inclusions or intergrowths. Pyrite also occurs as small (3–20 μm) framboids, discontinuous laminae, euhedral and anhedral crystals, and replacements of barite and fossils (mainly radiolarians). Paragenetically early wurtzite is present as clusters of very small (1–3 μm) aggregates of radiating crystals 0.5 to 1.0 μm long with cores of organic matter that overgrow framboidal pyrite; later wurtzite forms 10- to 30-μm bladed crystals. Equant grains (3–30 μm) and small (20 μm) angular clusters of zinc sulfide that include <1-μm-long, comb-like structures are sphalerite or wurtzite, or both. Minor siderite forms euhedral crystals intergrown with albite that enclose wurtzite and barite. Illite shows intergrowths with sphalerite; rare K-feldspar is intergrown with barite. Formation of these minerals and assemblages is attributed to early diagenetic processes.
Whole-rock geochemical data for 15 samples show large ranges in redox proxies including Post Archean Average Shale (PAAS)-normalized enrichment factors (EFs) for V, U, Mo, and Re, and Al-normalized ratios for V, U, and Mo. Results for most black mudstones, with or without abundant barite and/or pyrite, suggest deposition within an oxygen minimum zone. Cerium anomalies, PAAS-normalized and calculated on a detrital-free basis, range widely from 0.49 to 0.96 and may reflect diagenetic overprinting by Ce-depleted fluids. Considering data for both black and gray mudstones, the overall geochemical pattern together with evidence from pyrite framboid sizes suggest that redox conditions fluctuated greatly from euxinic to oxic, like the redox profiles reported for modern shelf sediments offshore Peru and Namibia. The euxinic redox signatures in some Otuk black mudstones may correlate with widespread Early to Middle Triassic ocean anoxic events proposed for other regions.
Calculations of median EFs for trace elements in Otuk black mudstones reveal both enrichments and depletions. Normalizations to the median composition of the three least-mineralized black mudstones show that barite- and/or pyrite-rich samples display large (>50%) positive changes for Li (+80.4%), V (+75.6%), Sr (+75.9%), Ba (+790%), Cu (+92.1%), Ni (+169%), Ag (+156%), Au (+3091%), As (+109%), Sb (+476%), and Se (+205%); Zn shows a moderate positive change of +42.1%. Moderate negative changes are evident only for Ge (−47.2%) and W (−30.6%). The local enrichments may reflect one or more factors including redox variations in bottom waters and pore fluids, element mobility during diagenesis, and selective fractionation into minerals such as barite, pyrite, and wurtzite. Anomalously low U/Al and UEF values, compared to those for other modern and ancient organic-rich sediments and sedimentary rocks, are attributed to increased solubility and loss of U during bioturbation-related oxygenation in the subsurface.
Physicochemical constraints on barite, pyrite, and wurtzite formation are informed by use of a pH-fO2 plot constructed at 10 °C. Based on paragenetic evidence for multistage deposition of these three minerals, together with the presence of illite intergrown with ZnS and K-feldspar with barite, proposed diagenetic trends involve an increase in pH and fO2 related to the ingress of sulfate-rich pore fluids during bioturbation, followed by a return to lower then higher pH and fO2 conditions linked to carbon, sulfur, barium, and iron cycling during diagenesis. Labile Ba of marine pelagic origin was mobilized from organic-rich sediment upward to the sulfate-methane transition zone where barite precipitated during the interaction of reduced Ba- and CH4-rich fluids with sulfate-bearing pore fluids. The formation of paragenetically early wurtzite (ZnS) crystals, as well as locally high EF values for Cu, Ni, Ag, and Au, is attributed to metal enrichment of pore fluids, with sources being derived in part from water-column deposition from hydrothermal plumes related to coeval Triassic seafloor vent systems including a volcanogenic massive sulfide deposit in British Columbia and the Wrangellia Large Igneous Province in Alaska.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2021.120568","usgsCitation":"Slack, J.F., McAleer, R.J., Shanks, W., and Dumoulin, J.A., 2021, Diagenetic barite-pyrite-wurtzite formation and redox signatures in Triassic mudstone, Brooks Range, northern Alaska: Chemical Geology, v. 585, 120568, 22 p., https://doi.org/10.1016/j.chemgeo.2021.120568.","productDescription":"120568, 22 p.","ipdsId":"IP-130237","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":450344,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2021.120568","text":"Publisher Index Page"},{"id":418739,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -168.604267717169,\n 71.70733094087223\n ],\n [\n -168.604267717169,\n 67.12370451837805\n ],\n [\n -140.49132965188403,\n 67.12370451837805\n ],\n [\n -140.49132965188403,\n 71.70733094087223\n ],\n [\n -168.604267717169,\n 71.70733094087223\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"585","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Slack, John F. 0000-0001-6600-3130 jfslack@usgs.gov","orcid":"https://orcid.org/0000-0001-6600-3130","contributorId":1032,"corporation":false,"usgs":true,"family":"Slack","given":"John","email":"jfslack@usgs.gov","middleInitial":"F.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":877040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":877041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shanks, Wayne (Pat)","contributorId":240838,"corporation":false,"usgs":true,"family":"Shanks","given":"Wayne (Pat)","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":877042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dumoulin, Julie A. 0000-0003-1754-1287 dumoulin@usgs.gov","orcid":"https://orcid.org/0000-0003-1754-1287","contributorId":203209,"corporation":false,"usgs":true,"family":"Dumoulin","given":"Julie","email":"dumoulin@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":877043,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225619,"text":"70225619 - 2021 - Effects of sea ice decline and summer land use on polar bear home range size in the Beaufort Sea","interactions":[],"lastModifiedDate":"2021-10-28T13:48:10.388947","indexId":"70225619","displayToPublicDate":"2021-10-26T08:47:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Effects of sea ice decline and summer land use on polar bear home range size in the Beaufort Sea","docAbstract":"Animals responding to habitat loss and fragmentation may increase their home ranges to offset declines in localized resources or they may decrease their home ranges and switch to alternative resources. In many regions of the Arctic, polar bears (Ursus maritimus) exhibit some of the largest home ranges of any quadrupedal mammal. Polar bears are presently experiencing a rapid decline in Arctic sea ice extent and a change in sea ice composition. For the Southern Beaufort Sea subpopulation of polar bears, this has resulted in a divergent movement pattern where most of the subpopulation remains on the sea ice in the summer melt season while the remainder move to land. We evaluated the effects of summer land use and maternal denning on the annual and seasonal utilization distribution size (i.e., home range) of adult female polar bears in the Southern Beaufort Sea subpopulation over 30 yr (1986–2016) during a period of rapid sea ice decline. For bears that remained on the summer sea ice, model-derived mean annual utilization distributions were 64% larger in 1999–2016 (
