The first reported invasion of Asian swamp eels (Monopterus albus, ASE) in the continental United States was in the state of Georgia in 1994. This population was first discovered within several ponds on a private nature centre, but the ponds drained via an outflow pipe into marsh habitats along the Chattahoochee River. Our objective was to delineate the current invasion extent of ASE in the Chattahoochee River, Georgia, by sampling juvenile ASE within an occupancy modelling framework. We sampled 111 and 100 sites in 2015 and 2016 respectively, on 10 occasions, each within a 2-km radius of the purported invasion point to estimate the spatial extent of their invasion in this system. Leaf-litter traps (LLTs) were effective at documenting an increase in the invasion extent of ASE, from within 100 m of the Chattahoochee Nature Center pond outflow to 1.6 km away. Documenting the extent of invasion of this population has proven elusive in the past, but the use of LLTs to target juvenile eels has documented a larger invasion extent than previously known in the study system. The results of this research can be used to develop effective control and management strategies, such as locating potential breeding areas for targeted removal sampling.
Hinkley Valley, in the central to western Mojave Desert of southeastern California, has a long historical record owing to its position as a crossroads for rail and road traffic and its position adjacent to the Mojave River. Subflow in the Mojave River provided groundwater recharge that maintained water consumption and demand by way of shallow wells for local agriculture in the valley. Its crossroads position led to construction of several power-transmission lines, pipeline, and communications cable routes that transect Hinkley Valley. One of these, a natural gas pipeline and its associated compressor station, was the locus of hexavalent chromium, Cr(VI), released into, and consequent contamination of, groundwater. Understanding the movement and fate of the contaminants is a complex hydrologic and geochemical problem. Geologic mapping of the Hinkley Valley area provides framework elements for use in resolving this problem. This report provides new information on surface and subsurface geology to better constrain the origin and geometry of hydrologically important deposits in the Hinkley Valley area and describes youthful faults that may control sediment distribution and groundwater flow. The geologic map (sheet 1) presents substantial new information on surficial geology, including Pliocene deposits, but does not contain significant new work on bedrock. Bedrock investigations were specific to identifying youthful faults and representative outcrops for rocks that were penetrated by boreholes in the valley. Special attention was placed on locating and describing youthful faults. In addition, we analyzed gravity data to (1) map horizontal gradients that we interpret to reflect long-term fault traces and to (2) estimate the depth to bedrock, which is defined as Miocene and older intrusive and metamorphic rocks for the purposes of this report. The subsurface geology of Hinkley Valley was investigated by examining borehole sediment cores and rock encountered at the base of the sediment section. We analyzed the core to determine depositional environments, provenance, and age of the sediment that infilled the valley. Valleys, mountains, and basins in the Hinkley Valley area are topographically complex and incompletely named. The nearly flat floored Hinkley Valley slopes gently northward. It is framed by Mount General and the informally named “Hinkley hills” (southeast of Mount General) on the northeast and by Iron Mountain and Lynx Cat Mountain on the southwest, although breaks in the western mountains allow stream connections between Hinkley Valley and another valley to the west that is herein referred to as Hawes valley. At its south end, Hinkley Valley is traversed by the entrenched Mojave River, which passes east out of the valley past Barstow. North of Hinkley Valley, a few low hills (including Red Hill) separate the valley from a broad west-sloping piedmont that is part of the physiographic Harper Basin (of which the Harper Lake playa is the center). The lower part of this piedmont, however, is referred to as Water Valley, although it is not a distinct valley. The name derives from groundwater sourced from subflow in the Mojave River, which caused shallow water and even artesian flow in Water Valley but not in other parts of the Harper Basin. When water filled the Harper Basin to form Pleistocene Lake Harper it not only submerged Water Valley but also northern Hinkley Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3458","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board and the State Water Resources Control Board","usgsCitation":"Miller, D.M., Langenheim, V.E., and Haddon, E.K., 2020, Geologic map and borehole stratigraphy of Hinkley Valley and vicinity, San Bernardino County, California: U.S. Geological Survey Scientific Investigations Map 3458, pamphlet 23 p., 2 sheets, scale 1:24,000, https://doi.org/10.3133/sim3458.","productDescription":"Pamphlet,: iv, 23 p.; 2 Sheets ; 2 Tables; Database; Data Release; Metadata","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102109","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":381271,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_sheet2.pdf","text":"Sheet 2","size":"32 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381270,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_sheet1.pdf","text":"Sheet 1","size":"40 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381269,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_database.zip","text":"Database","size":"7.5 MB","linkFileType":{"id":6,"text":"zip"}},{"id":381268,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_base.zip","text":"Base","size":"1.25 GB","linkFileType":{"id":6,"text":"zip"}},{"id":381267,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_metadata.txt","size":"10 KB","linkFileType":{"id":2,"text":"txt"}},{"id":381266,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_pamphlet.pdf","text":"Pamphlet","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":381451,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FV5LG5","linkHelpText":"Gravity data of the Hinkley area, southern California"},{"id":381273,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_table_7.xlsx","text":"Table 7","size":"60 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":381265,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3458/covrthb.jpg"},{"id":381272,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3458/sim3458_table_3.xlsx","text":"Table 3","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"}}],"country":"United States","state":"California","county":"San Bernadino County","otherGeospatial":"Hinkley Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.26257324218749,\n 34.80647431931937\n ],\n [\n -117.06619262695312,\n 34.80647431931937\n ],\n [\n -117.06619262695312,\n 35.060352812431496\n ],\n [\n -117.26257324218749,\n 35.060352812431496\n ],\n [\n -117.26257324218749,\n 34.80647431931937\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
A three-dimensional groundwater-flow model was developed for the aquifer system of Long Island, New York, to evaluate (1) responses of the hydrologic system to changes in natural and anthropogenic hydraulic stresses, (2) the subsurface distribution of groundwater age, and (3) the regional-scale distribution of groundwater travel times and the source of water to fresh surface waters and coastal receiving waters. The model also provides the groundwater flow components used to define model boundaries for possible inset models used for local-scale analyses.
The three-dimensional, groundwater flow model developed for this investigation uses the numerical code MODFLOW–NWT to represent steady-state conditions for average groundwater pumping and aquifer recharge for 2005–15. The particle-tracking algorithm MODPATH, which simulates advective transport in the aquifer, was used to estimate groundwater age, delineate the areas at the water table that contribute recharge to coastal and freshwater bodies, and estimate total travel times of water from the water table to discharge locations.
A three-dimensional, 1-meter (3.3-foot) topobathymetric model was used to determine land-surface altitudes for the island and seabed altitudes for the surrounding coastal waters. The mapped extents and surface altitudes of major geologic units were compiled and used to develop a three-dimensional hydrogeologic framework of the aquifer system, including aquifers and confining units. Lithologic data from deep boreholes and previous aquifer-test results were used to estimate the three-dimensional distribution of hydraulic conductivity in principal aquifers. Natural recharge from precipitation was estimated for 2005–15 using a modified Thornthwaite-Mather methodology as implemented in a soil-water balance model. Components of anthropogenic recharge—wastewater return flow, storm water inflow, and inflow from leaky infrastructure—also were estimated for 2005–15. Groundwater withdrawals for various sources, including public water supply, industrial, remediation, and agricultural, were compiled or estimated for the same period.
