In large freshwater systems, the dominant production pathways supporting food webs are often spatiotemporally variable. We used stable isotope analysis and analysis of covariance (ANCOVA) models to investigate spatial and interannual variation in the dominant production pathways supporting fish consumers within the central basin of Lake Erie. We examined C and N stable isotope ratios of zooplankton, benthic invertebrates, and four species of fish common to nearshore areas of the central basin (yellow perch, Perca flavescens; white perch, Morone americana; rainbow smelt, Osmerus mordax; and round goby, Neogobius melanostomus) using tissue samples collected in 2017 and 2019. δ 13C values varied by location consistent with expected baseline differences in nutrient loading (13C was more enriched in the southern region) in two of six ANCOVA models. Furthermore, δ 15N values varied with individual fish size and by location in a manner consistent with spatial patterns of nutrient loading from surrounding agricultural landscapes (15N was more enriched in the northern region) and a longitudinal gradient of eutrophication, decreasing from west to east. These patterns were not exhibited by all species and did not necessarily persist across years, suggesting that additional factors (e.g., regional diet differences, river plume dynamics) also contributed to observed δ 13C and δ 15N variation. We suggest that spatiotemporal variation of stable isotope ratios should be accounted for in studies of trophic basis of production and food web structure in Lake Erie.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2023.07.006","usgsCitation":"Tellier, J., Höök, T., Kraus, R., and Collingsworth, P., 2023, Spatially and temporally variable production pathways support the Lake Erie central basin food web: Journal of Great Lakes Research, v. 49, no. 5, p. 1137-1149, https://doi.org/10.1016/j.jglr.2023.07.006.","productDescription":"13 p.","startPage":"1137","endPage":"1149","ipdsId":"IP-144710","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":488148,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1016/j.jglr.2023.07.006","text":"Publisher Index Page"},{"id":485518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"central Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.89113891431519,\n 42.642087429833964\n ],\n [\n -81.54918796649198,\n 42.5518853383298\n ],\n [\n -82.00732338256428,\n 42.28154650023495\n ],\n [\n -82.41825696788968,\n 42.08990823363946\n ],\n [\n -82.65704269990312,\n 41.35456309813142\n ],\n [\n -81.71855924152474,\n 41.51441245425303\n ],\n [\n -80.95777679301685,\n 41.82391066009919\n ],\n [\n -80.89113891431519,\n 42.642087429833964\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Tellier, Joshua M.","contributorId":354641,"corporation":false,"usgs":false,"family":"Tellier","given":"Joshua M.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":936058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Höök, Tomas O.","contributorId":354642,"corporation":false,"usgs":false,"family":"Höök","given":"Tomas O.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":936059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936060,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collingsworth, Paris D.","contributorId":354643,"corporation":false,"usgs":false,"family":"Collingsworth","given":"Paris D.","affiliations":[{"id":84645,"text":"Illinois-Indiana SeaGrant","active":true,"usgs":false}],"preferred":false,"id":936061,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252124,"text":"70252124 - 2023 - Intra-lake trends and inter-lake comparisons of Mysis diluviana life history variables and their relationships to food limitation","interactions":[],"lastModifiedDate":"2024-03-15T14:54:24.698932","indexId":"70252124","displayToPublicDate":"2023-09-30T09:47:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Intra-lake trends and inter-lake comparisons of Mysis diluviana life history variables and their relationships to food limitation","title":"Intra-lake trends and inter-lake comparisons of Mysis diluviana life history variables and their relationships to food limitation","docAbstract":"The opossum shrimp, Mysis diluviana, is an important member of the offshore food webs of the Laurentian Great Lakes, but its response to ecosystem changes that have occurred over the past several decades is not well understood. We combined the data of four long-term sampling programs, adding several years of data (post and prior) to previously published analyses to offer a longer-term, cross-basin analysis of M. diluviana populations in the Great Lakes from 1997 to 2019. Densities were high in lakes Superior and Ontario (summer values 100–300/m2), high and variable but declining (from 200–300/m2 in 1997–2004 to less than 100/m2 in 2017–2019) in Lake Michigan, low (∼20–50/m2 since 2005) in Lake Huron, and very low in shallower eastern Lake Erie (<1/m2). Biomass showed similar trends. Life history parameters (mortality, fecundity, and growth) were consistently highest in eastern Lake Erie, followed by lakes Ontario, Michigan, Huron, and Superior. Generation time was 1 year in Lake Erie and 2 years in the other lakes. Cross-basin relationships between annual M. diluviana areal densities and food indices (chlorophyll-a concentration and zooplankton biomass) were non-linear, increasing with food levels up to about 250 mysids/m2 and about 650 mg dry wt/m2. Annual growth rates were also positively correlated to both food indices in the four deep lakes, but fecundity and mortality rates were not. Our results suggest food availability is a primary factor predicting M. diluviana density and biomass. Density-dependent mortality and fish predation could explain some of the inter-lake differences, but these relationships could benefit from further investigations.
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]\n}","volume":"49","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Holda, Toby J.","contributorId":189287,"corporation":false,"usgs":false,"family":"Holda","given":"Toby J.","affiliations":[],"preferred":false,"id":896682,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watkins, J.M.","contributorId":334840,"corporation":false,"usgs":false,"family":"Watkins","given":"J.M.","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":896683,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scofield, Anne E.","contributorId":270329,"corporation":false,"usgs":false,"family":"Scofield","given":"Anne E.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":896684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pothoven, Stephen","contributorId":334841,"corporation":false,"usgs":false,"family":"Pothoven","given":"Stephen","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":896685,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Warner, David 0000-0003-4939-5368","orcid":"https://orcid.org/0000-0003-4939-5368","contributorId":217346,"corporation":false,"usgs":true,"family":"Warner","given":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":896686,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Brien, Timothy P. 0000-0003-4502-5204 tiobrien@usgs.gov","orcid":"https://orcid.org/0000-0003-4502-5204","contributorId":2662,"corporation":false,"usgs":true,"family":"O’Brien","given":"Timothy","email":"tiobrien@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":896687,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bowen, Kelly L.","contributorId":38382,"corporation":false,"usgs":false,"family":"Bowen","given":"Kelly","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":896688,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Currie, Warren J.S.","contributorId":334845,"corporation":false,"usgs":false,"family":"Currie","given":"Warren","email":"","middleInitial":"J.S.","affiliations":[{"id":34798,"text":"DFO Canada","active":true,"usgs":false}],"preferred":false,"id":896689,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jude, David J.","contributorId":334847,"corporation":false,"usgs":false,"family":"Jude","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":896690,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Boynton, Patrick","contributorId":334848,"corporation":false,"usgs":false,"family":"Boynton","given":"Patrick","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":896691,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rudstam, Lars G. 0000-0002-3732-6368","orcid":"https://orcid.org/0000-0002-3732-6368","contributorId":213508,"corporation":false,"usgs":false,"family":"Rudstam","given":"Lars","email":"","middleInitial":"G.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":896692,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70260095,"text":"70260095 - 2023 - Petrology and geochronology of Cretaceous–Eocene plutonic rocks in northeastern Washington, USA: Crustal thickening, slab rollback, and origin of the Challis episode","interactions":[],"lastModifiedDate":"2024-10-28T12:02:39.587199","indexId":"70260095","displayToPublicDate":"2023-09-30T07:00:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Petrology and geochronology of Cretaceous–Eocene plutonic rocks in northeastern Washington, USA: Crustal thickening, slab rollback, and origin of the Challis episode","docAbstract":"Cretaceous through Eocene plutonic rocks in northeastern Washington, USA, document a 60 m.y. history of crustal thickening and subsequent collapse and extension in response to two terrane-accretion events. Rocks emplaced 113–53 Ma have increasing La/Yb ratios reflecting orogenic plateau development after arrival of the Insular terrane by 100 Ma. Plutons emplaced 52–45 Ma (the Challis episode) document collapse of this plateau and define a SW-younging age progression attributed to breakoff and rollback of the Farallon slab following accretion of the Siletzia terrane at ca. 50 Ma. All of the rocks have chemical traits of arc magmas, likely inherited from their lower-crustal sources, but low B/Be ratios and the lack of evidence for amphibole fractionation indicate the Eocene magmas formed under drier conditions than are typical of active subduction settings. These magmas also originated at greater depth (eclogitic vs. gabbroic source) and were emplaced more shallowly than the earlier ones. All rocks have overlapping Sr-Nd and O isotopic data, indicating significant contributions from older continental crust, and depleted mantle Nd model ages become older toward the east, defining three regions that correspond with previously inferred lower-crustal domains. Farallon slab rollback also drove extension (core complex formation, dike swarms) and crustal uplift, which, along with voluminous magmatism, define the Challis episode. This tectonic model is further supported by seismic tomography, which has identified remnants of a detached slab in the upper mantle beneath the region.
Most of the population of the Treasure Valley and the surrounding area of southwestern Idaho and easternmost Oregon depends on groundwater for domestic supply, either from domestic or municipal-supply wells. Current and projected rapid population growth in the area has caused concern about the long-term sustainability of the groundwater resource. In 2016, the U.S. Geological Survey, in cooperation with the Idaho Water Resource Board and the Idaho Department of Water Resources, began a project to construct a numerical groundwater-flow model of the westernmost portion of the western Snake River Plain aquifer system, called the Treasure Valley.
The development of the model was guided by several objectives, including:
The model was constructed with a spatial scale and level of detail that aimed to meet these objectives while balancing the sometimes-competing goals of fast runtimes, numerical stability, usability, and parsimony.
The Treasure Valley Groundwater Flow Model (TVGWFM) is a three-dimensional finite-difference numerical model constructed using MODFLOW 6 (Langevin and others, 2017, Documentation for the MODFLOW 6 Groundwater Flow Model: U.S. Geological Survey Techniques and Methods, book 6, chap. A55, 197 p., https://doi.org/10.3133/tm6A55). The model covers the westernmost portion of the western Snake River Plain and is discretized into a regular grid of 64 by 65 cells with a side length of 1 mile and 6 layers of varying depth and active area. A historical model period was developed consisting of 360 month-long stress periods for 1986–2015. The model builds upon previous modeling efforts by adding a transient period, incorporating new head and discharge observations to constrain parameters, incorporating information from the hydrogeologic framework model (HFM) of Bartolino (2019, Hydrogeologic framework of the Treasure Valley and surrounding area, Idaho and Oregon: U.S. Geological Survey Scientific Investigations Report 2019–5138, https://doi.org/10.3133/sir20195138) and incorporating refined estimates of evapotranspiration and irrigation classification of lands in the study area.
The TVGWFM includes all significant components of recharge to and discharge from the aquifer. Inflows include canal seepage, irrigation and precipitation recharge, mountain-front recharge, rivers and stream seepage, and seepage from Lake Lowell. Outflows include discharge to agricultural drainage ditches, discharge to rivers and streams, pumping, and discharge to Lake Lowell. Each recharge or discharge component is represented separately using individual MODFLOW 6 packages.
Parameter values were derived with a combination of trial-and-error steps and automated parameter estimation using PEST software (Doherty, J.E., 2005, PEST, model-independent parameter estimation–User manual: Watermark Numerical Computing, https://pesthomepage.org/documentation). Parameter estimates were constrained with several types of observation data, including water levels, water level changes, vertical water level differences, drain discharges, change in drain discharges, river seepage, seepage from Lake Lowell, and change in seepage from Lake Lowell. Material properties from the hydrogeologic framework were also used to assign the minimum and maximum values of some parameters.