In the southwestern United States, non-native grass invasions have increased wildfire occurrence in deserts and the likelihood of fire spread to and from other biomes with disparate fire regimes. The elevational transition between desertscrub and montane grasslands, woodlands, and forests generally occurs at ∼1,200 masl and has experienced fast suburbanization and an expanding wildland-urban interface (WUI). In summer 2020, the Bighorn Fire in the Santa Catalina Mountains burned 486 km2 and prompted alerts and evacuations along a 40-km stretch of WUI below 1,200 masl on the outskirts of Tucson, Arizona, a metropolitan area of >1M people. To better understand the changing nature of the WUI here and elsewhere in the region, we took a multidimensional and timely approach to assess fire dynamics along the Desertscrub-Semi-desert Grassland ecotone in the Catalina foothills, which is in various stages of non-native grass invasion. The Bighorn Fire was principally a forest fire driven by a long-history of fire suppression, accumulation of fine fuels following a wet winter and spring, and two decades of hotter droughts, culminating in the hottest and second driest summer in the 125-yr Tucson weather record. Saguaro (Carnegia gigantea), a giant columnar cactus, experienced high mortality. Resprouting by several desert shrub species may confer some post-fire resiliency in desertscrub. Buffelgrass and other non-native species played a minor role in carrying the fire due to the patchiness of infestation at the upper edge of the Desertscrub biome. Coupled state-and-transition fire-spread simulation models suggest a marked increase in both burned area and fire frequency if buffelgrass patches continue to expand and coalesce at the Desertscrub/Semi-desert Grassland interface. A survey of area residents six months after the fire showed awareness of buffelgrass was significantly higher among residents that were evacuated or lost recreation access, with higher awareness of fire risk, saguaro loss and declining property values, in that order. Sustained and timely efforts to document and assess fast-evolving fire connectivity due to grass invasions, and social awareness and perceptions, are needed to understand and motivate mitigation of an increasingly fire-prone future in the region.
In active volcanic regions, high-temperature chemical reactions in the hydrothermal system consume CO2 sourced from magma or from the deep crust, whereas reactions with silicates at shallow depths mainly consume atmospheric CO2. Numerous studies have quantified the load of dissolved solids in rivers that drain volcanic regions to determine chemical weathering rates and atmospheric CO2 consumption rates. However, the balance between thermal and non-thermal components to riverine fluxes in these areas remains poorly constrained, hindering accurate estimates of atmospheric CO2 consumption rates. Here we use the Ge/Si ratio and the stable silicon isotopes (δ30Si) as tracers for quantifying non-thermal silicon contributions in rivers draining the Yellowstone Plateau Volcanic Field, USA. The Ge/Si ratio (µmol.mol−1) was determined for seven thermal water samples (183 ± 22), eight rivers (35 ± 23) and six creeks flowing into Yellowstone Lake (5 ± 3) during base flow and during peak water discharge following snowmelt. The δ30Si value (‰) was determined for thermal waters (−0.09 ± 0.04), Yellowstone River at Yellowstone Lake outlet (1.91 ± 0.23) and creek samples (0.82 ± 0.29). The calculated atmospheric CO2 consumption associated with non-thermal waters flowing through Yellowstone's rivers during peak discharge is ∼3.03 ton.km−2.yr−1, which is ∼2% of the annual mean atmospheric CO2 consumption in other volcanic regions. This study highlights the significance of quantifying seasonal variations in chemical weathering rates for improving estimates of atmospheric CO2 consumption rates in active volcanic regions.
The U.S. Geological Survey, in cooperation with the Alabama Department of Transportation, evaluated the role of culvert construction in altering streams and habitats of benthic macroinvertebrate communities at selected study sites in the northern East Gulf Coastal Plain of Alabama during 2011–19. Analysis included examinations of changes in stream channel geometry, suspended sediment, turbidity, and benthic macroinvertebrate communities.
Topographic surveys of stream channel cross sections, upstream and downstream of the culvert, were conducted before and after construction. Changes in channel geometry (cross-sectional area, top width, mean depth, and thalweg slope) were assessed by using paired sample t-tests to compare before- and after-construction channel geometry measurements. Statistically significant changes in stream channel geometry between the before- and after-construction measurements were observed at four of the six study sites. Analysis of the channel geometry data indicates that 1 site had no measured changes, and thalweg reach slopes were inverted at 4 of the 12 study reaches—2 measured in before-construction reaches and 2 measured in after-construction reaches.
Surface-water samples were collected during selected storm events for suspended sediment and turbidity analyses. Samples were simultaneously collected upstream and downstream of the culvert construction reaches during all three phases of construction (before, during, and after). Analysis focused on the parity of upstream to downstream simultaneous samples. The mean upstream to downstream paired ratios of sediment concentrations and turbidity from the after-construction phase indicate that colloidal and noncolloidal sediments were passing through the construction reaches at two of the six sites, noncolloidal sediments were being trapped in the construction reaches at two sites, and colloidal and noncolloidal sediments were being removed from the construction reach at two sites.