These data were incorporated into the model development to represent the aquifer system geometry, boundaries, and initial hydraulic properties of the regional aquifers and confining units within the Long Island aquifer system. Average hydraulic conditions—water levels and streamflows—for 2005–15 were estimated using existing data from the U.S. Geological Survey National Water Information System database. Model inputs were adjusted to best match average hydrologic conditions using inverse methods as implemented in the parameter-estimating software PEST. The calibrated model was used to simulate average hydrologic conditions in the aquifer system for 2005–15.
About 656 cubic feet per second of water was withdrawn on average annually for 2005–15 for water supply and an average of about 349 cubic feet per second of water recharged the aquifer annually from return flow and leaky infrastructure. Parts of New York City have drawdowns exceeding 25 feet, mostly because of urbanization and associated large decreases in recharge rates. Large areas in the western part of the island have drawdowns exceeding 10 feet, mostly from large groundwater withdrawals and sewering, which largely eliminates wastewater return flow. Water-table altitudes in eastern parts of the island increased by more than 2 feet in some areas as a result of wastewater return flow in unsewered areas and changes in land use. Changes in streamflows show a similar pattern as water-table altitudes. Streamflows generally decrease in western parts of the island where there are large drawdowns and increase in eastern parts of the island where water-table altitudes increase.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205091","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Walter, D.A., Masterson, J.P., Finkelstein, J.S., Monti, J., Jr., Misut, P.E., and Fienen, M.N., 2020, Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15: U.S. Geological Survey Scientific Investigations Report 2020–5091, 75 p., https://doi.org/10.3133/sir20205091.","productDescription":"Report: ix, 75 p.; 3 Data Releases","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112206","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":381521,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5091/images/"},{"id":381195,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.pdf","text":"Report","size":"35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5091"},{"id":381194,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5091/coverthb2.jpg"},{"id":381192,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P954DLLC","text":"USGS data release","linkHelpText":"Aquifer texture data describing the Long Island aquifer system"},{"id":381191,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KWQSEJ","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH6 used to simulate groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15"},{"id":381190,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90B6OTX","text":"USGS data release","linkHelpText":"Time domain electromagnetic surveys collected to estimate the extent of saltwater intrusion in Nassau and Queens Counties, New York, October-November 2017"},{"id":381520,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.XML"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.102783203125,\n 40.55554790286311\n ],\n [\n -73.7017822265625,\n 40.49709237269567\n ],\n [\n -72.8778076171875,\n 40.65147128144057\n ],\n [\n -72.037353515625,\n 40.91351257612758\n ],\n [\n -71.6143798828125,\n 41.16211393939692\n ],\n [\n -71.9384765625,\n 41.178653972331674\n ],\n [\n -72.191162109375,\n 41.236511201246216\n ],\n [\n -72.65808105468749,\n 41.11660732012896\n ],\n [\n -73.4490966796875,\n 40.967455873296714\n ],\n [\n -73.751220703125,\n 40.89275342420696\n ],\n [\n -74.0203857421875,\n 40.718119379753446\n ],\n [\n -74.102783203125,\n 40.55554790286311\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Subduction zones drive plate tectonics on Earth, yet subduction initiation and the related upper plate depositional and structural kinematics remain poorly understood because upper plate records are rare and often strongly overprinted by magmatism and deformation. During the late Paleozoic time, Laurentia’s western margin was truncated by a sinistral strike-slip fault that transformed into a subduction zone. Thick Permian strata in the Inyo Mountains of central-eastern California record this transition. Two basins that were separated by a transpressional antiform contain sedimentary lithofacies that record distinct patterns of shoaling and deepening conditions before and during tectonism associated with subduction initiation. Sandstone petrography and lithofacies analysis show that rocks in a southeastern basin are dominated by carbonate grains derived from adjacent carbonate shelves, whereas sandstones in a northwestern basin are predominantly quartzose with likely derivation from distant ergs or underlying strata. Detrital zircon spectra from all but the youngest strata in both basins are typical of Laurentian continent spectra with prominent peaks that indicate ultimate sources in Appalachia, Grenville, Yavapai/Mazatzal, and the Wyoming or Superior cratons. The first Cordilleran arc-derived detrital zircon grains appear in the uppermost strata of the northwestern basin and record Late Permian (ca. 260 Ma) Cordilleran arc magmatism at this approximate latitude, and a possible source area is suggested by geochemical similarities between these detrital zircons and broadly coeval magmatic zircons in the El Paso Mountains to the southwest. Deformation responsible for basin partitioning is consistent with sinistrally oblique contraction in the earliest Permian time. The data presented from the Inyo Mountains shed more light on the nature of Cordilleran subduction initiation and the upper-crustal response to this transition.
Biological conservation is a fundamental purpose of the National Park system, and Catoctin Mountain Park (CATO) supports high-quality habitat for native fishes in the headwaters of the Chesapeake Bay watershed in eastern North America. However, native Blue Ridge sculpin (Cottus caeruleomentum) have been extirpated in Big Hunting Creek above Cunningham Falls in CATO. Prior research indicates that infection by the fungal-like protist Dermocystidium is a likely cause for the extirpation, but elevated stream temperatures also have been observed in the study area, and it remains unknown whether thermal stress may exacerbate infections or otherwise limit habitat suitability for fishes in CATO.
The purpose of this study was to quantify spatial variation in summer stream temperatures and to evaluate the effects of temperature on sculpin growth rates and susceptibility to Dermocystidium infection. We used observational and experimental methods to address these objectives. First, we deployed stream temperature gages at 10 sites throughout the study area to assess hourly and daily temperatures during the summer of 2019. Second, we conducted an in situ fish enclosure experiment at five of the temperature sites to assess fish growth and susceptibility to Dermocystidium infection over a 45-day exposure period. For this experiment we collected sculpin from a stream in CATO that supports a robust population of Blue Ridge sculpin (Owens Creek) and held them in quarantine for 50 days in the Experimental Stream Laboratory at the U.S. Geological Survey (USGS) Leetown Science Center. Pre-exposure histopathology confirmed the absence of Dermocystidium infection prior to the introduction of fish into experimental enclosures.