A final parameter realization was reached that minimized residuals between the observed and modelled values for the various observation groups. Mean residuals for the observation groups were 15.4 feet (ft) for water levels, 0.2 ft for water level changes, 19.4 ft for vertical water level differences, −3.9 cubic feet per second (ft3/s) for drain discharges, 0.0 ft3/s for changes in drain discharge, 45.0 ft3/s for river seepage, −40.1 ft3/s for Lake Lowell seepage, and 126.3 ft3/s for changes in Lake Lowell seepage. The quality of the model’s fit to observations varied spatially, with notable areas of under- or over-simulation of water levels present to the northwest and southwest of Lake Lowell, in the foothills along the eastern model boundary, and near the City of Eagle. Trends were observed in the residuals of many of the observation groups, indicating that the model is missing or not fully reproducing some phenomena that are observed in the system.
The TVGWFM can be used as a tool for water resource planning, for understanding the interactions of groundwater and surface water at a basin scale, and for facilitating conjunctive management, but may lack the precision needed for water rights administration at a local scale. Additional sources of uncertainty or limitations of the model are noted. The quantity and spatial distribution of canal seepage and infiltration of irrigation water recharge, the largest sources of recharge to the system, are unknown and approximated indirectly. There is poor understanding of how canal seepage and incidental recharge change as land is converted from agricultural (irrigated) to suburban (semi-irrigated). These uncertainties will affect any scenarios that investigate changes to land use or irrigation practices. Finally, the model has relatively high water-level residuals around and to the southwest of Lake Lowell and should not be used to estimate water level effects in that region.
The model was built with multiple, broadly expressed objectives and did not optimize performance for specific uses. However, the model and the tools included in an associated data release provide ample flexibility to improve the model for future uses. Adjustments and improvements could be made by refining the model in an area of interest, collecting additional calibration data, applying more rigorous boundary conditions, or re-estimating model parameters to optimize model performance for a specific model forecast.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235096","collaboration":"Prepared in cooperation with the Idaho Water Resource Board and the Idaho Department of Water Resources","usgsCitation":"Hundt, S.A., and Bartolino, J.R., 2023, Groundwater-flow model of the Treasure Valley, southwestern Idaho, 1986–2015: U.S. Geological Survey Scientific Investigations Report 2023–5096, 107 p., https://doi.org/10.3133/sir20235096.","productDescription":"Report: xii, 107 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-127901","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":501062,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115439.htm","linkFileType":{"id":5,"text":"html"}},{"id":421318,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5096/sir20235096.pdf","text":"Report","size":"30.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5096"},{"id":421321,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5096/images"},{"id":421317,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5096/coverthb.jpg"},{"id":421320,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U6OOPH","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and archive for a groundwater flow model of the Treasure Valley aquifer system, southwestern Idaho"},{"id":421322,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5096/sir20235096.XML"}],"country":"United States","state":"Idaho","otherGeospatial":"Treasure Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.26392993194762,\n 44.27650517719664\n ],\n [\n -117.26392993194762,\n 42.71456173603502\n ],\n [\n -115.50611743194747,\n 42.71456173603502\n ],\n [\n -115.50611743194747,\n 44.27650517719664\n ],\n [\n -117.26392993194762,\n 44.27650517719664\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
The Sparta-Memphis aquifer, present across much of eastern Arkansas, is the second most used groundwater resource in the State, with the Mississippi River Valley alluvial aquifer being the primary groundwater resource. The U.S. Geological Survey, in cooperation with Arkansas Department of Agriculture-Natural Resources Division, Arkansas Geological Survey, Natural Resources Conservation Service, Union County Water Conservation Board, and the Union County Conservation District, collects groundwater data across the Sparta-Memphis aquifer extent in Arkansas. This report presents water-level data for measurements conducted during two time periods, January–May 2013 and January–June 2015, and discusses water-level altitude changes for the 2011–13 and 2013–15 periods in the Sparta-Memphis aquifer. Accompanying water-level data in this report include groundwater-quality data for the period 2010–15 in the Sparta-Memphis aquifer. Groundwater data can guide ongoing and future groundwater-monitoring efforts and inform management of the aquifers in Arkansas.
Water levels measured at 306 wells from January to May 2013 and 273 wells from January to June 2015 are graphically presented as potentiometric-surface maps. Measurements from 2011, 2013, and 2015 were used in the construction of 2011–13 and 2013–15 water-level change maps. Select long-term hydrographs are included in the report to illustrate water-level changes at the local scale.
Water-level data show the influence of climate, pumping, and conservation and management efforts on groundwater levels. With respect to climate, the study area experienced extreme drought conditions between January 2011 and December 2012. The proximate effects of drought—increased evapotranspiration, decreased recharge, and increased irrigation needs—resulted in water-level declines that were particularly notable in the northern and central portions of the study area.
Groundwater sampled in 2010–15 from 148 wells completed in the Sparta-Memphis aquifer was analyzed for specific conductance, pH, chloride (Cl) concentration, and bromide (Br) concentration. In 2015, groundwater-quality data from 103 wells completed in the Sparta-Memphis aquifer had a median specific conductance of 356 microsiemens per centimeter at 25 degrees Celsius and a median Cl concentration of 9.5 milligrams per liter (mg/L). The data show two areas of higher Cl (greater than 10 mg/L) and higher Br (greater than 0.5 mg/L) concentrations in Union, Calhoun, and Bradley Counties in southern Arkansas and Monroe and Phillips Counties in eastern-central Arkansas. A Cl and Br mixing model indicates the two regions of wells may have different sources of higher salinity. In the greater Union County area, water in most wells may be a mixture of recharge or precipitation and higher salinity groundwater from the Nacatoch aquifer. Water in wells in eastern-central Arkansas may be sourced from aquifers having a higher Cl concentration (and thus, also a higher Cl-to-Br ratio).
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Turtles are one of the most imperiled vertebrate groups in the world. With habitat destruction unabated in many places, urban and suburban greenspaces may serve as refugia for turtles, at least those species able to tolerate heavily altered landscapes. In south-central Louisiana, we have conducted a turtle capture–mark–recapture effort in two ponds in an urban greenspace for 13 yr to understand species composition, survival, and individual growth rates. We had 574 total captures of 251 individuals of five species from 2009–2021, with Trachemys scripta elegans (Red-Eared Sliders) and Sternotherus odoratus (Eastern Musk Turtles) being the most common. Apparent annual survival for T. scripta (0.79) was similar to estimates reported in other studies in altered habitats, whereas apparent annual survival for S. odoratus (0.89) was slightly or much higher than other published studies. Growth rates of T. scripta were comparable to other studies and showed both sexes have similar rates of growth until maturity, which is earlier and at a smaller size in males. The two ponds showed marked differences in captures by size, with significantly more juvenile T. scripta captured in the pond with more vegetation, depth, and a softer bottom. Most T. scripta (78.5%) that were recaptured came from the same pond from which they were originally captured. The basic demographic data gained in this study can serve as a starting point for broader questions on urbanization effects and as a comparison to more natural populations.
The Equus Beds aquifer and Cheney Reservoir are primary sources for the city of Wichita’s current (2023) water supply. The Equus Beds aquifer storage and recovery (ASR) project was developed by the city of Wichita in the early 1990s to meet future water demands using the Little Arkansas River as an artificial aquifer recharge water source during above-base-flow conditions. Little Arkansas River water is removed from the river at an ASR Facility intake structure, treated using National Primary Drinking Water Regulations as a guideline, and is infiltrated into the Equus Beds aquifer through recharge basins or injected into the aquifer through recharge wells for later use. The U.S. Geological Survey, in cooperation with the city of Wichita, completed this study to quantify and characterize Little Arkansas River water-quality data. Data in this report can be used to evaluate changing conditions, provide science-based information for decision making, and help meet regulatory requirements.
Continuous (hourly) physicochemical properties were measured, and discrete water-quality samples were collected from three Little Arkansas River sites located along the easternmost extent of the Equus Beds aquifer during 1995 through 2021 over a range of streamflow conditions. The Little Arkansas River at Highway 50 near Halstead, Kansas, streamgage (U.S. Geological Survey station 07143672; hereafter referred to as the “Highway 50 site”) is located upstream from the other two sites, and the Little Arkansas River near Sedgwick, Kans., streamgage (U.S. Geological Survey station 07144100; hereafter referred to as the “Sedgwick site”) is located downstream from the other two sites; these two sites bracket most of the easternmost part of the Equus Beds aquifer. The Little Arkansas River upstream of ASR Facility near Sedgwick, Kans., streamgage (U.S. Geological Survey station 375350097262800; hereafter referred to as the “Upstream ASR site”) is located between the Highway 50 and Sedgwick sites, about 14.7 river miles (mi) downstream from the Highway 50 site, about 1.7 river mi upstream from the Sedgwick site, and immediately upstream from the ASR Facility intake structure. Surrogate models for water-quality constituents of interest (including bromide, dissolved organic carbon, 2-chloro-4-isopropylamino-6-amino-s-triazine [deethylatrazine], atrazine, and metolachlor) were updated or developed using continuously measured and concomitant discrete data. These surrogate models, along with previously developed regression models, were used to compute concentrations (at the Highway 50, Sedgwick, and Upstream ASR sites) and loads (at the Highway 50 and Sedgwick sites) during the study period. Federal criteria were used to evaluate water quality. Where applicable, water-quality data were compared to Federal national drinking-water regulations. Flow-normalized water-quality constituent trends were evaluated using Weighted Regressions on Time, Discharge, and Season (WRTDS) statistical models and water-quality trends were described using WRTDS bootstrap tests.
Continuously computed primary ion concentrations were generally larger at the Highway 50 site compared to the Sedgwick site. During the study period, the Federal secondary maximum contaminant level (SMCL) for dissolved solids was exceeded 57 percent of the time at the Highway 50 site and 38 percent of the time at the Sedgwick site. Computed bromide concentrations were larger at the Highway 50 site and exceeded the city of Wichita treatment threshold about 70, 21, and 19 percent of the time at the Highway 50, Sedgwick, and Upstream ASR sites, respectively. Chloride concentrations exceeded the Federal SMCL about 16 percent of the time at the Highway 50 site and did not exceed the SMCL at the Sedgwick site. Continuous arsenic concentrations exceeded the Federal Maximum Contaminant Level (MCL) 9 to 15 percent of the time at the Sedgwick and Highway 50 sites, respectively, during the study. Atrazine concentrations exceeded the Federal MCL 10 percent of the time at the Highway 50 and Sedgwick sites and 14 percent of the time at the Upstream ASR site during the study; computed glyphosate concentrations at the Sedgwick site never exceeded the MCL during the study.
Little Arkansas River flow-normalized primary ion concentrations during 1995 through 2021 generally had downward trends and decreases were generally larger at the Highway 50 site compared to the Sedgwick site. Dissolved solids and chloride concentrations decreased at the Highway 50 and Sedgwick sites. Bromide had no trend at the Highway 50 site and a downward trend at the Sedgwick site. Nitrate plus nitrite and total phosphorus concentrations had upward trends at the Highway 50 site but downward trends at the Sedgwick site, whereas total organic carbon had upward trends at both sites. Nitrate plus nitrite, total nitrogen, total phosphorus, and total organic carbon fluxes had upward trends at the Highway 50 and Sedgwick sites. Suspended-sediment concentrations had an upward trend at the Highway 50 site and had no trend at the Sedgwick site. Arsenic concentrations had downward trends at the Highway 50 and Sedgwick sites.