Benthic macroinvertebrates were collected and identified at five of the six sites from instream habitats that were available in sampled areas both upstream and downstream of the culvert construction reaches. Differences between upstream and downstream reaches and the Wilcoxon rank sum statistic were used to examine changes in metrics of benthic macroinvertebrate communities between before- and after-construction phases. Benthic macroinvertebrate sampling results did not indicate that culvert construction caused impairment to communities at study sites. No tolerance metrics suggested a major change in the pollution tolerance of the communities. The same upstream to downstream patterns in abundance-weighted tolerance values were observed in the before- and after-construction periods at each site. At one site, the difference between upstream and downstream richness-based tolerance values increased, but the after-construction upstream and downstream richness-based tolerance values were lower (indicating a less pollution-tolerant macroinvertebrate community) than in the before-construction period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215096","collaboration":"Prepared in cooperation with the Alabama Department of Transportation","usgsCitation":"Pugh, A.L., and Gill, A.C., 2021, Effects of culvert construction on streams and macroinvertebrate communities at selected sites in the East Gulf Coastal Plain of Alabama, 2010–19: U.S. Geological Survey Scientific Investigations Report 2021–5096, 52 p., https://doi.org/10.3133/sir20215096.","productDescription":"Report: vii, 52 p.; Data Release; Dataset","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-097029","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":390797,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P906BOVO","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Aerial imagery, benthic macroinvertebrate, topographic survey, and soil survey datasets collected for a study of effects of culverts on the natural conditions of streams in the East Gulf Coastal Plain of Alabama, 2010–2019"},{"id":390796,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5096/sir20215096.pdf","text":"Report","size":"15.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5096"},{"id":390795,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5096/coverthb.jpg"},{"id":390798,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Alabama","otherGeospatial":"East Gulf Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.08837890625,\n 34.69646117272349\n ],\n [\n -88.165283203125,\n 34.69646117272349\n ],\n [\n -88.505859375,\n 31.98012335736804\n ],\n [\n -88.363037109375,\n 30.315987718557867\n ],\n [\n -88.121337890625,\n 30.268556249047727\n ],\n [\n -87.747802734375,\n 30.173624550358536\n ],\n [\n -87.418212890625,\n 30.35391637229704\n ],\n [\n -87.264404296875,\n 30.477082932837682\n ],\n [\n -87.4072265625,\n 30.581179257386985\n ],\n [\n -87.451171875,\n 30.741835717889792\n ],\n [\n -87.593994140625,\n 30.86451022625836\n ],\n [\n -87.5390625,\n 31.005862904624205\n ],\n [\n -84.979248046875,\n 30.996445897426373\n ],\n [\n -85.067138671875,\n 31.175209828310845\n ],\n [\n -85.045166015625,\n 31.306715155075167\n ],\n [\n -85.067138671875,\n 31.456782472114313\n ],\n [\n -85.078125,\n 31.71882222408327\n ],\n [\n -85.0341796875,\n 31.970803930433096\n ],\n [\n -84.88037109375,\n 32.25926542645933\n ],\n [\n -84.9462890625,\n 32.35212281198644\n ],\n [\n -85.045166015625,\n 32.46342595776104\n ],\n [\n -85.23193359375,\n 32.48196313217176\n ],\n [\n -85.6494140625,\n 32.54681317351514\n ],\n [\n -86.044921875,\n 32.57459172113418\n ],\n [\n -86.693115234375,\n 32.648625783736726\n ],\n [\n -87.132568359375,\n 32.685619853722\n ],\n [\n -87.4951171875,\n 32.85190345738802\n ],\n [\n -87.73681640625,\n 33.109948297894285\n ],\n [\n -87.82470703125,\n 33.53223722395908\n ],\n [\n -87.91259765625,\n 34.10725639663118\n ],\n [\n -87.95654296875,\n 34.379712580462204\n ],\n [\n -88.08837890625,\n 34.69646117272349\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The key to Grasshopper Sparrow (Ammodramus savannarum) management is providing large areas of contiguous grassland of intermediate height with moderately deep litter and low shrub density. Grasshopper Sparrows have been reported to use habitats with 8–166 centimeters (cm) average vegetation height, 4–80 cm visual obstruction reading, 12–95 percent grass cover, 4–40 percent forb cover, less than 35 percent shrub cover, less than or equal to (≤) 38 percent bare ground, 5–61 percent litter cover, and ≤9 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842GG","usgsCitation":"Shaffer, J.A., Igl, L.D., Johnson, D.H., Sondreal, M.L., Goldade, C.M., Nenneman, M.P., Wooten, T.L., and Euliss, B.R., 2021, The effects of management practices on grassland birds—Grasshopper Sparrow (Ammodramus savannarum) (ver. 1.1, May 2023), chap. GG of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 59 p., https://doi.org/10.3133/pp1842GG.","productDescription":"v, 59 p.","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097131","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":416617,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/pp/1842/gg/versionHist.txt","text":"Version History","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":390671,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/gg/pp1842gg.pdf","text":"Report","size":"2.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–GG"},{"id":390670,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/gg/coverthb2.jpg"}],"edition":"Version 1.0: October 25, 2021; Version 1.1: May 2, 2023","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Lahars are one of the greatest hazards at many volcanoes, including Volcán de Fuego (Guatemala). On 1 December 2018 at 8:00pm local Guatemala time (2:00:00 UTC), an hour-long lahar event was detected at Volcán de Fuego by two permanent seismo-acoustic stations along the Las Lajas channel on the southeast side. To establish the timing, duration, and speed of the lahar, infrasound array records were examined to identify both the source direction(s) and the correlated energy fluctuations at the two stations. Co-located seismic and acoustic signals were also examined, which indicated at least 5 distinct energy pulses within the lahar record. We infer that varying sediment load and/or changes in flow velocity is shown by clear fluctuations in the acoustic and seismic power recorded at one of the stations. This particular event studied with infrasound provides insight into how lahars occur around Volcán de Fuego.
","language":"English","publisher":"Presses universitaires de Strasbourg","doi":"10.30909/vol.04.02.239256","usgsCitation":"Bosa, A., Johnson, J., DeAngelis, S., Lyons, J.J., Roca, A., Anderson, J., and Pineda, A., 2021, Tracking secondary lahar flow paths and characterizing pulses and surges using infrasound array networks at Volcán de Fuego, Guatemala: Volcanica, v. 4, no. 2, p. 239-256, https://doi.org/10.30909/vol.04.02.239256.","productDescription":"18 p.","startPage":"239","endPage":"256","ipdsId":"IP-130024","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":390965,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":450356,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.30909/vol.04.02.239256","text":"Publisher Index Page"}],"country":"Guatemala","otherGeospatial":"Volcán de Fuego","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.94242095947266,\n 14.396773712446521\n ],\n [\n -90.81298828125,\n 14.396773712446521\n ],\n [\n -90.81298828125,\n 14.500170089974075\n ],\n [\n -90.94242095947266,\n 14.500170089974075\n ],\n [\n -90.94242095947266,\n 14.396773712446521\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Bosa, Ashley 0000-0002-6981-0306","orcid":"https://orcid.org/0000-0002-6981-0306","contributorId":268013,"corporation":false,"usgs":false,"family":"Bosa","given":"Ashley","email":"","affiliations":[],"preferred":false,"id":825725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Jeffery","contributorId":268014,"corporation":false,"usgs":false,"family":"Johnson","given":"Jeffery","email":"","affiliations":[],"preferred":false,"id":825726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelis, Silvio","contributorId":268015,"corporation":false,"usgs":false,"family":"DeAngelis","given":"Silvio","affiliations":[],"preferred":false,"id":825727,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":825728,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roca, Amilcar","contributorId":268016,"corporation":false,"usgs":false,"family":"Roca","given":"Amilcar","email":"","affiliations":[],"preferred":false,"id":825729,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Jacob F. 0000-0001-6447-6778","orcid":"https://orcid.org/0000-0001-6447-6778","contributorId":268017,"corporation":false,"usgs":false,"family":"Anderson","given":"Jacob F.