We found that stream temperatures were warmer where sculpin have been extirpated than elsewhere in CATO where sculpin persist. However, the fish enclosure experiment revealed a positive effect of temperature on fish growth, suggesting that increased food availability and foraging rates compensated for increased metabolic demands in the warmest sites. Moreover, fish held in enclosures did not develop Dermocystidium infection. Our results therefore suggest that current environmental conditions in upper Big Hunting Creek may be suitable for Blue Ridge sculpin reintroduction, and this could ultimately lead to sportfishing opportunities by increasing the forage base for native brook trout (Salvelinus fontinalis).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201137","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Hitt, N.P., Kessler, K.G., Kelly, Z.A., Rogers, K.M., Macmillan, H.E., and Walsh, H.L., 2020, Assessing native fish restoration potential in Catoctin Mountain Park: U.S. Geological Survey Open-File Report 2020–1137, 17 p., https://doi.org/10.3133/ofr20201137.","productDescription":"Report: vii, 17 p.; Data Release","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122955","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":381222,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P950A13P","text":"USGS data release","linkHelpText":"Stream temperature and sculpin growth data collected from Catoctin Mountain Park in 2019"},{"id":381221,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1137/ofr20201137.pdf","text":"Report","size":"4.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1137"},{"id":381220,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1137/coverthb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Catoctin Mountain Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.51781463623047,\n 39.60621720230201\n ],\n [\n -77.38151550292969,\n 39.60621720230201\n ],\n [\n -77.38151550292969,\n 39.70137566512028\n ],\n [\n -77.51781463623047,\n 39.70137566512028\n ],\n [\n -77.51781463623047,\n 39.60621720230201\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
White-nose syndrome (WNS) is an emerging infectious disease of hibernating bats caused by a fungus previously known as Geomyces destructans and reclassified as Pseudogymnoascus destructans. The disease was first documented in 2006 in New York, has since spread across much of eastern North America, and as of January 2012, had caused the death of at least 5.7 to 6.7 million bats. Previous studies have suggested that environmental conditions play a strong role in WNS mortality. However, to predict where and when the disease will spread to new sites is difficult because detailed site information and associated environmental data are notably sparse. This paper presents a chronology of where and when WNS was detected in North America before October 2011 and indicates who reported the infections. This paper also presents available data on WNS-infected site elevation, geology, sediment chemistry and biota, air temperature, and relative humidity.
By the end of September 2011, at least 241 known WNS-infected sites were in North America and the number of infected sites per winter season had increased each year since 2006. The progressive increase in the number of infected sites per winter season suggests that the number of WNS infections had not peaked as of the 2010–11 winter season. WNS-infected sites include caves and mines, but the sites are not restricted by elevation, lithology, or strata age. Available data on site sediment chemistry are sparse but present a wide range of values, suggesting that caves and mines may contain a great range of microenvironments that are still poorly understood. The distribution of WNS may be restricted by air temperature and relative humidity. Published air temperature values from WNS-infected sites range from −15 to 33 degrees Celsius (but most temperature values are less than 20 degrees Celsius), and relative humidity values range from 50 to 100 percent. The spread of WNS may be restricted by a cave or mine temperature threshold of 20 degrees Celsius (which is likely to be south of most of the continental United States) and by some yet to be determined threshold of low relative humidity. These results indicate that WNS may not spread south into Mexico or to Puerto Rico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201117","usgsCitation":"Swezey, C.S., and Garrity, C.P., 2020, Environmental data associated with sites infected with white-nose syndrome (WNS) before October 2011 in North America: U.S. Geological Survey Open-File Report 2020–1117, 67 p., https://doi.org/10.3133/ofr20201117.","productDescription":"x, 67 p.","numberOfPages":"67","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-117667","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":381184,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1117/coverthb.jpg"},{"id":381185,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1117/ofr20201117.pdf","text":"Report","size":"19.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1117"}],"country":"Canada, United States","state":"Connecticut, Delaware, Indiana, Kentucky, Maine, Maryland, Massachsetts, Missouri, New Brunswick, New Hampshire, New Jersey, New York, North Carolina, Nova Scotia, Ohio, Oklahoma, Ontario, Pennsylvania, Quebec, Tennessee, Vermont, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.95898437499999,\n 33.797408767572485\n ],\n [\n -75.673828125,\n 35.88905007936091\n ],\n [\n -74.53125,\n 39.436192999314095\n ],\n [\n -73.65234375,\n 40.78054143186033\n ],\n [\n -70.13671875,\n 42.16340342422401\n ],\n [\n -64.775390625,\n 43.389081939117496\n ],\n [\n -59.4140625,\n 46.31658418182218\n ],\n [\n -65.390625,\n 49.724479188712984\n ],\n [\n -66.0498046875,\n 54.521081495443596\n ],\n [\n -79.4970703125,\n 54.62297813269033\n ],\n [\n -89.296875,\n 56.77680831656842\n ],\n [\n -95.44921875,\n 52.669720383688166\n ],\n [\n -95.2294921875,\n 49.03786794532644\n ],\n [\n -89.7802734375,\n 48.37084770238366\n ],\n [\n -83.935546875,\n 46.9502622421856\n ],\n [\n -81.9580078125,\n 43.70759350405294\n ],\n [\n -83.5400390625,\n 41.64007838467894\n ],\n [\n -86.8798828125,\n 41.83682786072714\n ],\n [\n -87.451171875,\n 41.57436130598913\n ],\n [\n -88.24218749999999,\n 37.47485808497102\n ],\n [\n -89.2529296875,\n 36.56260003738545\n ],\n [\n -90.04394531249999,\n 35.24561909420681\n ],\n [\n -85.5615234375,\n 34.95799531086792\n ],\n [\n -82.880859375,\n 34.994003757575776\n ],\n [\n -82.265625,\n 35.15584570226544\n ],\n [\n -81.01318359375,\n 35.08395557927643\n ],\n [\n -79.716796875,\n 34.77771580360469\n ],\n [\n -78.486328125,\n 33.815666308702774\n ],\n [\n -77.95898437499999,\n 33.797408767572485\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
New U-Pb and 40Ar/39Ar ages integrated with geologic mapping and observations across the western Alaska Range constrain the distribution and tectonic setting of Cretaceous to Oligocene magmatism along an evolving accretionary plate margin in south-central Alaska. These rocks were emplaced across basement domains that include Neoproterozoic to Jurassic carbonate and siliciclastic strata of the Farewell terrane, Triassic and Jurassic plutonic and volcanic rocks of the Peninsular terrane, and Jurassic and Cretaceous siliciclastic strata of the Kahiltna assemblage. Plutonic rocks of different ages also host economic mineralization including intrusion-related Au, porphyry Cu-Mo-Au, polymetallic veins and skarns, and peralkaline intrusion-related rare-earth elements. The oldest intrusive suites were emplaced ca. 104–80 Ma into the Peninsular terrane only prior to final accretion. Deformation of the northern Kahiltna succession and underlying Farewell terrane occurred at ca. 97 Ma, and more widespread deformation ca. 80 Ma involved south-vergent folding and thrusting of the Kahiltna assemblage that records collisional accretion of the Peninsular-Wrangellia terrane and juxtaposition of sediment wedges formed on the inboard and outboard terranes. More widespread magmatism ca. 75–55 Ma occurred in two general pulses, each having distinct styles of localized deformation. Circa 75–65 Ma plutons were emplaced in a transpressional setting and stitch the accreted Peninsular and Wrangellia terranes to the Farewell terrane. Circa 65–55 Ma magmatism occurred across the entire range and extends for more than 200 km inboard from the inferred position of the continental margin. The Paleocene plutonic suite generally reflects shallower emplacement depths relative to older suites and is associated with more abundant andesitic to rhyolitic volcanic rocks. Deformation ca. 58–56 Ma was concentrated along two high-strain zones, the most prominent of which is 1 km wide, strikes east-northeast, and accommodated dextral oblique motion. Emplacement of widespread intermediate to mafic dikes ca. 59–51 Ma occurred before a notable magmatic lull from ca. 51–44 Ma reflecting a late Paleocene to early Eocene slab window. Magmatism resumed ca. 44 Ma, recording the transition from slab window to renewed subduction that formed the Aleutian-Meshik arc to the southwest. In the western Alaska Range, Eocene magmatism included emplacement of the elongate north-south Merrill Pass pluton and large volumes of ca. 44–37 Ma andesitic flows, tuffs, and lahar deposits. Finally, a latest Eocene to Oligocene magmatic pulse involved emplacement of a compositionally variable but spatially concentrated suite of magmas ranging from gabbro to peralkaline granite ca. 35–26 Ma, followed by waning magmatism that coincided with initiation of Yakutat shallow-slab subduction. Cretaceous to Oligocene magmatism throughout the western Alaska Range collectively records terrane accretion, translation, and integration together with evolving subduction dynamics that have shaped the southern Alaska margin since the middle Mesozoic.