About one-quarter to one-half of the Little Arkansas River loads, including nutrients and sediment, were transported during 1 percent of the time during the study. Because streamflows are highly sensitive to climatic variation and an increase of extreme precipitation events in the Great Plains is expected, similar disproportionately large pollutant loading events may increase into the future. Continuous measurement of physicochemical properties in near-real time allowed characterization of Little Arkansas River surface water during conditions and time scales that would not have been possible otherwise and served as a complement to discrete water-quality sampling. Continuation of this water-quality monitoring will provide data to characterize changing conditions in the Little Arkansas River and possibly identify new and changing trends. Information in this report allows the city of Wichita to make informed municipal water-supply decisions using past and present water-quality conditions and trends in the watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235102","collaboration":"Prepared in cooperation with the city of Wichita, Kansas","usgsCitation":"Stone, M.L., and Klager, B.J., 2023, Long-term water-quality constituent trends in the Little Arkansas River, south-central Kansas, 1995–2021: U.S. Geological Survey Scientific Investigations Report 2023–5102, 103 p., https://doi.org/10.3133/sir20235102.","productDescription":"Report: ix, 103 p.; 1 Figure; 9 Tables; 5 Appendixes; Dataset","numberOfPages":"118","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-146544","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":421187,"rank":26,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_appendix10.zip","text":"Appendix 10","size":"46 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Weighted Regressions on Time, Discharge, 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Highway 50 near Halstead, Kansas; Little Arkansas River near Sedgwick, Kans.; and Little Arkansas River upstream of ASR Facility near Sedgwick, Kans., 2004–19"},{"id":421170,"rank":7,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_fig1.1.PDF","text":"Figure 1.1","size":"2.7 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Relations between turbidity sensors, 2004–19. A, YSI 6026 (YSI6026) and YSI 6136 (YSI6136) at the Little Arkansas River at Highway 50 near Halstead, Kansas"},{"id":421190,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":421169,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5102/images/"},{"id":421168,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102.XML","linkFileType":{"id":8,"text":"xml"}},{"id":501150,"rank":27,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115440.htm","linkFileType":{"id":5,"text":"html"}},{"id":421182,"rank":16,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_tables8.1-8.3.xlsx","text":"Tables 8.1–8.3","size":"112 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":421167,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102.pdf","text":"Report","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5102"},{"id":421188,"rank":20,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table11.1.xlsx","text":"Table 11.1","size":"51 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season estimated yearly water-quality constituent loads at the Little Arkansas River at Highway 50 near Halstead, Kansas and near Sedgwick, Kans., 1998–2021"},{"id":421166,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5102/coverthb.jpg"},{"id":421189,"rank":21,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table11.1.csv","text":"Table 11.1","size":"14 KB","linkFileType":{"id":7,"text":"csv"}},{"id":421201,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235102/full","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.1667,\n 38.6\n ],\n [\n -98.1667,\n 37.5\n ],\n [\n -97.25,\n 37.5\n ],\n [\n -97.25,\n 38.6\n ],\n [\n -98.1667,\n 38.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
As part of a joint Saudi Geological Survey (SGS) and United States Geological Survey project, we analyzed P-wave receiver functions from seismic stations covering most of the Kingdom of Saudi Arabia to map the thickness of the crust across the Arabia Plate. We present an update of crustal thickness estimates and fill in gaps for the western Arabian Shield and the rifted margin at the Red Sea (the coastal plain), as well as the eastern Arabian Platform. We applied a conventional H-k stacking algorithm and included careful attention to stacking weights, two forms of sedimentary corrections for stations located on the Arabian Platform, and additional processing for noisy stations. We obtained useful results at 154 stations from 898 teleseismic events over a 2-year period from 1995–1997 (for non-SGS stations) and a 6-year period from 2008–2014 (for SGS stations). Average crustal thickness (that is, depth to the Mohorovičić discontinuity [Moho] below the surface) beneath the Red Sea coastal plain (the rift margin) is 29 kilometers (km), beneath the volcanic fields (known in Arabic as harra [plural] or harrat [singular]) is 35 km, beneath the Arabian Shield (excluding harrats) is 37 km, and beneath the Arabian Platform is 38 km. Crustal thinning appears not to extend east of the rift escarpment, suggesting uniform extension that is no broader at depth than at the surface. In contrast to some previous interpretations that the Arabian Platform crust is thicker than that of the Arabian Shield, we find no statistically significant difference between their whole crustal thicknesses. However, the average sub-sedimentary crustal thickness (that is, the crystalline crust) for stations on the Arabian Platform is 34 km, 3 km thinner than the crust of the Arabian Shield. Individual station P-wave (pressure) velocity and S-wave (shear) velocity ratios (VP/VS) are highly variable for the Arabia Plate, ranging from 1.60 to 1.97 and averaging 1.75, with a standard deviation of 0.07. There are no statistically significant differences between VP/VS ratios of the different geologic regions of Saudi Arabia. Similar VP/ VS ratios, coupled with similar crustal thicknesses for harrats and the Arabian Shield, indicate that Cenozoic magmatism has contributed negligibly to crustal growth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231042","usgsCitation":"Blanchette, A.R., Klemperer, S.L., and Mooney, W.D., 2023, Crustal thickness and the VP/VS ratio within the Arabia Plate from P-wave receiver functions at 154 broadband seismic stations: U.S. Geological Survey Open-File Report 2023–1042, 325 p., https://doi.org/10.3133/ofr20231042.","productDescription":"xi, 325 p.","onlineOnly":"Y","ipdsId":"IP-136065","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":421029,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1042/coverthb.jpg"},{"id":421030,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1042/ofr20231042.pdf","text":"Report","size":"84.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1042"}],"country":"Saudi Arabia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[42.77933,16.34789],[42.64957,16.77464],[42.34799,17.07581],[42.27089,17.47472],[41.75438,17.83305],[41.22139,18.6716],[40.93934,19.48649],[40.24765,20.17463],[39.80168,20.33886],[39.1394,21.2919],[39.0237,21.98688],[39.06633,22.57966],[38.49277,23.68845],[38.02386,24.07869],[37.48363,24.28549],[37.15482,24.85848],[37.20949,25.08454],[36.93163,25.60296],[36.6396,25.82623],[36.24914,26.57014],[35.64018,27.37652],[35.13019,28.06335],[34.63234,28.05855],[34.78778,28.60743],[34.83222,28.95748],[34.95604,29.35655],[36.06894,29.19749],[36.50121,29.50525],[36.74053,29.86528],[37.50358,30.00378],[37.66812,30.33867],[37.99885,30.5085],[37.00217,31.50841],[39.00489,32.01022],[39.19547,32.16101],[40.39999,31.88999],[41.88998,31.19001],[44.7095,29.17889],[46.56871,29.09903],[47.45982,29.00252],[47.70885,28.52606],[48.41609,28.552],[48.80759,27.68963],[49.29955,27.46122],[49.47091,27.11],[50.15242,26.68966],[50.21294,26.27703],[50.1133,25.94397],[50.23986,25.60805],[50.52739,25.32781],[50.66056,24.9999],[50.81011,24.75474],[51.11242,24.55633],[51.38961,24.62739],[51.57952,24.2455],[51.61771,24.01422],[52.00073,23.00115],[55.0068,22.49695],[55.20834,22.70833],[55.66666,22],[54.99998,19.99999],[52.00001,19],[49.11667,18.61667],[48.18334,18.16667],[47.46669,17.11668],[47,16.95],[46.74999,17.28334],[46.36666,17.23332],[45.4,17.33334],[45.21665,17.43333],[44.06261,17.41036],[43.79152,17.31998],[43.38079,17.57999],[43.1158,17.08844],[43.21838,16.66689],[42.77933,16.34789]]]},\"properties\":{\"name\":\"Saudi Arabia\"}}]}","contact":"Earthquake Science Center
U.S. Geological Survey
350 N. Akron Rd.
Moffett Field, CA 94035
The Indio subbasin of the Coachella Valley is a desert area of southern California where a growing population depends primarily on groundwater for drinking and agricultural uses. The aquifer system has been supplemented with Colorado River water through managed recharge and widespread irrigation since the mid-20th century. We use a combination of geochemical modeling and trend analysis to identify changes in total dissolved solids through time, elucidate the sources of dissolved solids, and quantify the extent of contributions from those sources throughout the Indio subbasin. We conclude that recharged Colorado River water is the primary source and driver of increasing salinity, particularly in areas immediately downgradient from the recharge locations and in the eastern part of the subbasin away from the recharge ponds due to irrigation using imported water. Other contributions of dissolved solids to groundwater resources include geothermal waters, wastewater effluent, and agricultural return flow, although their effects are more localized. This study presents a broadly applicable framework for identifying sources of dissolved solids in groundwater wells and salinity trends at a regional scale in a large data set.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acsestwater.3c00239","usgsCitation":"Harkness, J.S., McCarthy, P.M., Jurgens, B., and Levy, Z., 2023, Salinity trends in a groundwater system supplemented by 50 years of imported Colorado River water: Environmental Science & Technology Water, v. 3, no. 10, p. 3253-3264, https://doi.org/10.1021/acsestwater.3c00239.","productDescription":"12 p.","startPage":"3253","endPage":"3264","ipdsId":"IP-148329","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":442051,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acsestwater.3c00239","text":"Publisher Index Page"},{"id":435173,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KUBQKM","text":"USGS data release","linkHelpText":"Inverse Model Data for: Salinity trends in a groundwater system supplemented by 50 years of imported Colorado River water"},{"id":421073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Coachella Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.13573439891272,\n 33.44388210065128\n ],\n [\n -115.83959902010358,\n 33.47539980820645\n ],\n [\n -116.10770512437531,\n 33.77373918192059\n ],\n [\n -116.49280298323828,\n 33.977110302145775\n ],\n [\n -116.5817654632921,\n 33.996309353196736\n ],\n [\n -116.645135997029,\n 33.92049840312886\n ],\n [\n -116.51108294489313,\n 33.79298404948595\n ],\n [\n -116.29781672558602,\n 33.66122225831866\n ],\n [\n -116.21372890197347,\n 33.55363612855925\n ],\n [\n -116.13573439891272,\n 33.44388210065128\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Harkness, Jennifer S. 0000-0001-9050-2570 jharkness@usgs.gov","orcid":"https://orcid.org/0000-0001-9050-2570","contributorId":224299,"corporation":false,"usgs":true,"family":"Harkness","given":"Jennifer","email":"jharkness@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883884,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCarthy, Patrick Michael 0000-0002-5492-1409","orcid":"https://orcid.org/0000-0002-5492-1409","contributorId":330033,"corporation":false,"usgs":true,"family":"McCarthy","given":"Patrick","email":"","middleInitial":"Michael","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883885,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203409,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883886,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Levy, Zeno F. 0000-0003-4580-2309","orcid":"https://orcid.org/0000-0003-4580-2309","contributorId":222340,"corporation":false,"usgs":true,"family":"Levy","given":"Zeno","middleInitial":"F.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883887,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249300,"text":"70249300 - 2023 - Future marsh evolution due to tidal changes induced by human adaptation to sea level rise","interactions":[],"lastModifiedDate":"2023-10-04T12:07:02.06102","indexId":"70249300","displayToPublicDate":"2023-09-21T07:00:45","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5053,"text":"Earth's Future","active":true,"publicationSubtype":{"id":10}},"title":"Future marsh evolution due to tidal changes induced by human adaptation to sea level rise","docAbstract":"With sea level rise threatening coastal development, decision-makers are beginning to act by modifying shorelines. Previous research has shown that hardening or softening shorelines may change the tidal range under future sea level rise. Tidal range can also be changed by natural factors. Coastal marshes, which humans increasingly depend on for shoreline protection, are ecologically sensitive to tidal range. Therefore, it is critical to examine how changes in tidal range could influence marsh processes. A marsh accretion model was used to investigate the ecological response of a San Francisco Bay, California, USA marsh to multiple tidal range scenarios and sea level rise from 2010 to 2100. The scenarios include a baseline scenario with no shoreline modifications in the estuary, a shoreline hardening scenario that amplifies the tidal range, and 14 tidal range scenarios as a sensitivity analysis that span tidal amplification and reduction of the baseline scenario. The modeling results expose key tradeoffs to consider when planning for sea level rise. Compared to the baseline, the hardening scenario shows minor differences. However, further tidal amplification prolongs marsh survival but decreases Sarcocornia pacifica cover, an important species for certain threatened wildlife and an effective attenuator of wave energy. Conversely, tidal reduction precipitates marsh drowning but shows gains in Sarcocornia pacifica cover. These mixed impacts of tidal amplification and reduction shown by the model indicate potential tradeoffs in relation to marsh survival, habitat characteristics, and shoreline protection. This study suggests the need for a cross-sectoral, regional approach to sea level rise adaptation.