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":825730,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pineda, Armando","contributorId":268018,"corporation":false,"usgs":false,"family":"Pineda","given":"Armando","email":"","affiliations":[],"preferred":false,"id":825731,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227082,"text":"70227082 - 2021 - Establishing the foundation for the global observing system for marine life","interactions":[],"lastModifiedDate":"2021-12-29T15:31:23.610526","indexId":"70227082","displayToPublicDate":"2021-10-25T09:22:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Establishing the foundation for the global observing system for marine life","docAbstract":"Maintaining healthy, productive ecosystems in the face of pervasive and accelerating human impacts including climate change requires globally coordinated and sustained observations of marine biodiversity. Global coordination is predicated on an understanding of the scope and capacity of existing monitoring programs, and the extent to which they use standardized, interoperable practices for data management. Global coordination also requires identification of gaps in spatial and ecosystem coverage, and how these gaps correspond to management priorities and information needs. We undertook such an assessment by conducting an audit and gap analysis from global databases and structured surveys of experts. Of 371 survey respondents, 203 active, long-term (>5 years) observing programs systematically sampled marine life. These programs spanned about 7% of the ocean surface area, mostly concentrated in coastal regions of the United States, Canada, Europe, and Australia. Seagrasses, mangroves, hard corals, and macroalgae were sampled in 6% of the entire global coastal zone. Two-thirds of all observing programs offered accessible data, but methods and conditions for access were highly variable. Our assessment indicates that the global observing system is largely uncoordinated which results in a failure to deliver critical information required for informed decision-making such as, status and trends, for the conservation and sustainability of marine ecosystems and provision of ecosystem services. Based on our study, we suggest four key steps that can increase the sustainability, connectivity and spatial coverage of biological Essential Ocean Variables in the global ocean: (1) sustaining existing observing programs and encouraging coordination among these; (2) continuing to strive for data strategies that follow FAIR principles (findable, accessible, interoperable, and reusable); (3) utilizing existing ocean observing platforms and enhancing support to expand observing along coasts of developing countries, in deep ocean basins, and near the poles; and (4) targeting capacity building efforts. Following these suggestions could help create a coordinated marine biodiversity observing system enabling ecological forecasting and better planning for a sustainable use of ocean resources.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2021.737416","usgsCitation":"Satterthwaite, E.V., Bax, N.J., Miloslavich, P., Ratnarajah, L., Canonico, G., Dunn, D., Simmons, S.E., Carini, R., Evans, K., Allain, V., Appeltans, W., Batten, S., Benedetti-Cecchi, L., Bernard, A.T., Bristol, R., Benson, A., Buttigieg, P.L., Gerhardinger, L.C., Chiba, S., Davies, T.E., Duffy, J., Giron-Nava, A., Hsu, A.J., Kraberg, A.C., Kudela, R.M., Lear, D., Montes, E., Muller-Karger, F., O’Brien, T.D., Obura, D., Provoost, P., Pruckner, S., Rebelo, L., Selig, E.R., Kjesbu, O.S., Starger, C., Stuart-Smith, R.D., Vierros, M., Waller, J.S., Weatherdon, L.V., Wellman, T., and Zivian, A., 2021, Establishing the foundation for the global observing system for marine life: Frontiers in Marine Science, v. 8, 737416, 19 p., https://doi.org/10.3389/fmars.2021.737416.","productDescription":"737416, 19 p.","ipdsId":"IP-127529","costCenters":[{"id":191,"text":"Colorado Water Science 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monitoring","docAbstract":"The Long Term Resource Monitoring (LTRM) element of the Upper Mississippi River Restoration program employs a harvest method for sampling submersed aquatic vegetation (SAV) whereby a rake is dragged ~1.5 m over the substrate and plant materials are retrieved. “Plant density” (PD) scores indicate SAV abundance and are based on the amount of plant material collected on the teeth of the rake. Standard PD scores are ordered, whole numbers from 0 (no SAV on the rake) to 5 (80-100% of rake teeth full) and are assigned at each subsite for all species combined and for each individual species.
In LTRM monitoring between 1998 and 2018, ~73% of non-zero, all-species-combined PD scores were 1s, and ~89% of individual SAV species were 1s. The preponderance of PD = 1 scores along with the wide range of fresh mass represented by PD = 1 (quantified in Drake and Lund 2020) limits inference about SAV abundance from LTRM monitoring data.
Field personnel noted that small plant fragments comprised a substantial fraction of PD = 1 observations and proposed a modification of the existing LTRM methods where PD = 1 was subdivided to include “trace” scores to represent such small fragments. Trace was defined as PD = 0.08, indicating a maximum of 1 of 13 gaps in the sampling rake filled to the level of an original PD = 1. Amounts of plant material greater than PD = 0.08 and up to the original score of 1 were defined PD = +1. This study used field data collected in 2018 (scoring and fresh weights of scored plant materials) from 136 vegetated sites in Pools 4, 8 and 13 to evaluate the proposed subdivision and to examine among-pool differences in PD data. In the study data, 33% of all-species-combined observations and 69% of species (grouped by morphology) that would previously have received a score of 1 were classified as PD = 0.08. PD scores of 0.08, +1, and 2-3 represented statistically distinct amounts of fresh mass in rake samples. There were systematic differences in the mass of SAV reflected by PD score based on plant morphology and species composition. The mean fresh mass of plant materials assigned a given PD score varied among the three pools, suggesting bias attributable to personnel. To reduce this bias in future data collection efforts, the field crews incorporated a calibration of plant density scores in annual field training. The results presented here describe how including a trace PD score in LTRM data collection improves the description of SAV abundance and consequently estimates of biomass from those PD scores. LTRM vegetation crews have recorded trace scores in annual sampling since 2019 as extra information (i.e. which does not change the LTRM data stream as 0.08 and +1 scores can still be combined for PD=1). Trace data are not currently available to outside users through the LTRM data browser but are available from vegetation component personnel upon request.