The northern long‐eared bat (Myotis septentrionalis) is one of the bat species most affected by white‐nose syndrome. Population declines attributed to white‐nose syndrome contributed to the species’ listing as federally threatened under the 1973 Endangered Species Act. Although one of the most abundant Myotine bats in eastern North America prior to white‐nose syndrome, little is known about northern long‐eared bats in the upper Midwest, USA. We assessed the habitat associations of the northern long‐eared bats on a regional scale using occupancy models that accounted for uncertainty in nightly detection to provide needed information on the distribution as white‐nose syndrome has recently arrived in this area. We monitored bat activity using zero‐crossing frequency‐division bat detectors for 10–15 nights at 20 detector sites at each of 3 sampling areas in Michigan, USA, and 6 sampling areas in Wisconsin, USA, stratified by mesic and xeric habitat types. We constructed northern long‐eared bat nightly detection histories for our occupancy analysis using maximum likelihood estimates from 2 commercially‐available automated identification programs: Kaleidoscope and Echoclass. We sampled for a total of 2,174 detector‐nights. Both Kaleidoscope and Echoclass identified northern long‐eared bat passes on 110 detector‐nights, whereas on 1,968 detector‐nights neither program identified a northern long‐eared bat call. Only one program or the other identified northern long‐eared bat calls on 206 detector‐nights, indicating an overall agreement rate of 35% on nights when calls were detected. We analyzed these data using an occupancy analysis accounting for the potential for false positives to assess the relationship between northern long‐eared bat presence and habitat characteristics. Our analyses indicated that the probability of a false positive at a site was low (0.015; 95% CI 0.009–0.021), and detection probability, but not occupancy, declined from 2015 to 2016 for sites in Wisconsin sampled in both years. Occupancy was positively associated with distance into the forest interior (distance from nearest road).
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1138","usgsCitation":"Hyzy, B.A., Russell, R., Silvis, A., Ford, W., Riddle, J.D., and Russell, K.R., 2020, Occupancy and detectability of northern long-eared bats in the Lake States Region: Wildlife Society Bulletin, v. 44, no. 4, p. 732-740, https://doi.org/10.1002/wsb.1138.","productDescription":"9 p.","startPage":"732","endPage":"740","ipdsId":"IP-095702","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":381445,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.52734374999999,\n 42.53689200787315\n ],\n [\n -87.802734375,\n 42.601619944327965\n ],\n [\n -87.6708984375,\n 44.574817404670306\n ],\n [\n -87.802734375,\n 45.042478050891546\n ],\n [\n -87.03369140625,\n 45.73685954736049\n ],\n [\n -85.4736328125,\n 46.07323062540835\n ],\n [\n -85.869140625,\n 46.649436163350245\n ],\n [\n -86.7041015625,\n 46.45299704748289\n ],\n [\n -88.00048828124999,\n 46.9502622421856\n ],\n [\n -88.9453125,\n 46.965259400349275\n ],\n [\n -90.37353515625,\n 46.63435070293566\n ],\n [\n -90.98876953125,\n 46.63435070293566\n ],\n [\n -90.76904296874999,\n 46.9052455464292\n ],\n [\n -91.97753906249999,\n 46.7248003746672\n ],\n [\n -92.28515625,\n 45.321254361171476\n ],\n [\n -91.0546875,\n 44.071800467511565\n ],\n [\n -90.52734374999999,\n 42.53689200787315\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Hyzy, Brenna A.","contributorId":171457,"corporation":false,"usgs":false,"family":"Hyzy","given":"Brenna","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":806603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, Robin E. 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":219536,"corporation":false,"usgs":true,"family":"Russell","given":"Robin E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":806604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Silvis, Alexander","contributorId":171585,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","email":"","affiliations":[{"id":26923,"text":"Virginia Polytechnic Institute, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":806605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. 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Importantly distinct from the abundant and widespread eastern subspecies (Progne subis subis), the western subspecies is of particular concern to Federal forest managers. Whereas the eastern subspecies is almost entirely dependent on artificial human-provided housing, the western subspecies continues to rely on natural cavities for nesting habitat (Bettinger, 2003). Accurate estimates of the regional abundance of the western purple martin are difficult to obtain; the most recent statewide census for Oregon, conducted in 2005, estimated the population at 1,100 pairs (Western Purple Martin Working Group, 2010). Several factors, including a small population size, loss of breeding habitat, and reductions in the number of suitable nesting sites have put populations of the western purple martin at risk throughout much of the Pacific Northwest region (Rockwell, 2019).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201130","usgsCitation":"Hagar, J.C., and Branch, E.C., 2020, Western purple martin (Progne subis arboricola) occurrence on the Siuslaw National Forest, summer 2019: U.S. Geological Survey Open-File Report 2020-1130, 25 p., https://doi.org/10.3133/ofr20201130.","productDescription":"iv, 25 p.","onlineOnly":"Y","ipdsId":"IP-117452","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":380937,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1130/coverthb.jpg"},{"id":380938,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1130/ofr20201130.pdf","text":"Report","size":"18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1130"}],"country":"United States","state":"Oregon","otherGeospatial":"Siuslaw National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.14001464843749,\n 43.691707903073805\n ],\n [\n -123.42041015624999,\n 43.691707903073805\n ],\n [\n -123.42041015624999,\n 44.66865287227321\n ],\n [\n -124.14001464843749,\n 44.66865287227321\n ],\n [\n -124.14001464843749,\n 43.691707903073805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Mercury is an environmentally ubiquitous neurotoxin, and its methylated form presents health risks to humans and other biota, primarily through dietary intake. Because methylmercury bioaccumulates and biomagnifies in living tissue, concentrations progressively increase at higher trophic positions in ecosystem food webs. Therefore, the greatest health risks are for organisms at the highest trophic positions and for humans who consume organisms such as fish from these high trophic positions. Data on environmental mercury concentrations in various media and biota provide a basis for comparison among sites and regions and for evaluating ecosystem health risks. The U.S. Geological Survey, in cooperation with the Natural Resources Department, Bad River Band of Lake Superior Chippewa, have compiled a dataset from analyses of mercury concentrations in surface water, bed sediment, fish tissue, Rana clamitans (green frog) tissue, Haliaeetus leucocephalus (bald eagle) feathers, Lontra canadensis (North American river otter) hair, Zizania palustris (northern wild rice), and litterfall from samples collected in the Bad River watershed, Wisconsin during 2004–18. These data originated from either the Natural Resources Department or another agency based on samples collected within or near to Bad River Tribal lands before transfer to the U.S. Geological Survey for compilation and analysis at the onset of the project. This report describes the compiled mercury dataset, provides comparisons to similar measurements in the region and elsewhere, and evaluates health risks to humans and to the sampled biota. Except for litterfall, data were not collected on a consistent, regular basis over a sufficient period to evaluate temporal patterns. The reported mercury concentrations are generally similar to those reported elsewhere in the upper Great Lakes region. Reported values are consistent with atmospheric deposition as the principal source and reflect a favorable environment for mercury methylation. Fish mercury concentrations increased at higher food web positions and generally increased with length in most species measured. Sander vitreus (walleye) present the greatest risk to humans among fishes considered here because of their high trophic position and associated elevated mercury concentrations in combination with relatively high walleye consumption rates by the Native American community. Methylmercury concentrations in wild rice are generally low and likely pose little health risk. Despite reports of declining atmospheric mercury deposition across eastern North America during the past decade, a downward trend in litterfall mercury deposition was not evident in samples collected during 2012–18. Limitations in this data compilation and analysis were noted due to missing information such as collection dates and site locations for some samples. Regular monitoring of mercury in litterfall and surface waters along with periodic collection of fish would enable evaluation of temporal change in the mercury cycle that might affect future risk to humans and aquatic ecosystem inhabitants.