Tidally influenced coastal wetlands, both saline and fresh, appear where terrestrial and marine environments meet and are considered important ecosystems for identifying the impacts of climate change. Coastal wetlands provide valuable benefits to society and the environment in the form of flood protection, water-quality improvements, and shoreline erosion reduction, making them one of the most important ecosystems in the world. Historically, these ecosystems have vertically adjusted to match rising sea levels through biologic and physical processes, but they are increasingly vulnerable to submergence as sea-level rise accelerates. Measuring vertical change on lands protected from human influence allows scientists to understand how vulnerable coastal wetlands are to submergence. But to fully understand this vulnerability, scientists must identify where vertical change in coastal wetlands is being measured across the lower 48 United States, a task that has not yet been undertaken. In this Fact Sheet, we document the spatial distribution of vertical change measurements in coastal wetlands to inform where gaps may still be in the Surface Elevation Table–Marker Horizon (SET-MH) coverage within protected lands across the lower 48 United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233039","usgsCitation":"Neville, J.A., and Gutenspergen, G.R., 2023, Spatial Distribution of Elevation Change Monitoring in Coastal Wetlands Across Protected Lands of the Lower 48 United States: U.S. Geological Survey Fact Sheet 2023–3029, 4 p., https://doi.org/10.3133/fs20233039.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-155035","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":420847,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3039/fs20233039.XML"},{"id":499693,"rank":6,"type":{"id":36,"text":"NGMDB Index 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U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Introduction: Antimicrobial resistance (AMR) is an increasing public health concern for humans, animals, and the environment. However, the contributions of spatially distributed sources of AMR in the environment are not well defined.
Methods: To identify the sources of environmental AMR, the novel microbial Find, Inform, and Test (FIT) model was applied to a panel of five antibiotic resistance-associated genes (ARGs), namely, erm(B), tet(W), qnrA, sul1, and intI1, quantified from riverbed sediment and surface water from a mixed-use region.
Results: A one standard deviation increase in the modeled contributions of elevated AMR from bovine sources or land-applied waste sources [land application of biosolids, sludge, and industrial wastewater (i.e., food processing) and domestic (i.e., municipal and septage)] was associated with 34–80% and 33–77% increases in the relative abundances of the ARGs in riverbed sediment and surface water, respectively. Sources influenced environmental AMR at overland distances of up to 13 km.
Discussion: Our study corroborates previous evidence of offsite migration of microbial pollution from bovine sources and newly suggests offsite migration from land-applied waste. With FIT, we estimated the distance-based influence range overland and downstream around sources to model the impact these sources may have on AMR at unsampled sites. This modeling supports targeted monitoring of AMR from sources for future exposure and risk mitigation efforts.
","language":"English","publisher":"Frontiers Media S.A.","doi":"10.3389/fmicb.2023.1223876","usgsCitation":"Wiesner-Friedman, C., Beattie, R.E., Stewart, J.R., Hristova, K.R., and Serre, M.L., 2023, Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework: Frontiers in Microbiology, v. 14, 1223876, 14 p., https://doi.org/10.3389/fmicb.2023.1223876.","productDescription":"1223876, 14 p.","ipdsId":"IP-149826","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":442109,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2023.1223876","text":"Publisher Index Page"},{"id":420771,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Kewaunee County","otherGeospatial":"Ahnapee River, East Twin River, Kewaunee River","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":3073,\"properties\":{\"name\":\"Kewaunee\",\"state\":\"WI\"},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-87.3761,44.6754],[-87.3774,44.674],[-87.381,44.6636],[-87.3858,44.6545],[-87.3911,44.6473],[-87.3944,44.6442],[-87.3966,44.6378],[-87.4045,44.6302],[-87.4085,44.6257],[-87.4137,44.6235],[-87.4223,44.6145],[-87.4263,44.61],[-87.4341,44.6056],[-87.442,44.6011],[-87.4428,44.5934],[-87.4468,44.5893],[-87.4502,44.5816],[-87.4544,44.5721],[-87.4604,44.5622],[-87.4664,44.555],[-87.4738,44.5455],[-87.476,44.5369],[-87.4761,44.5305],[-87.4796,44.5223],[-87.4851,44.5106],[-87.488,44.4974],[-87.4959,44.4706],[-87.5046,44.4575],[-87.5041,44.4534],[-87.5062,44.4457],[-87.5064,44.4375],[-87.5074,44.4279],[-87.5121,44.4188],[-87.5163,44.408],[-87.5191,44.3998],[-87.5212,44.3907],[-87.5209,44.3816],[-87.5218,44.3734],[-87.5232,44.3688],[-87.5279,44.3602],[-87.5351,44.3521],[-87.5386,44.3422],[-87.5368,44.338],[-87.5408,44.3331],[-87.5454,44.3277],[-87.6445,44.3273],[-87.7665,44.3271],[-87.7655,44.4146],[-87.7646,44.5017],[-87.7643,44.5888],[-87.7628,44.6477],[-87.7582,44.6522],[-87.7555,44.6558],[-87.7547,44.6608],[-87.7507,44.6667],[-87.7435,44.673],[-87.7389,44.6775],[-87.6413,44.6757],[-87.5193,44.6753],[-87.4384,44.6754],[-87.3973,44.6753],[-87.3761,44.6754]]]}}]}","volume":"14","noUsgsAuthors":false,"publicationDate":"2023-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Wiesner-Friedman, Corinne","contributorId":329682,"corporation":false,"usgs":false,"family":"Wiesner-Friedman","given":"Corinne","email":"","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":882931,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beattie, Rachelle Elaine 0000-0002-9648-4948","orcid":"https://orcid.org/0000-0002-9648-4948","contributorId":298312,"corporation":false,"usgs":true,"family":"Beattie","given":"Rachelle","email":"","middleInitial":"Elaine","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":882932,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Jill R.","contributorId":329683,"corporation":false,"usgs":false,"family":"Stewart","given":"Jill","email":"","middleInitial":"R.","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":882933,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hristova, Krassimira R.","contributorId":298313,"corporation":false,"usgs":false,"family":"Hristova","given":"Krassimira","email":"","middleInitial":"R.","affiliations":[{"id":64527,"text":"Marquette University","active":true,"usgs":false}],"preferred":false,"id":882934,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Serre, Marc L.","contributorId":329684,"corporation":false,"usgs":false,"family":"Serre","given":"Marc","email":"","middleInitial":"L.","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":882935,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70248428,"text":"ofr20231071 - 2023 - Southern (California) sea otter population status and trends at San Nicolas Island, 2020–2023","interactions":[],"lastModifiedDate":"2023-09-13T13:50:49.576331","indexId":"ofr20231071","displayToPublicDate":"2023-09-12T12:49:56","publicationYear":"2023","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":"2023-1071","displayTitle":"Southern (California) Sea Otter Population Status and Trends at San Nicolas Island, 2020–2023","title":"Southern (California) sea otter population status and trends at San Nicolas Island, 2020–2023","docAbstract":"The population of southern sea otters (Enhydra lutris nereis) at San Nicolas Island, California, has been monitored annually since the translocation of 140 southern sea otters to the island was completed in 1990. Monitoring efforts have varied in frequency and type across years. In 2017, the U.S. Navy and the U.S. Fish and Wildlife Service initiated a southern sea otter monitoring and research plan to determine the effects of military readiness activities on the growth or decline of the southern sea otter population at San Nicolas Island. The southern sea otter is the only subspecies of sea otter in California (hereafter, “sea otter\"). The monitoring program, at its basic level, includes seasonal surveys of population abundance, distribution, and foraging activity. From 2020 to 2023, we measured a 10-percent per annum increase in population abundance (95-percent confidence interval =0–20 percent), with 146 total individuals as of April 2023. Coinciding with the recent population growth, the sea otter distribution, which previously tended to concentrate on the island’s west end during 2003–2006 before shifting toward more use in the north and south sides during 2017–2019, appears to have shifted again during 2020–2023 to concentrate at the island’s east end. Forage data were collected between February 2020 and April 2023. There was a total of 773 forage dives in 60 forage bouts, with most of the identified prey on successful dives (n=401) recorded as sea urchins (66 percent), followed by bivalves (15 percent), snails (12 percent), and crabs (5.2 percent). Two lobsters and three abalone also were identified among the sea otter prey. Estimates of energy intake rates averaged 14.0 kilocalories per minute (95-percent confidence interval =10.8–17.2 kilocalories per minute). Monitoring data from the past two decades indicate that sea otters at San Nicolas Island have maintained a steady pattern of energy intake and population growth characteristic of a robust population, including a sixfold growth between 2000 and 2023. There was no conclusive evidence of density-dependent effects based on these patterns; however, estimates of energy intake rates for 2020–2023 were slightly lower than previous estimates from 2017 to 2019. Additionally, subtidal monitoring results at four sites around San Nicolas Island indicated that counts of purple sea urchins (Strongylocentrotus purpuratus) have increased between 2003 and 2023, whereas sea otter foraging surveys completed during the same period revealed that some sea otters have shifted toward higher consumption of purple sea urchins and bivalves compared to red sea urchins (S. fransicanus), which generally are the preferred larger prey of sea otters. These results contribute to the understanding of population dynamics and to the conservation and planning of future monitoring and research of sea otters at San Nicolas Island.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231071","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the U.S. Navy","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Yee, J.L., Tomoleoni, J.A., Kenner, M.C., Fujii, J.A., Bentall, G.B., Staedler, M.M., and Hatfield, B.B., 2023, Southern (California) sea otter population status and trends at San Nicolas Island, 2020–2023: U.S. Geological Survey Open-File Report 2023–1071, 37 p., https://doi.org/10.3133/ofr20231071.","productDescription":"vii, 37 p.","numberOfPages":"37","onlineOnly":"Y","ipdsId":"IP-155128","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":420734,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231071/full"},{"id":420730,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1071/covrthb.jpg"},{"id":420731,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1071/ofr20231071.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":420732,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1071/ofr20231071.xml"},{"id":420733,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1071/images"}],"country":"United States","state":"California","otherGeospatial":"San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.41914133101146,\n 33.23095571590274\n ],\n [\n -119.5256785251456,\n 33.28967086732948\n ],\n [\n -119.583789721946,\n 33.28157457228808\n ],\n [\n -119.57410452247933,\n 33.24816941739742\n ],\n [\n -119.54504892407897,\n 33.22791765206338\n ],\n [\n -119.47119927814498,\n 33.20867413062352\n ],\n [\n -119.43730108001157,\n 33.21576434144701\n ],\n [\n -119.41914133101146,\n 33.23095571590274\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
This paper applies a catch multiple survey analysis (CMSA) to Atlantic horseshoe crabs Limulus polyphemus in the Delaware Bay to generate robust population estimates for harvest management. Currently, horseshoe crabs along the U.S. Atlantic coast are harvested as bait for other fisheries and collected for their blood, which is used in a biomedical industry. The Delaware Bay is home to the largest population of horseshoe crabs and is a significant stopover for shorebirds to rebuild energy by consuming horseshoe crab eggs prior to completing their northward migration. To address this interrelationship, the Adaptive Resource Management (ARM) Framework has been used since 2013 to ensure that horseshoe crab harvest within the region takes into account the forage needs of migratory birds. Since its inception, the ARM Framework has used a single trawl survey's swept area-based population estimates of horseshoe crab relative abundance and a theoretical population model developed primarily from literature-derived values. With more data collected in the region in recent years and other sources of mortality that can now be quantified, a catch survey model can provide horseshoe crab population estimates going forward.