","language":"English","publisher":"U.S. Army Corps of Engineers, Upper Mississippi River Restoration Program","usgsCitation":"Drake, D.C., Lund, E., and Bales, K., 2021, Evaluation of a “trace” plant density score in LTRM vegetation monitoring: Long Term Resource Monitoring Technical Report LTRM-2018BI03a, 32 p.","productDescription":"32 p.","ipdsId":"IP-106633","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":391320,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":391319,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://umesc.usgs.gov/documents/publications/2021/drake_a_2021.html"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Lowenberg, Carol 0000-0002-2961-6808","orcid":"https://orcid.org/0000-0002-2961-6808","contributorId":221012,"corporation":false,"usgs":true,"family":"Lowenberg","given":"Carol","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":825693,"contributorType":{"id":2,"text":"Editors"},"rank":0}],"authors":[{"text":"Drake, Deanne C.","contributorId":207846,"corporation":false,"usgs":false,"family":"Drake","given":"Deanne","email":"","middleInitial":"C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":825690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lund, Eric","contributorId":221777,"corporation":false,"usgs":false,"family":"Lund","given":"Eric","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":826584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bales, Kyle","contributorId":267952,"corporation":false,"usgs":false,"family":"Bales","given":"Kyle","affiliations":[{"id":24495,"text":"Iowa Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":825692,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225695,"text":"70225695 - 2021 - Lagged wetland CH4 flux response in a historically wet year","interactions":[],"lastModifiedDate":"2021-11-03T12:52:20.561946","indexId":"70225695","displayToPublicDate":"2021-10-25T07:51:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Lagged wetland CH4 flux response in a historically wet year","docAbstract":"While a stimulating effect of plant primary productivity on soil carbon dioxide (CO2) emissions has been well documented, links between gross primary productivity (GPP) and wetland methane (CH4) emissions are less well investigated. Determination of the influence of primary productivity on wetland CH4 emissions (FCH4) is complicated by confounding influences of water table level and temperature on CH4 production, which also vary seasonally. Here, we evaluate the link between preceding GPP and subsequent FCH4 at two fens in Wisconsin using eddy covariance flux towers, Lost Creek (US-Los) and Allequash Creek (US-ALQ). Both wetlands are mosaics of forested and shrub wetlands, with US-Los being larger in scale and having a more open canopy. Co-located sites with multi-year observations of flux, hydrology, and meteorology provide an opportunity to measure and compare lag effects on FCH4 without interference due to differing climate. Daily average FCH4 from US-Los reached a maximum of 47.7 ηmol CH4 m−2 s−1 during the study period, while US-ALQ was more than double at 117.9 ηmol CH4 m−2 s−1. The lagged influence of GPP on temperature-normalized FCH4 (Tair-FCH4) was weaker and more delayed in a year with anomalously high precipitation than a following drier year at both sites. FCH4 at US-ALQ was lower coincident with higher stream discharge in the wet year (2019), potentially due to soil gas flushing during high precipitation events and lower water temperatures. Better understanding of the lagged influence of GPP on FCH4 due to this study has implications for climate modeling and more accurate carbon budgeting.
NASA's Voyager 1, Voyager 2, and Galileo spacecraft acquired hundreds of images of Jupiter's moon Europa. These images provide the only moderate- to high-resolution views of the moon's surface and are therefore a critical resource for scientific analysis and future mission planning. Unfortunately, uncertain knowledge of the spacecraft's position and pointing during image acquisition resulted in significant errors in the location of the images on the surface. The result is that adjacent images are poorly aligned, with some images displaced by more than 100 km from their correct location. These errors severely degrade the usability of the Voyager and Galileo imaging data sets. To improve the usability of these data sets, we used the U.S. Geological Survey Integrated Software for Imagers and Spectrometers to build a nearly global image tie-point network with more than 50,000 tie points and 135,000 image measurements on 481 Galileo and 221 Voyager images. A global least-squares bundle adjustment of our final Europa tie-point network calculated latitude, longitude, and radius values for each point by minimizing residuals globally, and resulted in root mean square (RMS) uncertainties of 246.6 m, 307.0 m, and 70.5 m in latitude, longitude, and radius, respectively. The total RMS uncertainty was 0.32 pixels. This work enables direct use of nearly the entire Galileo and Voyager image data sets for Europa. We are providing the community with updated NASA Navigation and Ancillary Information Facility Spacecraft, Planet, Instrument, C-matrix (pointing), and Events kernels, mosaics of Galileo images acquired during each observation sequence, and individual processed and projected level 2 images.
Baseflow is critical to sustaining streamflow in the Upper Colorado River Basin. Therefore, effective water resources management requires estimates of baseflow response to climatic changes. This study provides the first estimates of projected baseflow changes from historical (1984 – 2012) to thirty-year periods centered around 2030, 2050, and 2080 under warm/wet, median, and hot/dry climatic conditions using a hybrid statistical-deterministic baseflow model. Total baseflow supplied to the Lower Colorado River Basin may decline by up to 33%, although this value may increase in the near future by 6% under warm/wet conditions. The percentage of baseflow lost during in-stream transport is projected to increase by 1 - 5% relative to historical conditions. Results highlight that climate driven changes in high elevation hydrology have impacts on basin-wide water availability. Study results have implications for human and ecological water availability in one of the most heavily managed watersheds in the world.
Timber harvesting can influence headwater streams by altering stream productivity, with cascading effects on the food web and predators within, including stream salamanders. Although studies have examined shifts in salamander occupancy or abundance following timber harvest, few examine sublethal effects such as changes in growth and demography. To examine the effect of upland harvesting on growth of the stream-associated Ouachita dusky salamander (Desmognathus brimleyorum), we used capture–mark–recapture over three years at three headwater streams embedded in intensely managed pine forests in west-central Arkansas. The pine stands surrounding two of the streams were harvested, with retention of a 14- and 21-m-wide forested stream buffer on each side of the stream, whereas the third stream was an unharvested control. At the two treatment sites, measurements of newly metamorphosed salamanders were on average 4.0 and 5.7 mm larger post-harvest compared with pre-harvest. We next assessed the influence of timber harvest on growth of post-metamorphic salamanders with a hierarchical von Bertalanffy growth model that included an effect of harvest on growth rate. Using measurements from 839 individual D. brimleyorum recaptured between 1 and 6 times (total captures, n = 1229), we found growth rates to be 40% higher post-harvest. Our study is among the first to examine responses of individual stream salamanders to timber harvesting, and we discuss mechanisms that may be responsible for observed shifts in growth. Our results suggest timber harvest that includes retention of a riparian buffer (i.e., streamside management zone) may have short-term positive effects on juvenile stream salamander growth, potentially offsetting negative sublethal effects associated with harvest.