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201095","collaboration":"Prepared in cooperation with the Natural Resources Department, Bad River Band of Lake Superior Chippewa","usgsCitation":"Burns, D.A., 2020, Compilation of mercury data and associated risk to human and ecosystem health, Bad River Band of Lake Superior Chippewa, Wisconsin (ver 1.1, December 2020): U.S. Geological Survey Open-File Report 2020–1095, 19 p., https://doi.org/10.3133/ofr20201095.","productDescription":"Report: vii, 19 p.; Database; Data Release","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-110861","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":377882,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- USGS water data for the Nation"},{"id":377880,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1095/ofr20201095.pdf","text":"Report","size":"1.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1095"},{"id":377879,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1095/coverthb2.jpg"},{"id":377881,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HRS2C3","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Mercury data from the Bad River Watershed, Wisconsin, 2004–2018"},{"id":380931,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2020/1095/versionHist.txt","size":"448 B","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Wisconsin","otherGeospatial":"Bad River Tribal Lands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.48751831054686,\n 46.54658317951774\n ],\n [\n -90.4779052734375,\n 46.57868671298067\n ],\n [\n -90.51223754882812,\n 46.599449464868584\n ],\n [\n -90.59600830078125,\n 46.63057868059483\n ],\n [\n -90.69488525390625,\n 46.69184147024343\n ],\n [\n -90.78140258789062,\n 46.71632714994794\n ],\n [\n -90.7855224609375,\n 46.66734468444288\n ],\n [\n -90.83221435546875,\n 46.62020426357956\n ],\n [\n -90.8294677734375,\n 46.57774276255591\n ],\n [\n -90.83770751953125,\n 46.39619977845332\n ],\n [\n -90.55343627929688,\n 46.409457767475764\n ],\n [\n -90.54931640625,\n 46.54280504427768\n ],\n [\n -90.48751831054686,\n 46.54658317951774\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: August 2020; Version 1.1: December 2020","contact":"Director, New York Water Science Center
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Neonicotinoids are one of the most widely used classes of insecticides in the world. The neonicotinoid imidacloprid is commonly applied to hemlock (Tsuga spp.) stands in eastern North America to reduce tree mortality from infestations of the invasive hemlock woolly adelgid (HWA; Adelges tsugae). While laboratory and mesocosm studies have determined that imidacloprid can bioaccumulate in anurans and cause sublethal effects, no field studies have investigated whether salamanders or insects in streams adjacent to HWA treatments bioaccumulate imidacloprid or if sublethal effects are detectable in wild salamanders. We assessed relationships between imidacloprid exposure and stream salamander health in West Virginia, USA, using concentration of the stress hormone corticosterone and body condition indices (BCI) as response variables. Of 107 Desmognathus salamanders from 11 sites tested for bioaccumulation, we detected imidacloprid in 47 salamanders. Of 15 benthic macroinvertebrate samples tested, we detected imidacloprid, imidacloprid-urea, and imidacloprid-olefin in 15, 13, and 1 sample, respectively. Based on 115 Desmognathus salamanders sampled at 11 sites for stress hormone responses, corticosterone concentration increased with imidacloprid concentration in stream water. For 802 salamanders sampled at 48 sites, BCI decreased as concentration of imidacloprid in stream water increased, but explanatory power was low. Our study suggests that chronic leaching of imidacloprid from treated hemlock stands into adjacent streams has the potential to negatively affect aquatic organisms and may provide a route of exposure to higher trophic levels.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2020.e01292","usgsCitation":"Crayton, S.M., Wood, P.B., Brown, D., Millikin, A., McManus, T.J., Simpson, T.J., Ku, K., and Park, Y., 2020, Bioaccumulation of the pesticide imidacloprid in stream organisms and sublethal effects on salamanders: Global Ecology and Conservation, v. 24, p. 1-15, https://doi.org/10.1016/j.gecco.2020.e01292.","productDescription":"e01292, 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-112478","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":454705,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2020.e01292","text":"Publisher Index Page"},{"id":395902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Gauley River National Recreational Area, Monongahela National 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]\n}","volume":"24","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Crayton, Sara M.","contributorId":244780,"corporation":false,"usgs":false,"family":"Crayton","given":"Sara","email":"","middleInitial":"M.","affiliations":[{"id":48971,"text":"West Virginia University, Division of Forestry and Natural Resources, Morgantown, WV","active":true,"usgs":false}],"preferred":false,"id":834692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Petra B. 0000-0002-8575-1705 pbwood@usgs.gov","orcid":"https://orcid.org/0000-0002-8575-1705","contributorId":199090,"corporation":false,"usgs":true,"family":"Wood","given":"Petra","email":"pbwood@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Donald J.","contributorId":264847,"corporation":false,"usgs":false,"family":"Brown","given":"Donald J.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":834693,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Millikin, Alice R.","contributorId":276266,"corporation":false,"usgs":false,"family":"Millikin","given":"Alice R.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":834694,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McManus, Terence J.","contributorId":278590,"corporation":false,"usgs":false,"family":"McManus","given":"Terence","email":"","middleInitial":"J.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":834820,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Simpson, Tyler J.","contributorId":276267,"corporation":false,"usgs":false,"family":"Simpson","given":"Tyler","email":"","middleInitial":"J.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":834695,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ku, Kang-Mo","contributorId":276268,"corporation":false,"usgs":false,"family":"Ku","given":"Kang-Mo","email":"","affiliations":[{"id":56944,"text":"Chonnam National University","active":true,"usgs":false}],"preferred":false,"id":834696,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Park, Yong-Lak","contributorId":276271,"corporation":false,"usgs":false,"family":"Park","given":"Yong-Lak","email":"","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":834697,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70216480,"text":"sir20205123 - 2020 - Water levels and selected water-quality conditions in the Mississippi River Valley alluvial aquifer in eastern Arkansas, 2014","interactions":[],"lastModifiedDate":"2020-12-01T13:46:47.314594","indexId":"sir20205123","displayToPublicDate":"2020-12-01T05:43:03","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5123","displayTitle":"Water Levels and Selected Water-Quality Conditions in the Mississippi River Valley Alluvial Aquifer in Eastern Arkansas, 2014","title":"Water levels and selected water-quality conditions in the Mississippi River Valley alluvial aquifer in eastern Arkansas, 2014","docAbstract":"In 2014, the U.S. Geological Survey, in cooperation with the Arkansas Geological Survey and the Arkansas Natural Resources Commission, determined water-level altitudes in 468 wells in eastern Arkansas and collected water-quality samples from 144 wells. Water-level altitudes were calculated based on the measured depth to water in each well and used to construct a potentiometric-surface map of the Mississippi River Valley alluvial aquifer, and the water-quality samples were analyzed for chloride and bromide concentrations. Upon completion of the potentiometric-surface map, 10 depressions in the potentiometric surface were identified in the Mississippi Alluvial Plain: two large depressions, five small depressions, and three areas of decreased water levels. Analyses of water-quality samples identified several areas of elevated chloride/bromide ratios.