A CMSA was used to estimate male and female horseshoe crab population size for 2003–2021 using all quantifiable sources of mortality and three fishery-independent indices of abundance.
The CMSA results indicated that adult abundance of male and female horseshoe crabs was stable from 2003 to 2013 and then began to increase through 2017, a result that is consistent with stock rebuilding following a period of harvest restrictions as recommended by the ARM Framework. Population estimates were lower in recent years but remained above the levels estimated before implementation of the ARM Framework. In 2021, the CMSA estimated that there were over 6 million mature females and nearly 16 million mature male horseshoe crabs in the region.
The CMSA provides the best and most comprehensive population estimates of horseshoe crabs in Delaware Bay and will improve modeling efforts within the ARM Framework going forward.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10250","usgsCitation":"Anstead, K.A., Sweka, J., Barry, L., Hallerman, E., Smith, D.R., Ameral, N., Schmidtke, M., and Wong, R.A., 2023, Application of a catch multiple survey analysis for Atlantic horseshoe crab Limulus polyphemus in the Delaware Bay: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 15, no. 5, e10250, 16 p., https://doi.org/10.1002/mcf2.10250.","productDescription":"e10250, 16 p.","ipdsId":"IP-154235","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":442129,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10250","text":"Publisher Index Page"},{"id":420973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, New Jersey","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.0255903899312,\n 40.963933240903344\n ],\n [\n -76.0255903899312,\n 37.040030719320384\n ],\n [\n -73.65356124067296,\n 37.040030719320384\n ],\n [\n -73.65356124067296,\n 40.963933240903344\n ],\n [\n -76.0255903899312,\n 40.963933240903344\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Anstead, Kristen A.","contributorId":329847,"corporation":false,"usgs":false,"family":"Anstead","given":"Kristen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":883459,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sweka, John A.","contributorId":288581,"corporation":false,"usgs":false,"family":"Sweka","given":"John A.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":883460,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barry, Linda","contributorId":329848,"corporation":false,"usgs":false,"family":"Barry","given":"Linda","email":"","affiliations":[],"preferred":false,"id":883461,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hallerman, Eric M.","contributorId":279474,"corporation":false,"usgs":false,"family":"Hallerman","given":"Eric M.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":883462,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, David R. 0000-0001-9560-5210 dvsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-9560-5210","contributorId":329849,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"dvsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":883463,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ameral, Natalie","contributorId":329850,"corporation":false,"usgs":false,"family":"Ameral","given":"Natalie","email":"","affiliations":[],"preferred":false,"id":883464,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmidtke, Michael","contributorId":329851,"corporation":false,"usgs":false,"family":"Schmidtke","given":"Michael","email":"","affiliations":[],"preferred":false,"id":883465,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wong, Richard A.","contributorId":329852,"corporation":false,"usgs":false,"family":"Wong","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":883466,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70249497,"text":"70249497 - 2023 - Shallow fault slip of the 2020 M5.1 Sparta, North Carolina, earthquake","interactions":[],"lastModifiedDate":"2023-11-07T16:18:22.677501","indexId":"70249497","displayToPublicDate":"2023-09-11T06:36:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Shallow fault slip of the 2020 M5.1 Sparta, North Carolina, earthquake","docAbstract":"The 2020 M 5.1 Sparta, North Carolina, earthquake is the largest in the eastern United States since the 2011 M 5.8 Mineral, Virginia, earthquake and produced a ∼2.5‐km‐long surface rupture, unusual for an event of this magnitude. A geological field study conducted soon after the event indicates oblique slip along a east‐southeast‐trending fault with a consistently observed thrust component. My analysis of regional seismic waveforms, Interferometric Synthetic Aperture Radar, and Global Positioning System survey data yields a compact shallow rupture extending from Earth’s surface down‐dip to the southwest over a ∼3 km fault length. The inferred kinematic rupture is primarily toward the up‐dip and eastward along‐strike directions and has predominantly thrust motion in the west, transitioning to roughly equal thrust and left‐lateral strike‐slip motion in the east. No normal faulting component, as proposed in an earlier geophysical study, is necessary to explain the data. The prevalence of only dip‐slip motions observed at Earth’s surface may demand slip partitioning between dip slip and lateral motions at depth.
Pennsylvania experienced heavy rainfall on September 1 and 2, 2021, as the remnants of Hurricane Ida swept over parts of the State. Much of eastern and south-central Pennsylvania received 5 to 10 inches of rain, and most of the rainfall fell within little more than 6 hours. Southeastern Pennsylvania experienced widespread, substantial flooding, and the city of Philadelphia and surrounding areas were particularly affected by the flooding. U.S. Geological Survey (USGS) streamgages registered peak streamflows of record at 19 locations, and 52 locations experienced top 5 peak streamflows for the period of record and an annual exceedance probability estimate of at least 10 percent. During this September 2021 flood event, USGS personnel made over 60 streamflow measurements at streamgages in Pennsylvania using direct and indirect methods. Many of those streamflow measurements were made to verify or improve the accuracy, extent, or development of new stage-streamflow relations at streamgages operated by the USGS. After the floodwaters receded, USGS personnel identified and documented a total of 338 high-water marks in Pennsylvania, noting such things as their general description, location, height above land surface, and quality. Many of these high-water marks were used to create five flood-documentation maps for selected communities in southeastern Pennsylvania that experienced substantial flooding because of the remnants of Hurricane Ida. Digital datasets of the inundated areas, mapped boundaries, and water depth are available (Stuckey and Conlon, 2023).
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\"}}]}","edition":"Version 1.0: September 7, 2023; Version 1.1: September 28, 2023","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 170
Avian influenza viruses pose a threat to wildlife and livestock health. The emergence of highly pathogenic avian influenza (HPAI) in wild birds and poultry in North America in late 2021 was the first such outbreak since 2015 and the largest outbreak in North America to date. Despite its prominence and economic impacts, we know relatively little about how HPAI spreads in wild bird populations. In January 2022, we captured 43 mallards (Anas platyrhynchos) in Tennessee, USA, 11 of which were actively infected with HPAI. These were the first confirmed detections of HPAI H5N1 clade 2.3.4.4b in the Mississippi Flyway. We compared movement patterns of infected and uninfected birds and found no clear differences; infected birds moved just as much during winter, migrated slightly earlier, and migrated similar distances as uninfected birds. Infected mallards also contacted and shared space with uninfected birds while on their wintering grounds, suggesting ongoing transmission of the virus. We found no differences in body condition or survival rates between infected and uninfected birds. Together, these results show that HPAI H5N1 clade 2.3.4.4b infection was unrelated to body condition or movement behavior in mallards infected at this location during winter; if these results are confirmed in other seasons and as HPAI H5N1 continues to evolve, they suggest that these birds could contribute to the maintenance and dispersal of HPAI in North America. Further research on more species across larger geographic areas and multiple seasons would help clarify potential impacts of HPAI on waterfowl and how this emerging disease spreads at continental scales, across species, and potentially between wildlife and domestic animals.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-023-40921-z","usgsCitation":"Teitelbaum, C.S., Masto, N.M., Sullivan, J.D., Keever, A., Poulson, R., Carter, D., Blake-Bradshaw, A., Highway, C., Feddersen, J., Hagy, H.M., Gerhold, R.W., Cohen, B.S., and Prosser, D.J., 2023, North American wintering mallards infected with highly pathogenic avian influenza show few signs of altered local or migratory movements: Scientific Reports, v. 13, 14473, 11 p., https://doi.org/10.1038/s41598-023-40921-z.","productDescription":"14473, 11 p.","ipdsId":"IP-149626","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":442230,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-023-40921-z","text":"Publisher Index Page"},{"id":435195,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AZL1MN","text":"USGS data release","linkHelpText":"Data showing 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0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":882104,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keever, Allison","contributorId":187743,"corporation":false,"usgs":false,"family":"Keever","given":"Allison","email":"","affiliations":[],"preferred":false,"id":882105,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poulson, Rebecca L.","contributorId":198807,"corporation":false,"usgs":false,"family":"Poulson","given":"Rebecca L.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":882106,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carter, Deborah","contributorId":213914,"corporation":false,"usgs":false,"family":"Carter","given":"Deborah","affiliations":[{"id":38928,"text":"University of Georgia Southeastern Cooperative Wildlife Disease Study","active":true,"usgs":false}],"preferred":false,"id":882107,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blake-Bradshaw, Abigail","contributorId":316651,"corporation":false,"usgs":false,"family":"Blake-Bradshaw","given":"Abigail","email":"","affiliations":[{"id":68664,"text":"Tennessee Technical University","active":true,"usgs":false}],"preferred":false,"id":882108,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Highway, Corey","contributorId":329379,"corporation":false,"usgs":false,"family":"Highway","given":"Corey","email":"","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":882109,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Feddersen, Jamie","contributorId":329381,"corporation":false,"usgs":false,"family":"Feddersen","given":"Jamie","email":"","affiliations":[{"id":13408,"text":"Tennessee Wildlife Resources Agency","active":true,"usgs":false}],"preferred":false,"id":882110,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hagy, Heath M.","contributorId":172326,"corporation":false,"usgs":false,"family":"Hagy","given":"Heath","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":882111,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gerhold, Richard W.","contributorId":201770,"corporation":false,"usgs":false,"family":"Gerhold","given":"Richard","email":"","middleInitial":"W.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":882112,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Cohen, Bradley S.","contributorId":171513,"corporation":false,"usgs":false,"family":"Cohen","given":"Bradley","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":882113,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Prosser, Diann J. 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":221167,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":882114,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70248872,"text":"70248872 - 2023 - Ground motion and seismic hazard in the central and eastern United States","interactions":[],"lastModifiedDate":"2026-03-19T15:08:47.759038","indexId":"70248872","displayToPublicDate":"2023-09-01T10:02:25","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":21641,"text":"Technical Letter Report","active":true,"publicationSubtype":{"id":1}},"title":"Ground motion and seismic hazard in the central and eastern United States","docAbstract":"This report describes work carried out under the U.S. Nuclear Regulatory Commission (NRC) Interagency Agreement to the U.S. Geological Survey (USGS) “Research to Support NRC’s Seismic Hazard Analyses” for Task 3, “Seismic Hazard and Ground Motion Models.” The focus of this work has been on evaluation of the Next Generation Attenuation (NGA)-East groundmotion models (GMMs) with available ground-motion data and evaluation of alternative methods for characterizing epistemic uncertainty for probabilistic seismic hazard analysis (PSHA).