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8238","usgsCitation":"Guzy, J.C., Halstead, B., Halloran, K.M., Homyack, J.A., and Willson, J.D., 2021, Increased growth rates of stream salamanders following forest harvesting: Ecology and Evolution, v. 11, no. 24, p. 17723-17733, https://doi.org/10.1002/ece3.8238.","productDescription":"11 p.","startPage":"17723","endPage":"17733","ipdsId":"IP-127689","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450369,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.8238","text":"External Repository"},{"id":397302,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","county":"Howard County","otherGeospatial":"Ouachita Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.306640625,\n 34.384246040152185\n ],\n [\n -93.416748046875,\n 34.384246040152185\n ],\n [\n -93.416748046875,\n 35.232159412017154\n ],\n [\n -94.306640625,\n 35.232159412017154\n ],\n [\n -94.306640625,\n 34.384246040152185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"24","noUsgsAuthors":false,"publicationDate":"2021-10-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Guzy, Jacquelyn C. 0000-0003-2648-398X","orcid":"https://orcid.org/0000-0003-2648-398X","contributorId":288520,"corporation":false,"usgs":true,"family":"Guzy","given":"Jacquelyn","email":"","middleInitial":"C.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":838477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":838478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Halloran, Kelly M.","contributorId":288948,"corporation":false,"usgs":false,"family":"Halloran","given":"Kelly","email":"","middleInitial":"M.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":838479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Homyack, Jessica A.","contributorId":288949,"corporation":false,"usgs":false,"family":"Homyack","given":"Jessica","email":"","middleInitial":"A.","affiliations":[{"id":56610,"text":"Weyerhaeuser Company","active":true,"usgs":false}],"preferred":false,"id":838480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Willson, John D.","contributorId":288952,"corporation":false,"usgs":false,"family":"Willson","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":838481,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229453,"text":"70229453 - 2021 - Surface-water/groundwater boundaries affect seasonal PFAS concentrations and PFAA precursor transformations","interactions":[],"lastModifiedDate":"2022-03-09T15:47:07.062788","indexId":"70229453","displayToPublicDate":"2021-10-23T09:32:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9161,"text":"Environmental Science: Processes & Impacts","active":true,"publicationSubtype":{"id":10}},"title":"Surface-water/groundwater boundaries affect seasonal PFAS concentrations and PFAA precursor transformations","docAbstract":"Elevated concentrations of per- and polyfluoroalkyl substances (PFAS) in drinking-water supplies are a major concern for human health. It is therefore essential to understand factors that affect PFAS concentrations in surface water and groundwater and the transformation of perfluoroalkyl acid (PFAA) precursors that degrade into terminal compounds. Surface-water/groundwater exchange can occur along the flow path downgradient from PFAS point sources and biogeochemical conditions can change rapidly at these exchange boundaries. Here, we investigate the influence of surface-water/groundwater boundaries on PFAS transport and transformation. To do this, we conducted an extensive field-based analysis of PFAS concentrations in water and sediment from a flow-through lake fed by contaminated groundwater and its downgradient surface-water/groundwater boundary (defined as ≤100 cm below the lake bottom). PFAA precursors comprised 45 ± 4.6% of PFAS (PFAA precursors + 18 targeted PFAA) in the predominantly oxic lake impacted by a former fire-training area and historical wastewater discharges. In shallow porewater downgradient from the lake, this percentage decreased significantly to 25 ± 11%. PFAA precursor concentrations decreased by 85% between the lake and 84–100 cm below the lake bottom. PFAA concentrations increased significantly within the surface-water/groundwater boundary and in downgradient groundwater during the winter months despite lower stable concentrations in the lake water source. These results suggest that natural biogeochemical fluctuations associated with surface-water/groundwater boundaries may lead to PFAA precursor loss and seasonal variations in PFAA concentrations. Results of this work highlight the importance of dynamic biogeochemical conditions along the hydrological flow path from PFAS point sources to potentially affected drinking water supplies.","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D1EM00329A","usgsCitation":"Tokranov, A.K., LeBlanc, D.R., Pickard, H.M., Ruyle, B.J., Barber, L., Hull, R.B., Sunderland, E.M., and Vecitis, C.D., 2021, Surface-water/groundwater boundaries affect seasonal PFAS concentrations and PFAA precursor transformations: Environmental Science: Processes & Impacts, v. 23, no. 12, p. 1893-1905, https://doi.org/10.1039/D1EM00329A.","productDescription":"13 p.","startPage":"1893","endPage":"1905","ipdsId":"IP-111866","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":450371,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1039/d1em00329a","text":"Publisher Index Page"},{"id":436134,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HPBFRT","text":"USGS data release","linkHelpText":"Concentrations of per- and polyfluoroalkyl substances (PFAS) and related chemical and physical data at and near surface-water/groundwater boundaries on Cape Cod, Massachusetts, 2016-19"},{"id":396919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Ashumet Pond, Cape Cod","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.55196762084961,\n 41.58412041539796\n ],\n [\n -70.48055648803711,\n 41.58412041539796\n ],\n [\n -70.48055648803711,\n 41.64867312729944\n ],\n [\n -70.55196762084961,\n 41.64867312729944\n ],\n [\n -70.55196762084961,\n 41.58412041539796\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tokranov, Andrea K. 0000-0003-4811-8641","orcid":"https://orcid.org/0000-0003-4811-8641","contributorId":255483,"corporation":false,"usgs":true,"family":"Tokranov","given":"Andrea","email":"","middleInitial":"K.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LeBlanc, Denis R. 0000-0002-4646-2628","orcid":"https://orcid.org/0000-0002-4646-2628","contributorId":219907,"corporation":false,"usgs":true,"family":"LeBlanc","given":"Denis","email":"","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":837522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pickard, Heidi M. 0000-0001-8312-7522","orcid":"https://orcid.org/0000-0001-8312-7522","contributorId":261821,"corporation":false,"usgs":false,"family":"Pickard","given":"Heidi","email":"","middleInitial":"M.","affiliations":[{"id":53027,"text":"Harvard John A. Paulson School of Engineering and Applied Sciences","active":true,"usgs":false}],"preferred":false,"id":837523,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruyle, Bridger J. 0000-0003-1941-4732","orcid":"https://orcid.org/0000-0003-1941-4732","contributorId":261820,"corporation":false,"usgs":false,"family":"Ruyle","given":"Bridger","email":"","middleInitial":"J.","affiliations":[{"id":53027,"text":"Harvard John A. Paulson School of Engineering and Applied Sciences","active":true,"usgs":false}],"preferred":false,"id":837524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barber, Larry B. 0000-0002-0561-0831","orcid":"https://orcid.org/0000-0002-0561-0831","contributorId":218953,"corporation":false,"usgs":true,"family":"Barber","given":"Larry B.