A water-level altitude difference map was constructed using 345 groundwater levels measured in 2010 and 2014. Differences in water-level altitude ranged from –10.2 feet in Craighead County to 18.00 feet in Prairie County. Analysis of the overall water-level altitude differences indicated a decline in approximately 84 percent of the wells measured in both 2010 and 2014, including in areas where previous studies indicated water-level altitude increases between 2008 and 2012. Analysis of long-term hydrographs of wells in the study area indicated that mean annual water levels declined in all but two counties. The decline in water levels observed in the hydrographs suggests continued growth of the cones of depression caused by groundwater use in the Mississippi River Valley alluvial aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205123","collaboration":"Prepared in cooperation with the Arkansas Natural Resources Commission and the Arkansas Geological Survey","usgsCitation":"Rodgers, K.D., and Whaling, A.R., 2020, Water levels and selected water-quality conditions in the Mississippi River Valley alluvial aquifer in eastern Arkansas, 2014: U.S. Geological Survey Scientific Investigations Report 2020–5123, 22 p., 3 pls., https://doi.org/10.3133/sir20205123.","productDescription":"Report: v, 22 p.; 3 Plates: 20.04 x25.98 inches or smaller; Data Release","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-082198","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":380660,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5123/coverthb.jpg"},{"id":380661,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5123/sir20205123.pdf","text":"Report","size":"962 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5123"},{"id":380662,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5123/sir20205123_plate1.pdf","text":"Plate 1","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5123 Plate 1","linkHelpText":"— Potentiometric Surface of the Mississippi River Valley Alluvial Aquifer, Spring 2014"},{"id":380664,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5123/sir20205123_plate3.pdf","text":"Plate 3","size":"1.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5123 Plate 3","linkHelpText":"— Chloride/Bromide Ratio of the Mississippi River Valley Alluvial Aquifer, Spring 2014–15"},{"id":380663,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5123/sir20205123_plate2.pdf","text":"Plate 2","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5123 Plate 2","linkHelpText":"— Difference in Water Level of the Mississippi River Valley Alluvial Aquifer, 2010–14"},{"id":380665,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F757197T","text":"USGS data release","description":"USGs data Release","linkHelpText":"Water-level data, selected water-quality data, and the potentiometric dataset for the Mississippi River Valley alluvial aquifer in eastern Arkansas, spring 2014"}],"country":"United States","state":"Arkansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.85693359375,\n 36.527294814546245\n ],\n [\n -91.62597656249999,\n 35.24561909420681\n ],\n [\n -92.21923828124999,\n 34.74161249883172\n ],\n [\n -91.8017578125,\n 33.94335994657882\n ],\n [\n -91.62597656249999,\n 33.22949814144951\n ],\n [\n -91.60400390625,\n 33.04550781490999\n ],\n [\n -91.01074218749999,\n 33.08233672856376\n ],\n [\n -90.72509765625,\n 34.07086232376631\n ],\n [\n -90.3076171875,\n 34.88593094075317\n ],\n [\n -89.97802734375,\n 35.460669951495305\n ],\n [\n -89.67041015625,\n 36.03133177633187\n ],\n [\n -90.3076171875,\n 36.049098959065645\n ],\n [\n -90.06591796875,\n 36.33282808737917\n ],\n [\n -90.15380859375,\n 36.491973470593685\n ],\n [\n -90.52734374999999,\n 36.50963615733049\n ],\n [\n -90.85693359375,\n 36.527294814546245\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
1) Movement has been studied extensively in stream salmonids, and most data suggest that population-level behavior is best described by a leptokurtic distribution. This distribution emphasizes the large proportion of sedentary individuals in a population, which can implicitly lead to assumptions of low population connectivity and overlook the ecological significance of rare individuals with more mobile phenotypes. 2) We report findings of a multi-season radio telemetry study conducted on four adjacent populations of wild brook trout (Salvelinus fontinalis) connected by Loyalsock Creek in northcentral Pennsylvania. We used these data to investigate temporal and spatial patterns in movement and fitness tradeoffs associated with behavioral phenotype. 3) Similar to previous studies, we found that 59 of the 120 radio-tagged individuals (49%) were sedentary and moved less than 200 m. Only 18% of individuals dispersed more than 1 km, but the maximum distanced moved exceeded 13 km. We also found that mobile individuals had significantly higher summer and fall survival than did sedentary fish, which could indicate that there are fitness benefits associated with vagility. 4) Most long-distance movements were the result of fish migrating from small tributaries into a larger mainstem river in the days after spawning. Therefore, even though mobility was only expressed for a short duration and by relatively few individuals in the population, the behavior appears to maintain metapopulation connectivity throughout the watershed. 5) Our study highlights the ecological significance of rare phenotypes for population demography across large spatial scales and the need to understand movement across multiple temporal and spatial scales to ensure adequate conservation of critical forms of cryptic life history diversity.