When the Interagency Agreement commenced, the USGS’s National Seismic Hazard Model (NSHM) was being updated, and a significant part of the update for the 2018 NSHM included the introduction of new GMMs for the central and eastern United States (CEUS) (Petersen et al., 2020). In addition to the implementation of the then-recently developed NGA-East GMMs (Goulet et al., 2018), the ground-motion characterization for the CEUS in the 2018 NSHM also included a logic-tree branch with weights applied to the updated “adjusted seed” models that were developed as part of the NGA-East process and in updates by the GMM developers.
The Statement of Work for Task 3 of the NRC Interagency Agreement to the USGS included the following parts: (1) Describe technically acceptable approaches in combining GMMs for use in PSHA calculations; (2) Evaluate the effect of using different sets of GMMs (NGA-East SSHAC versus NGA-East USGS) on PSHA calculations; (3) Describe the results of the GMM testing against recorded data, and provide a recommendation on the GMMs application for use in the PSHA; (4) Evaluate the impacts of recently updated individual GMMs on the published NGAEast models; and (5) Submit a final Technical Letter Report documenting results of this task.
We present results from this work in two sections: (Chapter 1) Updated Central and Eastern United States Ground Motions and Ground-Motion Analyses; and (Chapter 2) Approaches to Combining Ground-Motion Models for Probabilistic Seismic Hazard Analysis in the Central and Eastern United States.
","language":"English","publisher":"U.S. Nuclear Regulatory Commission","usgsCitation":"Moschetti, M.P., Thompson, E.M., Boyd, O.S., Engler, D.T., Worden, B., Ferragut, G., Rezaeian, S., and Powers, P.M., 2023, Ground motion and seismic hazard in the central and eastern United States: Technical Letter Report, 73 p.","productDescription":"73 p.","ipdsId":"IP-154468","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":501310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"central and eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.90807059528223,\n 48.94073422059739\n ],\n [\n -106.90807059528223,\n 20.331161730583176\n ],\n [\n -61.27021618898843,\n 20.331161730583176\n ],\n [\n -61.27021618898843,\n 48.94073422059739\n ],\n [\n -106.90807059528223,\n 48.94073422059739\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":883992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":883993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":883994,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Engler, Davis T. 0000-0002-7133-3545","orcid":"https://orcid.org/0000-0002-7133-3545","contributorId":265962,"corporation":false,"usgs":true,"family":"Engler","given":"Davis","email":"","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":883995,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Worden, Bruce","contributorId":64648,"corporation":false,"usgs":true,"family":"Worden","given":"Bruce","affiliations":[],"preferred":false,"id":957156,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ferragut, Gabriel Christian 0000-0002-0776-9176","orcid":"https://orcid.org/0000-0002-0776-9176","contributorId":330101,"corporation":false,"usgs":true,"family":"Ferragut","given":"Gabriel Christian","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":883996,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893 srezaeian@usgs.gov","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":4395,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","email":"srezaeian@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":883997,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Powers, Peter M. 0000-0003-2124-6184 pmpowers@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6184","contributorId":176814,"corporation":false,"usgs":true,"family":"Powers","given":"Peter","email":"pmpowers@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":883998,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70267362,"text":"70267362 - 2023 - Induced seismicity and its impact on existing seismic hazard analysis","interactions":[],"lastModifiedDate":"2025-05-21T13:52:21.335242","indexId":"70267362","displayToPublicDate":"2023-09-01T08:48:53","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":21641,"text":"Technical Letter Report","active":true,"publicationSubtype":{"id":1}},"title":"Induced seismicity and its impact on existing seismic hazard analysis","docAbstract":"We develop a scheme for mapping changes in earthquake rates within a region in near-real-time. A specific goal of the work is to track recent changes in the rates of induced earthquakes in the central and eastern United States. We map rates in a time window of interest, map rates in a preceding time window, and then ratio the two maps to show changes. A proof-of-concept map is first prepared, comparing rates during the first six months of 2010 with rates from the preceding five years; this demonstration map shows, among other things, the growth of induced seismicity in Oklahoma during 2010. We then present a series of ten ratio maps: the first six months of 2018 compared with the preceding five years, and so on in six-month increments through the end of 2022. These maps show changes in the rates and locations of induced earthquakes, as well as other seismicity trends. Map regions, time windows, and other model parameters are easily adaptable for other applications.","language":"English","publisher":"U.S. Nuclear Regulatory Comission","usgsCitation":"Mueller, C., and Shumway, A., 2023, Induced seismicity and its impact on existing seismic hazard analysis: Technical Letter Report, xv, 17 p.","productDescription":"xv, 17 p.","ipdsId":"IP-152895","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":486279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":486278,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.nrc.gov/docs/ML2325/ML23257A188.pdf"}],"noUsgsAuthors":false,"publicationDate":"2023-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Mueller, Charles 0000-0002-1868-9710 cmueller@usgs.gov","orcid":"https://orcid.org/0000-0002-1868-9710","contributorId":140380,"corporation":false,"usgs":true,"family":"Mueller","given":"Charles","email":"cmueller@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shumway, Allison 0000-0003-1142-7141 ashumway@usgs.gov","orcid":"https://orcid.org/0000-0003-1142-7141","contributorId":147862,"corporation":false,"usgs":true,"family":"Shumway","given":"Allison","email":"ashumway@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937974,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70252959,"text":"70252959 - 2023 - Interstate 15 wildlife crossing design considerations for focal wildlife species - Santa Ana-Palomar Mountains Linkage southern California","interactions":[],"lastModifiedDate":"2024-04-12T13:52:55.734858","indexId":"70252959","displayToPublicDate":"2023-09-01T08:38:51","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Interstate 15 wildlife crossing design considerations for focal wildlife species - Santa Ana-Palomar Mountains Linkage southern California","docAbstract":"The Nature Conservancy (TNC) and the California Department of Transportation (Caltrans), along with landowners including San Diego State University, California Department of Fish and Wildlife, Western Riverside Regional Conservation Authority and Riverside County Flood Control District are developing wildlife crossing infrastructure projects along a 3-mile stretch of Interstate 15 (I-15) in the Santa Ana-Palomar Mountains Linkage (hereafter ‘Linkage’) in southern California. These crossings will provide a critical missing link that will help reconnect wildlife in the coastal Santa Ana Mountains west of I-15 with those in the interior Palomar and Eastern Peninsular ranges to the east of I-15. The Linkage supports intact and diverse habitats including coastal sage scrub, grasslands, chaparral, and oak and riparian woodlands, and has been a focus of regional conservation efforts for the last 30 years.
The three wildlife crossing infrastructure projects include enhancement of the existing Temecula Creek I-15 Bridge, construction of a new vegetated wildlife overcrossing, and construction of a new stand-alone wildlife culvert.
Given the challenges and level of financial investment required to secure wildlife crossings for I-15 in the Linkage, TNC and Caltrans proposed that planning efforts would benefit from input by taxonomic experts on design concepts that meet the needs of the broadest range of wildlife. While wildlife crossings are becoming more common, optimal designs that meet the needs of a variety of wildlife species are largely unknown and can be site specific. To address this challenge, we held a workshop in February 2022 that brought together over 50 wildlife experts to brainstorm and identify specific design considerations for various focal wildlife species groups (medium/large mammals, small animals, birds, bats, plants, and invertebrates) that might use the identified I-15 wildlife crossings (The Nature Conservancy 2022).
Lead experts for each focal species group worked together to identify specific wildlife crossing features or attributes for each of the three proposed wildlife crossings.
Specific attributes evaluated by experts for each crossing type and species group included, at a minimum:
• Crossing Structure Attributes;
o Habitat features? (cover, habitat structure, substrate, moisture, light and noise mitigation)
• Crossing Approach Area Features;
o Habitat features (cover type/density, substrate, water, light and noise mitigation)
• Barrier design to reduce roadkill and/or to funnel wildlife to the crossing;
• Additional research to resolve uncertainties related to crossing design
Based on the design considerations for each potential crossing type, the experts then weighed in on the suitability of the existing location and probability of use by their focal species or groups of species. With proposed design features, Temecula Creek Bridge has moderate or high probability of use by 27 of the 36 focal wildlife species assessed, while the vegetated overcrossing could meet the needs of 26 of the 36 species. When combined, Temecula Creek Bridge and the vegetated overcrossing have a moderate or high probability of use for 34 of the 36 species. The wildlife culvert has a moderate or high level of expected use by 10 of the 36 focal species and could serve connectivity needs for representative species from all but the bird and plant species groups.