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":837525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hull, Robert B.","contributorId":193841,"corporation":false,"usgs":false,"family":"Hull","given":"Robert","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":837526,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sunderland, Elsie M.","contributorId":151016,"corporation":false,"usgs":false,"family":"Sunderland","given":"Elsie","email":"","middleInitial":"M.","affiliations":[{"id":18166,"text":"Harvard University, Cambridge, M","active":true,"usgs":false}],"preferred":false,"id":837527,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vecitis, Chad D.","contributorId":193842,"corporation":false,"usgs":false,"family":"Vecitis","given":"Chad","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":837528,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70225585,"text":"70225585 - 2021 - Evaluation of satellite imagery for monitoring Pacific walruses at a large coastal haulout","interactions":[],"lastModifiedDate":"2021-10-26T14:15:05.326145","indexId":"70225585","displayToPublicDate":"2021-10-23T09:13:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of satellite imagery for monitoring Pacific walruses at a large coastal haulout","docAbstract":"Pacific walruses (Odobenus rosmarus divergens) are using coastal haulouts in the Chukchi Sea more often and in larger numbers to rest between foraging bouts in late summer and autumn in recent years, because climate warming has reduced availability of sea ice that historically had provided resting platforms near their preferred benthic feeding grounds. With greater numbers of walruses hauling out in large aggregations, new opportunities are presented for monitoring the population. Here we evaluate different types of satellite imagery for detecting and delineating the peripheries of walrus aggregations at a commonly used haulout near Point Lay, Alaska, in 2018–2020. We evaluated optical and radar imagery ranging in pixel resolutions from 40 m to ~1 m: specifically, optical imagery from Landsat, Sentinel-2, Planet Labs, and DigitalGlobe, and synthetic aperture radar (SAR) imagery from Sentinel-1 and TerraSAR-X. Three observers independently examined satellite images to detect walrus aggregations and digitized their peripheries using visual interpretation. We compared interpretations between observers and to high-resolution (~2 cm) ortho-corrected imagery collected by a small unoccupied aerial system (UAS). Roughly two-thirds of the time, clouds precluded clear optical views of the study area from satellite. SAR was unaffected by clouds (and darkness) and provided unambiguous signatures of walrus aggregations at the Point Lay haulout. Among imagery types with 4–10 m resolution, observers unanimously agreed on all detections of walruses, and attained an average 65% overlap (sd 12.0, n 100) in their delineations of aggregation boundaries. For imagery with ~1 m resolution, overlap agreement was higher (mean 85%, sd 3.0, n 11). We found that optical satellite sensors with moderate resolution and high revisitation rates, such as PlanetScope and Sentinel-2, demonstrated robust and repeatable qualities for monitoring walrus haulouts, but temporal gaps between observations due to clouds were common. SAR imagery also demonstrated robust capabilities for monitoring the Point Lay haulout, but more research is needed to evaluate SAR at haulouts with more complex local terrain and beach substrates.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13214266","usgsCitation":"Fischbach, A., and Douglas, D.C., 2021, Evaluation of satellite imagery for monitoring Pacific walruses at a large coastal haulout: Remote Sensing, v. 13, no. 21, 4266, 19 p., https://doi.org/10.3390/rs13214266.","productDescription":"4266, 19 p.","ipdsId":"IP-131033","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":450373,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13214266","text":"Publisher Index Page"},{"id":436135,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S2UL7N","text":"USGS data release","linkHelpText":"Walrus Haulout Outlines Apparent from Satellite Imagery Near Point Lay Alaska, Autumn 2018-2020"},{"id":390960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Point Lay haulout area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.19366455078125,\n 69.33674271476097\n ],\n [\n -162.9986572265625,\n 69.6121624754292\n ],\n [\n -162.542724609375,\n 69.96796725849453\n ],\n [\n -162.48504638671875,\n 70.00368818988092\n ],\n [\n -162.73223876953122,\n 70.03372158435194\n ],\n [\n -163.289794921875,\n 69.70286804851057\n ],\n [\n -163.36669921875,\n 69.47778343567616\n ],\n [\n -163.3447265625,\n 69.337711892853\n ],\n [\n -163.19366455078125,\n 69.33674271476097\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"21","noUsgsAuthors":false,"publicationDate":"2021-10-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":200780,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony S.","email":"afischbach@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":825688,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":825689,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225561,"text":"ofr20211101 - 2021 - Detection and measurement of land-surface deformation, Pajaro Valley, Santa Cruz and Monterey counties, California, 2015–18","interactions":[],"lastModifiedDate":"2021-10-26T13:24:52.319349","indexId":"ofr20211101","displayToPublicDate":"2021-10-22T17:20:13","publicationYear":"2021","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":"2021-1101","displayTitle":"Detection and Measurement of Land-Surface Deformation, Pajaro Valley, Santa Cruz and Monterey Counties, California, 2015–18","title":"Detection and measurement of land-surface deformation, Pajaro Valley, Santa Cruz and Monterey counties, California, 2015–18","docAbstract":"Land-surface deformation (subsidence) caused by groundwater withdrawal is identified as an undesirable result in the Pajaro Valley Water Management Agency’s Basin Management Plan and California’s Sustainable Groundwater Management Act. In Pajaro Valley, groundwater provides nearly 90 percent of the total water supply. To aid the development of sustainable groundwater management criteria, the U.S. Geological Survey, in cooperation with the Pajaro Valley Water Management Agency, performed an analysis of land-surface deformation (subsidence and uplift) in Pajaro Valley for 2015–18, using Interferometric Synthetic Aperture Radar and continuous Global Positioning System methods. Land-surface deformation results were then compared with subsurface geology and groundwater altitudes to better understand the hydromechanical response of the coastal aquifer system. The results indicate the land surface is generally stable with only small magnitudes (less than 1 inch) of seasonal land-surface deformation (subsidence in the summer and uplift in the winter) during 2015–18. During this time, the largest magnitude of land-surface deformation was less than 2 inches of subsidence and was localized in one area just north of the city limits of Watsonville, California. Groundwater altitudes during 2015–18 demonstrated seasonal variability and annual to multi-annual increases after reaching historical lows by the mid-1990s. The small magnitudes of land-surface deformation coupled with groundwater-altitude increases in most areas indicate that the subsidence likely is largely elastic and recoverable. The Corralitos-Pajaro Valley groundwater basin contains fine-grained (clay) sediments that have the potential for permanent aquifer-system compaction and resultant land subsidence. However, groundwater altitudes throughout the Pajaro Valley have increased above historical lows, and observed increases in groundwater altitudes coincided with changes in groundwater management activities. Observed relations between groundwater management activities and groundwater altitudes indicate that management of groundwater supplies could minimize the potential for permanent land-surface deformation in Pajaro Valley.