","language":"English","publisher":"Wiley","doi":"10.1111/fwb.13637","usgsCitation":"Wagner, T., and White, S., 2020, Behavior at short temporal scales drives dispersal dynamics and survival in a metapopulation of brook trout (Salvelinus fontinalis): Freshwater Biology, v. 66, no. 2, p. 278-285, https://doi.org/10.1111/fwb.13637.","productDescription":"8 p.","startPage":"278","endPage":"285","ipdsId":"IP-118703","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":454727,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/fwb.13637","text":"Publisher Index Page"},{"id":395892,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Double Creek, East Branch Creek, Loyalsock Creek, Pole Bridge Creek, Shanerburg Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.93528175354004,\n 41.254193933121606\n ],\n [\n -76.92240715026855,\n 41.254193933121606\n ],\n [\n -76.92240715026855,\n 41.266646415620784\n ],\n [\n -76.93528175354004,\n 41.266646415620784\n ],\n [\n -76.93528175354004,\n 41.254193933121606\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"66","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Shannon","contributorId":276311,"corporation":false,"usgs":false,"family":"White","given":"Shannon","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":834736,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216479,"text":"ofr20201116 - 2020 - Multiple-well monitoring site adjacent to the North and South Belridge Oil Fields, Kern County, California","interactions":[],"lastModifiedDate":"2020-11-25T12:52:01.362381","indexId":"ofr20201116","displayToPublicDate":"2020-11-24T12:43:43","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1116","displayTitle":"Multiple-Well Monitoring Site Adjacent to the North and South Belridge Oil Fields, Kern County, California","title":"Multiple-well monitoring site adjacent to the North and South Belridge Oil Fields, Kern County, California","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the California State Water Resources Control Board, is evaluating several questions about oil and gas development and groundwater resources in California, including (1) the location of groundwater resources; (2) the proximity of oil and gas operations to groundwater and the geologic materials between them; (3) evidence (or no evidence) of fluids from oil and gas sources in groundwater; and (4) the pathways or processes responsible when fluids from oil and gas sources are present in groundwater (U.S. Geological Survey, 2017). As part of this evaluation, the USGS installed a multiple-well monitoring site in the southern San Joaquin Valley groundwater basin adjacent to the North and South Belridge oil fields, about 7 miles southwest of Lost Hills, California. Data collected at the Belridge multiple-well monitoring site (BWSD) provide information about the geology, hydrology, geophysical properties, and geochemistry of the aquifer system, thus enhancing understanding of relations between adjacent groundwater and the North and South Belridge oil fields in an area where there are few groundwater data. This report presents construction information for the BWSD and initial hydrogeologic data collected from the site. A similar site installed to the east of the Lost Hills oil field, 11.5 miles to the north of the BWSD site, was described by Everett and others (2020a).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201116","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Everett, R.R., Brown, A.A., Gillespie, J.M., Kjos, A., and Fenton, N.C., 2020, Multiple-well monitoring site adjacent to the North and South Belridge Oil Fields, Kern County, California: U.S. Geological Survey Open-File Report 2020-1116, 10 p., https://doi.org/10.3133/ofr20201116.","productDescription":"Report: 10 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-112077","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":380658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1116/ofr20201116.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1116"},{"id":380659,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96WITX5","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Aquifer test data for the Belridge multiple-well monitoring site (BWSD), Kern County, California"},{"id":380657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1116/coverthb.jpg"}],"country":"United States","state":"California","county":"Kern County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-120.1945,35.788],[-120.1842,35.789],[-120.1655,35.7891],[-120.1474,35.7887],[-120.0816,35.7886],[-119.9688,35.7896],[-119.852,35.7891],[-119.7618,35.7906],[-119.6472,35.7895],[-119.5395,35.79],[-119.4301,35.7905],[-119.3308,35.7899],[-119.2169,35.7906],[-119.1182,35.7903],[-118.9027,35.789],[-118.6504,35.7897],[-118.6441,35.7896],[-118.5885,35.7897],[-118.5233,35.7892],[-118.4785,35.7915],[-118.4706,35.7919],[-118.4502,35.7908],[-118.2716,35.7896],[-118.2562,35.7894],[-118.2387,35.7897],[-118.2137,35.7894],[-118.1956,35.7896],[-118.1632,35.7893],[-118.0839,35.7865],[-118.0697,35.7859],[-118.009,35.7861],[-117.9234,35.7863],[-117.9249,35.7986],[-117.9005,35.7983],[-117.8738,35.7988],[-117.8523,35.7985],[-117.6362,35.7958],[-117.6355,35.7086],[-117.6537,35.7085],[-117.6527,35.6776],[-117.6176,35.6775],[-117.6166,35.6493],[-117.6353,35.6487],[-117.6354,35.6233],[-117.6352,35.5807],[-117.6356,35.5666],[-117.6351,35.5639],[-117.6346,35.4472],[-117.6352,35.3755],[-117.6353,35.3464],[-117.6351,35.3319],[-117.6343,35.3174],[-117.6341,35.3028],[-117.6345,35.2874],[-117.6343,35.2742],[-117.6341,35.2588],[-117.6339,35.2447],[-117.6342,35.2302],[-117.634,35.2157],[-117.6338,35.2011],[-117.6336,35.1861],[-117.6334,35.1707],[-117.6338,35.1562],[-117.6336,35.1417],[-117.6333,35.1271],[-117.6331,35.1126],[-117.6329,35.098],[-117.6352,35.0981],[-117.636,35.0872],[-117.6358,35.0727],[-117.6356,35.0581],[-117.6357,35.0295],[-117.6361,35.015],[-117.6357,34.985],[-117.6351,34.8233],[-117.6519,34.8227],[-117.6704,34.8221],[-117.7757,34.8229],[-118.1408,34.8195],[-118.1493,34.8195],[-118.5995,34.8175],[-118.8946,34.8181],[-118.8945,34.818],[-118.8825,34.791],[-118.9772,34.7902],[-118.9771,34.8126],[-119.2462,34.8147],[-119.2461,34.857],[-119.2797,34.858],[-119.2779,34.8793],[-119.3844,34.8794],[-119.385,34.884],[-119.3849,34.899],[-119.4382,34.8999],[-119.4438,34.8999],[-119.4544,34.8999],[-119.4571,34.9],[-119.4746,34.9004],[-119.4746,34.9005],[-119.4746,34.9136],[-119.474,34.9367],[-119.474,34.9499],[-119.474,34.9576],[-119.474,34.9721],[-119.4746,35.0184],[-119.4746,35.0325],[-119.4745,35.077],[-119.4908,35.077],[-119.4914,35.092],[-119.5004,35.0915],[-119.5088,35.0906],[-119.5628,35.0883],[-119.5583,35.1369],[-119.5566,35.1601],[-119.5549,35.1791],[-119.5769,35.1787],[-119.6095,35.1773],[-119.6675,35.1749],[-119.6675,35.1908],[-119.6675,35.2049],[-119.6688,35.2617],[-119.7397,35.2629],[-119.7572,35.2633],[-119.7746,35.2633],[-119.8113,35.2641],[-119.8122,35.3508],[-119.8815,35.3501],[-119.8824,35.41],[-119.8824,35.4246],[-119.8831,35.4377],[-119.9999,35.4396],[-120.0007,35.4695],[-120.0171,35.469],[-120.0194,35.4835],[-120.0358,35.4834],[-120.0359,35.497],[-120.0523,35.4974],[-120.053,35.5124],[-120.0699,35.5128],[-120.0711,35.5268],[-120.0875,35.5276],[-120.0876,35.6139],[-120.1951,35.6151],[-120.1947,35.7481],[-120.1942,35.7626],[-120.1945,35.788]]]},\"properties\":{\"name\":\"Kern\",\"state\":\"CA\"}}]}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
This report presents abundance and occurrence data for three species of ambystomad salamanders (Ambystoma maculatum, A. jeffersonianum, and A. opacum) collected over a 3-year period (2000, 2001, and 2002) at 200 potentional breeding sies within the Delaware Water Gap National Recreation Area (DEWA). In addition, numerous measures of inpond, near-pond, and landscape attributes were measured and used to inform statistical models to determine species-habitat relationships in the DEWA.