","language":"English","publisher":"The Nature Conservancy","usgsCitation":"Smith, T., Brehme, C.S., Carpenter, J., Frost, N.A., Jennings, M., Kus, B., Quinnell, S., Straham, S., and Vickers, T.W., 2023, Interstate 15 wildlife crossing design considerations for focal wildlife species - Santa Ana-Palomar Mountains Linkage southern California, iv, 46 p.","productDescription":"iv, 46 p.","ipdsId":"IP-156534","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":427729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":427723,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.scienceforconservation.org/products/interstate-15-wildlife-crossing-design"}],"country":"United States","state":"California","otherGeospatial":"Santa Ana-Palomar Mountains Linkage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.96277940038678,\n 33.17061852067067\n ],\n [\n -116.96277940038678,\n 33.583670223835924\n ],\n [\n -117.44162796500501,\n 33.583670223835924\n ],\n [\n -117.44162796500501,\n 33.17061852067067\n ],\n [\n -116.96277940038678,\n 33.17061852067067\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Trish","contributorId":335587,"corporation":false,"usgs":false,"family":"Smith","given":"Trish","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":898763,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brehme, Cheryl S. 0000-0001-8904-3354 cbrehme@usgs.gov","orcid":"https://orcid.org/0000-0001-8904-3354","contributorId":3419,"corporation":false,"usgs":true,"family":"Brehme","given":"Cheryl","email":"cbrehme@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":898764,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carpenter, Jill","contributorId":335588,"corporation":false,"usgs":false,"family":"Carpenter","given":"Jill","email":"","affiliations":[{"id":80443,"text":"LSA Associates","active":true,"usgs":false}],"preferred":false,"id":898765,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frost, Nancy A.","contributorId":200382,"corporation":false,"usgs":false,"family":"Frost","given":"Nancy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":898766,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jennings, Megan","contributorId":298254,"corporation":false,"usgs":false,"family":"Jennings","given":"Megan","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":898767,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kus, Barbara E. 0000-0002-3679-3044 barbara_kus@usgs.gov","orcid":"https://orcid.org/0000-0002-3679-3044","contributorId":3026,"corporation":false,"usgs":true,"family":"Kus","given":"Barbara E.","email":"barbara_kus@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":898768,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Quinnell, Scott","contributorId":335593,"corporation":false,"usgs":false,"family":"Quinnell","given":"Scott","email":"","affiliations":[],"preferred":false,"id":898780,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Straham, Spring","contributorId":335589,"corporation":false,"usgs":false,"family":"Straham","given":"Spring","email":"","affiliations":[{"id":80444,"text":"Wildspring Ecology","active":true,"usgs":false}],"preferred":false,"id":898769,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Vickers, T. Winston","contributorId":198755,"corporation":false,"usgs":false,"family":"Vickers","given":"T.","email":"","middleInitial":"Winston","affiliations":[],"preferred":false,"id":898770,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70264030,"text":"70264030 - 2023 - Eastern Indigo snake (Drymarchon couperi) shelter site use In peninsular Florida, USA, and implicatIons for habItat conservatIon","interactions":[],"lastModifiedDate":"2025-03-05T17:28:54.304812","indexId":"70264030","displayToPublicDate":"2023-08-31T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1894,"text":"Herpetological Conservation and Biology","onlineIssn":"2151-0733","printIssn":"1931-7603","active":true,"publicationSubtype":{"id":10}},"title":"Eastern Indigo snake (Drymarchon couperi) shelter site use In peninsular Florida, USA, and implicatIons for habItat conservatIon","docAbstract":"Shelters are critical for many species as protection from predators and extreme temperatures. Successful conservation of reptiles requires understanding both shelter site requirements and availability. The Eastern Indigo Snake (EIS; Drymarchon couperi) is endemic to the southeastern U.S. and is federally listed. Recovery has focused on maximizing unfragmented landscapes, with less attention on fine-scale features such as shelter sites. In the northern EIS range, Gopher Tortoise (Gopherus polyphemus) burrows are used extensively for shelter. Although EIS in peninsular Florida often shelter in tortoise burrows, they also use other shelters where tortoise burrows are scarce or absent. Solely focusing EIS survey and management efforts where Gopher Tortoises are present may overlook occupied habitats and misallocate resources. We investigated the importance of different shelter sites in central Florida using data from radio-tracked EIS. We modeled the use of shelter categories as a function of sex, season, and habitat using Bayesian multinomial Generalized Linear Models. Results showed that EIS in peninsular Florida used Gopher Tortoise burrows across all seasons and habitats. Tortoise burrow use was highest in xeric habitats and lowest in mesic habitats where burrows are most and least abundant, respectively. There was less variability in shelter site use in disturbed habitats and flatwoods. Tortoise burrow use by EIS in the cool season across sexes and habitats in our study was much lower than in southern Georgia. Our results indicate that EIS are less dependent on Gopher Tortoise burrows in peninsular Florida and that suitable habitats with few or no tortoise burrows could still provide conservation value for EIS.
","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Bolt, M., Bauder, J.M., Legare, M., Jenkins, C., Rothermel, B., and Breininger, D., 2023, Eastern Indigo snake (Drymarchon couperi) shelter site use In peninsular Florida, USA, and implicatIons for habItat conservatIon: Herpetological Conservation and Biology, v. 18, no. 2, p. 362-373.","productDescription":"12 p.","startPage":"362","endPage":"373","ipdsId":"IP-139825","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":482919,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.herpconbio.org/contents_vol18_issue2.html","linkFileType":{"id":8,"text":"xml"}},{"id":482920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, 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Rebecca","affiliations":[{"id":84051,"text":"NASA Environmental and Medical Contract","active":true,"usgs":false}],"preferred":false,"id":929526,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bauder, Javan Mathias 0000-0002-2055-5324","orcid":"https://orcid.org/0000-0002-2055-5324","contributorId":337814,"corporation":false,"usgs":true,"family":"Bauder","given":"Javan","email":"","middleInitial":"Mathias","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":929527,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Legare, Michael L.","contributorId":351810,"corporation":false,"usgs":false,"family":"Legare","given":"Michael L.","affiliations":[{"id":84053,"text":"Merritt Island National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":929528,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jenkins, Christopher L.","contributorId":351811,"corporation":false,"usgs":false,"family":"Jenkins","given":"Christopher L.","affiliations":[{"id":13223,"text":"The Orianne Society","active":true,"usgs":false}],"preferred":false,"id":929529,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rothermel, Betsie B.","contributorId":351812,"corporation":false,"usgs":false,"family":"Rothermel","given":"Betsie B.","affiliations":[{"id":17991,"text":"Archbold Biological Station","active":true,"usgs":false}],"preferred":false,"id":929530,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Breininger, David R.","contributorId":351813,"corporation":false,"usgs":false,"family":"Breininger","given":"David R.","affiliations":[{"id":84051,"text":"NASA Environmental and Medical Contract","active":true,"usgs":false}],"preferred":false,"id":929531,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70247945,"text":"pp1884 - 2023 - Roles of regional structures and country-rock facies in defining mineral belts in central Idaho mineral province with detail for Yellow Pine and Thunder Mountain mining districts","interactions":[],"lastModifiedDate":"2026-02-19T17:27:07.609639","indexId":"pp1884","displayToPublicDate":"2023-08-29T15:01:01","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1884","displayTitle":"Roles of Regional Structures and Country-Rock Facies in Defining Mineral Belts in Central Idaho Mineral Province with Detail for Yellow Pine and Thunder Mountain Mining Districts","title":"Roles of regional structures and country-rock facies in defining mineral belts in central Idaho mineral province with detail for Yellow Pine and Thunder Mountain mining districts","docAbstract":"The central Idaho metallogenic province hosts numerous mineral deposit types. These include Late Cretaceous precious-polymetallic vein deposits, amagmatic Paleocene–Eocene breccia-hosted gold-tungsten-antimony deposits, and Eocene mercury deposits in metasedimentary roof pendants and in Late Cretaceous granitoids. Hot-springs gold deposits in Eocene volcanic rocks are also included in the central Idaho province. New sensitive high mass-resolution ion microprobe (SHRIMP) uranium-lead (U-Pb) ages for igneous rocks and for detrital zircon analyses of metasedimentary rocks along with geologic mapping clarify the geologic framework of the mineral deposits. This framework includes (1) structural controls for regional distribution of mining districts, (2) progressive structural development of individual districts, (3) regional sedimentary facies and their control of metals associations resulting in regional belts, and (4) influences of the several regional magmatic events.
In central Idaho, 15 mining districts form two clusters that are grouped about a 200-kilometer (km) long system of normal faults. The northwestern cluster is in the regional hanging wall west of large, west-side-down faults, and the mineral deposits are located along smaller faults and fractures that cut the regional hanging wall. The southeastern cluster is in the regional hanging wall east of a linked large east-side-down fault and along and controlled by related hanging wall faults. At the southern extent of the regional fault system, the Yellow Pine-Thunder Mountain districts span a nearly 24-km-wide, east-tilted crustal block of normal-fault dominoes, exposing original crustal depths from 5 to 10 km deep on the west in the Late Cretaceous to shallow-surface depths on the east in the Eocene.
Ore deposition in the northwestern district cluster was primarily Late Cretaceous and related to Idaho batholith plutons with only a single deposit related to a small Eocene intrusion; in the southeastern cluster, most deposits were initiated in the Late Cretaceous but with varying manifestations of overprinted Eocene mineralization activity. In the Yellow Pine-Thunder Mountain districts at the southern extent of the southern cluster, several mineralizing pulses occurred during hanging-wall collapse, such that (1) early deposits were multiply overprinted and (2) deposit depths, ages, and structural characteristics change progressively eastward. Originally deep-seated western Yellow Pine district deposits are Late Cretaceous viscoplastic mesothermal veins overprinted by Paleocene and Eocene breccia-hosted epithermal deposits. Central Yellow Pine district deposits contain early deeper vein systems but are primarily Paleocene and Eocene breccia-hosted epithermal deposits in Late Cretaceous plutonic rocks and Proterozoic–Paleozoic roof pendant rocks. Eastern district deposits are Eocene hot-springs-related deposits in the roof pendant. Thunder Mountain deposits farthest east are near-surface hot-springs deposits in Eocene volcanic and volcaniclastic rocks that overlie buried Cretaceous igneous and older roof pendant rocks.