","language":"English","publisher":"U.S. Geological","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211101","collaboration":"Prepared in cooperation with the Pajaro Valley Water Management Agency","programNote":"Water Availability and Use Science Program","usgsCitation":"Brandt, J.T., Earll, M.M., Sneed, M., and Henson, W., 2021, Detection and measurement of land-surface deformation, Pajaro Valley, Santa Cruz and Monterey counties, California, 2015–18: U.S. Geological Survey Open-File Report 2021–1101, 16 p., https://doi.org/10.3133/ofr20211101.","productDescription":"Report: vi, 16 p.; Data Release","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-118756","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":390883,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FNARQO","linkHelpText":"Interferometric Synthetic Aperture Radar and Water Level Data, Pajaro Valley, Santa Cruz and Monterey Counties, California, 1970–2018"},{"id":390882,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1101/images"},{"id":390881,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1101/ofr20211101.xml"},{"id":390880,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1101/ofr20211101.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390879,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1101/covrthb.jpg"}],"country":"United States","state":"California","county":"Monterey County, Santa Cruz County","otherGeospatial":"Pajaro Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.89468383789061,\n 36.71356812817935\n ],\n [\n -121.57058715820312,\n 36.71356812817935\n ],\n [\n -121.57058715820312,\n 36.97074107796435\n ],\n [\n -121.89468383789061,\n 36.97074107796435\n ],\n [\n -121.89468383789061,\n 36.71356812817935\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The HayWired earthquake scenario, led by the U.S. Geological Survey (USGS), anticipates the impacts of a hypothetical moment magnitude 7.0 earthquake on the Hayward Fault. The fault runs along the east side of California’s San Francisco Bay and is among the most active and dangerous in the United States, passing through a densely urbanized and interconnected region. A scientifically realistic scenario is one way to learn from a large earthquake before one occurs in the bay region. The USGS and its partners in the HayWired Coalition are working to energize residents and businesses to engage in new and ongoing efforts to prepare the region for such a future earthquake.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213054","usgsCitation":"Wein, A.M., Jones, J.L., Johnson, L.A., Kroll, C., Strauss, J., Witkowski, D., Cox, D.A., 2021, The HayWired Earthquake Scenario—Societal Consequences: U.S. Geological Survey Fact Sheet 2021–3054, 6 p., https://doi.org/10.3133/fs20213054.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-132490","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":553,"text":"Science Application for Risk Reduction (SAFRR)","active":false,"usgs":true}],"links":[{"id":390874,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20183016","text":"Fact Sheet 2018-3016","linkHelpText":"– The HayWired Earthquake Scenario—We Can Outsmart Disaster"},{"id":390873,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20175013V3","text":"Scientific Investigations Report 2017-5013 Volume 3","linkHelpText":"– The HayWired Earthquake Scenario—Societal Consequences"},{"id":390872,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20175013v2","text":"Scientific Investigations Report 2017-5013 Volume 2","linkHelpText":"– The HayWired Earthquake Scenario—Engineering Implications"},{"id":390871,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20175013v1","text":"Scientific Investigations Report 2017-5013 Volume 1","linkHelpText":"– The HayWired Earthquake Scenario—Earthquake Hazards"},{"id":390870,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3054/fs20213054.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3054/covrthb.jpg"},{"id":395080,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://geonarrative.usgs.gov/liquefactionandsealevelrise/","text":"Liquefaction and Sea-Level Rise","linkHelpText":"– A USGS storymap presenting the impacts of sea-level rise on liquefaction severity around the San Francisco Bay Area, California for the M7.0 ‘HayWired’ earthquake scenario along the Hayward Fault"},{"id":392899,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20175013","text":"Scientific Investigations Report 2017-5013","linkHelpText":"– The HayWired Earthquake Scenario"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.0908203125,\n 37.24782120155428\n ],\n [\n -121.26708984374999,\n 37.24782120155428\n ],\n [\n -121.26708984374999,\n 38.37611542403604\n ],\n [\n -123.0908203125,\n 38.37611542403604\n ],\n [\n -123.0908203125,\n 37.24782120155428\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Menlo Park, Calif.
Office—Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
In the deserts of the Southwestern United States, increased off-highway vehicle use can lead to widespread vehicular damage to desert ecosystems. As the popularity and intensity of vehicle use on public lands continues, the Bureau of Land Management (BLM) is challenged to manage the routes used by recreationists while minimizing activity beyond designated routes and mitigating environmental impacts. Ecosystem function and habitat quality can be degraded by vehicle activities, especially when the activities are occurring outside authorized routes or authorized open areas. Restoration mitigates damage to soils and vegetation; however, methods vary across the desert, results appear to be inconsistent, and standardized monitoring plans do not exist. The Desert Renewable Energy Conservation Plan Land Use Plan Amendment to the California Desert Conservation Area Land Use Plan identified the need for, and directed implementation of, standardized monitoring of restoration, which includes minimizing surface disturbance to agency prescribed levels in areas of critical environmental concern and on California Desert National Conservation Lands. To assist the BLM in implementing the Desert Renewable Energy Conservation Plan Land Use Plan Amendment, we define ecological restoration as the process of halting or minimizing future degradation while simultaneously assisting the recovery of ecosystem function and community composition in relation to intact reference sites. The monitoring strategies provided in this protocol are used to restore degraded ecosystems after use of non-routes has ceased (non-designated routes or vehicle-caused linear disturbances) by applying techniques to improve edaphic properties, hydrologic function, and biotic community composition. This protocol also provides criteria that can be used to distinguish the status of non-routes and land parcels as “restored” or “disturbed.” This protocol was developed by the U.S. Geological Survey, in collaboration with BLM restoration practitioners, to identify standard restoration methods and establish criteria to determine when restoration is achieved. This protocol also develops new methods to increase restoration rates and successes on public lands in the southern California deserts. BLM’s long-term implementation plan for the evaluation of road restoration described in this report is to transition toward managing the work, including developing the workforce and long-term storage and management of the data during the next several years. This report is intended to be regularly updated as the program develops.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2A17","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Esque, T.C., Jackson, K.R., Rice, A.M., Childers, J.K., Woods, C.S., Fesnock-Parker, A., Johnson, A.C., Price, L.J., Forgrave, K.E., Scoles-Sciulla, S.J., and DeFalco, L.A., 2021, Protocol for route restoration in California’s desert renewable energy conservation plan area: U.S. Geological Survey Techniques and Methods 2-A17, 60 p., https://doi.org/10.3133/tm2A17.","productDescription":"viii, 60 p.","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126835","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":390844,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/a17/covrthb.jpg"},{"id":390845,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/a17/tm2a17.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390846,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/02/a17/tm2a17.xml"},{"id":390847,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/02/a17/images"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.09204101562501,\n 35.137879119634185\n ],\n [\n -116.03759765625,\n 35.137879119634185\n ],\n [\n -116.03759765625,\n 36.4566360115962\n ],\n [\n -118.09204101562501,\n 36.4566360115962\n ],\n [\n -118.09204101562501,\n 35.137879119634185\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819