The mining district clusters are sited across several northwest-striking paleostratigraphic belts that are exposed in roof pendants and are offset by the regional normal fault system. A northeastern belt is Mesoproterozoic strata associated with gold-silver-copper±cobalt deposits. A central belt of Neoproterozoic rocks is not associated with mineral deposits in the central Idaho mineral province. A southwestern belt composed of probable Paleozoic deep-water miogeoclinal slope rocks and late Paleozoic epicratonic basinal rocks is thin and narrowly exposed but associated with gold-silver-antimony-tungsten±mercury deposits. These metasedimentary rocks (and their metal associations) are parts of regional mineral belts in which metal endowments are related to particular sedimentary facies belts and their Cretaceous thrust-fault juxtaposition and where these features have proximity to Late Cretaceous or Eocene igneous rocks. Offset and preservation or erosional stripping of these facies belts, thrust plates, igneous settings, and the associated regional mineral belts were controlled by the sense and magnitude of displacements across the regional normal-fault system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1884","programNote":"Mineral Resources Program","usgsCitation":"Lund, K., Aleinikoff, J.N., and Holm-Denoma, C., 2023, Roles of regional structures and country-rock facies in defining mineral belts in central Idaho mineral province with detail for Yellow Pine and Thunder Mountain mining districts (ver. 1.1, September 2023): U.S. Geological Survey Professional Paper 1884, 53 p., https://doi.org/10.3133/pp1884.","productDescription":"Report: vii, 53 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-108495","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":422432,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1884/pp1884.xml"},{"id":422431,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1884/images"},{"id":420574,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/pp/1884/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":500195,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115237.htm","linkFileType":{"id":5,"text":"html"}},{"id":420149,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P931I3A3","text":"USGS data release","linkHelpText":"SHRIMP U-Pb and LA-ICPMS U-Pb geochronologic data for igneous and metasedimentary rocks in central Idaho mineral province, U.S.A., 2023"},{"id":420146,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1884/pp1884.pdf","text":"Report","size":"19.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1884"},{"id":420145,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1884/coverthb2.jpg"},{"id":422433,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/pp1884/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"PP 1884"}],"country":"United States","state":"Idaho","otherGeospatial":"Yellow Pine and Thunder Mountain Mining Districts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.00,\n 47.00\n ],\n [\n -117.00,\n 43.00\n ],\n [\n -112.00,\n 43.00\n ],\n [\n -112.00,\n 47.00\n ],\n [\n -117.00,\n 47.00\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: August 29, 2023; Version 1.1: September 6, 2023","contact":"Center Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225
Acipenser oxyrinchus oxyrinchus (Atlantic sturgeon) were once abundant and supported large-scale fisheries throughout much of the east coast of the United States. However, historic overharvest and habitat loss resulted in dramatic declines in abundance and eventual listing under the Endangered Species Act of the United States. As part of this listing, Atlantic sturgeon populations were divided into five distinct population segments (DPSs), with many management activities occurring at the level of the DPS. However, because subadult and adult Atlantic sturgeon can make large, coast-wide migrations and often mix extensively with individuals from other populations, individuals may be exposed to conservation threats away from their natal river or DPS, ultimately making it difficult to determine the appropriate spatial scale for management activities. To help address this uncertainty, the U.S. Geological Survey performed genetic assignment tests to determine the natal origin of 329 Atlantic sturgeon that were encountered as mortalities or taken during permitted activities in 2021. Overall, most individuals assigned to the Hudson River population, with additional major contributions from the James River Fall and Delaware River populations. However, a sizeable proportion of individuals were assigned to more distantly located populations in the southeastern United States. These results highlight the prevalence of long-distance movements in Atlantic sturgeon and underscore that populations may be vulnerable to threats far from their natal rivers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231054","collaboration":"Prepared in cooperation with the National Marine Fisheries Service","usgsCitation":"White, S.L., Johnson, R.L., Lubinski, B.A., Eackles, M.S., and Kazyak, D.C., 2023, Genetic population assignments of Atlantic sturgeon provided to National Marine Fisheries Service, 2022: U.S. Geological Survey Open-File Report 2023–1054, 10 p., https://doi.org/10.3133/ofr20231054.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-136703","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":419992,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P943B0GT","text":"USGS data release","linkHelpText":"Individual assignments and microsatellite genotypes for Atlantic sturgeon from 2021"},{"id":419987,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1054/coverthb.jpg"},{"id":419988,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1054/ofr20231054.pdf","text":"Report","size":"3.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1054"},{"id":419989,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231054/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1054"},{"id":419990,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1054/images/"},{"id":419991,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1054/ofr20231054.XML"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.7831526539585,\n 44.088449979425064\n ],\n [\n -69.15659153864834,\n 48.019075945012474\n ],\n [\n -72.30141815542203,\n 45.45727029656064\n ],\n [\n -75.65820657339519,\n 44.94778904002476\n ],\n [\n -75.28903339967198,\n 40.89558675319671\n ],\n [\n -76.46298604911388,\n 40.19400583735714\n ],\n [\n -78.43915371095366,\n 36.5604218577474\n ],\n [\n -84.16051772711577,\n 32.88156909351903\n ],\n [\n -83.55638083759197,\n 30.468214471664382\n ],\n [\n -80.73133729902378,\n 30.687235465695224\n ],\n [\n -78.61360348508101,\n 32.88392643413653\n ],\n [\n -75.62804928587406,\n 34.56644068656462\n ],\n [\n -74.64749468377897,\n 36.185966080642245\n ],\n [\n -74.65126231991508,\n 38.03204715851999\n ],\n [\n -73.38050438077715,\n 40.0657922005791\n ],\n [\n -69.39263502960705,\n 41.17701014917236\n ],\n [\n -70.1486437939862,\n 43.328483757491426\n ],\n [\n -66.7831526539585,\n 44.088449979425064\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Objective
Sea Lamprey Petromyzon marinus provide important ecological services within their native range, such as nutrient cycling, and can also act as a prey source for other species. Adult Sea Lamprey must access freshwater rivers to spawn, and because of this they are susceptible to changes in river connectivity. Human-made structures, such as dams, can exclude them from usable habitat. Sea Lamprey dam passage has not been extensively studied in Maine, despite Maine being within the native range of this species. The goals of this study were to evaluate upstream passage efficiency at the Milford Dam on the Penobscot River, Maine, and to provide comprehensive information about adult Sea Lamprey passage at five other dams throughout the Penobscot River watershed.
Methods
In 2020–2021 we captured and tagged 150 Sea Lamprey at the Milford Dam, the lowest dam in the Penobscot River, Maine, and displaced them downstream to assess passage efficiency at this dam and five upstream dams. In 2020, 50 Sea Lamprey were released on the east shore of the river downstream of Milford Dam; in 2021, the east shore release was repeated with an additional 50 fish and another 50 fish were released on the west shore.
Result
Between 70–82% of Sea Lamprey were observed passing Milford Dam again after mean delay times of 9–11 days. The release location did not affect dam passage success or the amount of time that was required to locate and use the passage structures. Sea Lampreys from both release groups were equally likely to approach the entrance to the fishway upon returning to Milford Dam, despite the fishway being located against the eastern shore of the river. However, high flows shortly after release may have resulted in higher attraction to the fishway in 2020. Passage success at dams upstream of Milford was highly variable. All Sea Lamprey were able to successfully navigate past West Enfield Dam (100% passage, n = 63), whereas Brownsmill Dam apparently acted as a complete barrier to further migration (0% passage, n = 7). Fish from all years and release groups together had a median upstream migration distance of 38.8 km after fish had passed Milford Dam, and a maximum observed upstream travel distance of approximately 100 km, indicating that most tagged Sea Lamprey ended their migration in the vicinity of a dam.
Conclusion
The results of this study indicate that Sea Lamprey have high passage efficiency at the Milford Dam and highlight areas within the Penobscot River basin—such as the Brownsmill Dam—where passage facilities are currently inadequate for Sea Lamprey.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10919","usgsCitation":"Peterson, E., Thors, R., Frechette, D., and Zydlewski, J.D., 2023, Adult Sea Lamprey approach and passage at the Milford Dam fishway, Penobscot River, Maine, United States: Journal of Fisheries Management, v. 43, no. 4, p. 1052-1065, https://doi.org/10.1002/nafm.10919.","productDescription":"14 p.","startPage":"1052","endPage":"1065","ipdsId":"IP-147299","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467098,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10919","text":"Publisher Index Page"},{"id":466001,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Milford Dam fishway, Penobscot River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.65399945645208,\n 44.94347505924921\n ],\n [\n -68.65399945645208,\n 44.93937668118309\n ],\n [\n -68.64306273135283,\n 44.93937668118309\n ],\n [\n -68.64306273135283,\n 44.94347505924921\n ],\n [\n -68.65399945645208,\n 44.94347505924921\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Peterson, Erin","contributorId":347938,"corporation":false,"usgs":false,"family":"Peterson","given":"Erin","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":922756,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thors, Rex","contributorId":347941,"corporation":false,"usgs":false,"family":"Thors","given":"Rex","affiliations":[{"id":83269,"text":"Maine Seagrant","active":true,"usgs":false}],"preferred":false,"id":922757,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frechette, Danielle","contributorId":347942,"corporation":false,"usgs":false,"family":"Frechette","given":"Danielle","affiliations":[{"id":68617,"text":"Maine Department of Marine Resources","active":true,"usgs":false}],"preferred":false,"id":922758,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":922759,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70247706,"text":"sir20235067 - 2023 - Geology and assessment of coal resources for the Cherokee coal bed in the Fort Union Formation, south-central Wyoming","interactions":[],"lastModifiedDate":"2026-03-09T16:56:31.872382","indexId":"sir20235067","displayToPublicDate":"2023-08-14T17:00:00","publicationYear":"2023","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":"2023-5067","displayTitle":"Geology and Assessment of Coal Resources for the Cherokee Coal Bed in the Fort Union Formation, South-Central Wyoming","title":"Geology and assessment of coal resources for the Cherokee coal bed in the Fort Union Formation, south-central Wyoming","docAbstract":"The Cherokee coal bed is a locally thick and laterally continuous coal bed in the Overland Member of the Paleocene Fort Union Formation in south-central Wyoming. It represents a significant resource that is easily accessible and may be extractable through both surface and underground mining methods. A database of more than 600 data points, comprising coalbed methane wells, coal exploration drill holes, and measured sections, was compiled from a previously released geologic database and reinterpreted to provide a more detailed geologic model for the Cherokee coal bed. The thickest part of the Cherokee coal bed lies along the crest of the Wamsutter arch, an east-west trending anticlinal feature that separates the Great Divide subbasin to north from the Washakie subbasin to the south. The Cherokee coal bed consists of several laterally persistent benches separated by partings that range in thickness from one inch to greater than 100 feet. A series of detailed geologic cross sections through the study area show both the structural geology and the distribution and areal extent of the individual coal benches of the Cherokee coal bed.
Data generated from the geologic model were used in stochastic geostatistical analyses to estimate the remaining or in-place coal resources. Certain parameters, as described later in the text, were applied to calculate available coal resources for surface and underground mining. This study is part of an ongoing process by the U.S. Geological Survey (USGS) to transition from a distance-based approach to a probabilistic approach for determining uncertainty in coal resource assessment. This probabilistic approach uses quantitative statistical methods to determine the potential range of uncertainty in coal resource estimates, whereas the distance-based approach does not provide any mathematical method to determine the range of uncertainty. Using stochastic geostatistical methods, utilizing 100 realizations or gridding iterations of the data, in-place resources were calculated, with a 90 percent probability, to be 15.261 ± 0.464 billion short tons (bst). Available coal resources tonnages were calculated using separate sets of criteria for surface and underground mining methods, based on probable mining parameters. Tonnage values were calculated based on estimated coal densities determined from available coal quality data. Available coal resources that meet the parameters for surface mining methods were calculated, with a 90 percent probability, to be 0.813 ± 0.038 bst.
Available coal resources that meet the parameters for underground mining methods were calculated, with a 90 percent probability, to be 2.393 ± 0.055 bst. The calculations were based on estimates of the resources that meet the parameters for the optimum mining of the thickest coal benches of the Cherokee coal bed. This is depicted in a series of cross sections through the study area that show projected underground mining horizons in the Cherokee coal bed, based on the thickest combinations of individual coal benches.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20235067","programNote":"Energy Resources Program","usgsCitation":"Shaffer, B.N., and Olea, R.A., 2023, Geology and assessment of coal resources for the Cherokee coal bed in the Fort Union Formation, south-central Wyoming: U.S. Geological Survey Scientific Investigations Report 2023–5067, 29 p., https://doi.org/10.3133/sir20235067.","productDescription":"Report: vii, 30 p.; 6 Figures: 36.00 x 24.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-132141","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":419765,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92K1UT6","text":"USGS data release","linkHelpText":"Cherokee coal bed drill hole data from the Fort Union Formation in the Little Snake River coal field and Red Desert area, Wyoming"},{"id":419763,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5067/coverthb2.jpg"},{"id":419764,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067.pdf","text":"Report","size":"6.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067"},{"id":419766,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig07.pdf","text":"Figure 7","size":"108 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 7"},{"id":419768,"rank":6,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig09.pdf","text":"Figure 9","size":"104 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 9"},{"id":419769,"rank":7,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig16.pdf","text":"Figure 16","size":"96 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 16"},{"id":419770,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig17.pdf","text":"Figure 17","size":"104 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 17"},{"id":419771,"rank":9,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig18.pdf","text":"Figure 18","size":"104 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 18"},{"id":419767,"rank":5,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067_fig08.pdf","text":"Figure 8","size":"104 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5067 Figure 8"},{"id":420209,"rank":10,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5067/images"},{"id":420210,"rank":11,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5067/sir20235067.xml"},{"id":420217,"rank":12,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235067/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5067"},{"id":500948,"rank":13,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115199.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wyoming","otherGeospatial":"Cherokee Coal Bed, Fort Union Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.3333,\n 42\n ],\n [\n -108.3333,\n 41.4167\n ],\n [\n -107.5,\n 41.4167\n ],\n [\n -107.5,\n 42\n ],\n [\n -108.3333,\n 42\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046