The results of a 3-year study of ambystomatid salamander breeding habits and habitats in the (DEWA) that was conducted by the U.S. Geological Survey, in cooperation with the National Park Service, are described in the report. The objectives of the study were to document the population status and critical breeding habitats of the three species of ambystomatid salamanders known to be present in the DEWA—Ambystoma maculatum (spotted salamander), A. opacum (marbled salamander), and A. jeffersonianum (Jefferson salamander). DEWA managers are interested in ecological information on these species for several reasons. First, at the time the study began, there was little known regarding the status of pond-breeding amphibians and their habitats in the DEWA. Second, because they require undegraded habitats in both terrestrial and aquatic habitats to successfully complete their life cycles, the status of ambystomatid salamanders is widely viewed as indicative of overall ecosystem health. Third, because ambystomatid salamanders and other pond-breeding amphibians have been observed in numerous artificial impoundments with the DEWA, park managers would like to assess whether dismantling or discontinuing maintenance of artificial impoundments could affect pond-breeding amphibians and possibly other species that use pond or wetland habitats in the Park.
In 2001, 2002, and 2003, the size and location of 200 wetlands, ponds, and artificial impoundments, and related landscape positions (Ridge versus Valley; Pennsylvania side versus New Jersey side of the Delaware river) were mapped, and site habitat data relating to salamander occurrence and abundance patterns were collected. The data collected during this study provide important new baseline information on ambystomatid salamanders and wetland habitats in the DEWA that will enhance long-term inventory and monitoring efforts. In addition, breeding habitat assessments indicate that ambystomatid salamanders may be sensitive to a wide variety of stresses important in the DEWA and in the region. In particular, recent trends in development (for example, roads) in and near the DEWA, regional increases in the acidity of precipitation, and predicted long-term warming trends for the region could be detrimental to pond-breeding salamander populations because of their effects on breeding site quality and quantity, and on the integrity of migration corridors. In contrast, the results of the study indicate management plans to eliminate small impoundments are not likely to adversely affect salamanders in DEWA, at least in the short-term. However, it is possible that these small impoundments may offer stable habitats that provide a rescure effect during long-term droughts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205081","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Snyder, C.D., Young, J.A., Julian, J.T., King, T.L., and Julian, S.E., 2020, Assessment of Ambystomatid salamander populations and their breeding habitats in the Delaware Water Gap National Recreation Area: U.S. Geological Survey Scientific Investigations Report 2020–5081, 41 p., https://doi.org/10.3133/sir20205081.","productDescription":"Report: viii, 41 p.; Data Release","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113175","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":380510,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5081/coverthb.jpg"},{"id":380511,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5081/sir20205081.pdf","text":"Report","size":"3.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5081"},{"id":380512,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XCVHY3","text":"USGS data release","linkHelpText":"Ambystomatid salamander population and breeding pond habitat data for the Delaware Water Gap National Recreation Area (2001–2003)"}],"country":"United States","state":"New Jersey, Pennsylvania","otherGeospatial":"Delaware Water Gap National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.7564697265625,\n 41.380930388318\n ],\n [\n -74.8992919921875,\n 41.29844430929419\n ],\n [\n -74.9761962890625,\n 41.18278832811288\n ],\n [\n -75.1080322265625,\n 41.06692773019345\n ],\n [\n -75.179443359375,\n 40.992337919312305\n ],\n [\n -75.1629638671875,\n 40.93011520598305\n ],\n [\n -75.0970458984375,\n 40.93841495689795\n ],\n [\n -74.893798828125,\n 41.075210270566636\n ],\n [\n -74.6630859375,\n 41.253032440653186\n ],\n [\n -74.7564697265625,\n 41.380930388318\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
11649 Leetown Road
Kearneysville, WV 25430
The U.S. Geological Survey, in cooperation with the Bernalillo County Public Works Division, conducted a 1-year study in 2015 to assess the spatial and temporal distribution of evapotranspiration (ET) and available water within the East Mountain area in Bernalillo County, New Mexico. ET and available water vary spatiotemporally because of complex interactions among environmental factors, including vegetation characteristics, soil characteristics, topography, and climate.
Precipitation data from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (P) were used in conjunction with actual ET (ETa) data from the Operational Simplified Surface Energy Balance (SSEBop) model to estimate available water (P – ETa) at 100-meter (m) resolution in the study area. Maps, descriptive statistics, boxplots, regression analyses (continuous data), and multiple comparison tests (categorical data) were used to characterize P, ETa, and available water and their relations to topographic, soil, and vegetation datasets in the East Mountain area. Five categories of the natural land-cover type (evergreen forest, shrub, herbaceous, deciduous forest, and mixed forest) and four categories of developed land-cover type specific to residential intensity (developed open, developed low, developed medium, and developed high) were analyzed individually and in interaction with multiple elevation, tree canopy, and soil texture classes.
Annual mean P in 2015 in the East Mountain area was 608 millimeters (mm), and annual mean ETa was 543 mm (89 percent of annual P in 2015), indicating that in 2015, a spatial mean of about 65 mm of water was available for runoff, soil moisture replenishment, or groundwater recharge. Monthly ETa was greatest in July and smallest in January. The intervening months did not show smooth temporal or consistent spatial changes from month to month. Months with lower ETa (January to March, October to December) also tended to have greater available water, indicating that soil moisture (water supply) and potential ET (water demand) may have been out of phase.
Regression analyses showed that monthly ETa data had the highest correlation with annual ETa among the atmospheric, topographic, soil, or vegetation datasets, particularly during the early and late growing season (March, April, May, and September). In contrast, monthly P was highly variable and not as highly correlated with annual ETa. Among landscape variables, correlations with annual ETa were highest for tree canopy cover (coefficient of determination [R2] = 0.46). Correlations between ETa and other landscape variables were lower (R2 = 0.06–0.19): available soil water in the top 100 centimeters, soil bulk density of layer 1, slope, sand content of soil layer 1, soil depth, available soil water in the top 25 centimeters, leaf area index, aspect eastness, and elevation. Evergreen forest areas had the highest annual median ETa, followed by mixed forest, open residential areas, and deciduous forest. Available water typically was higher in landcover types with lower ETa: herbaceous cover, followed by deciduous forest, high-intensity developed areas, and shrub. Deciduous forest had the second highest median available water, despite having the fourth highest ETa, because deciduous forest had greater P than most other areas. Annual median ETa typically was greatest in the second highest elevation band (2,401–2,800 m above the North American Vertical Datum of 1988 [NAVD 88]), and lower in the highest elevation band (2,801–3,254 m above NAVD 88), despite having greater P, likely because of decreased tree canopy cover or a shift from evergreen to deciduous trees at the highest elevations.
Annual median ETa increased with tree canopy cover, regardless of landcover type. ETa correlation was higher with tree canopy than with leaf area index or normalized difference vegetation index. This result indicates that it is important to include the thermal band (from satellite multispectral data) in vegetation indices used to describe ETa, perhaps to account for the influence of energy limitation or water limitation on ET. Of all natural landcover types, finer soils had the most available water, whereas coarser soils had the least available water. Relations of soil type with P – ETa were different than with ETa, indicating ET and available water have a complex response to differences in soil type. Further modeling would be useful in determining soils’ infiltration, storage, conductivity, and plant-water availability relations to individual storms for each position in the landscape, as well as the corresponding effects of these processes on ET and available water.
The best multivariate linear model for annual ETa had an R2 value of 0.62. Monthly ETa models had R2 values between 0.16 and 0.65. Models usually, but not always, performed best during the growing season. These results indicate that even the best multivariate linear models cannot explain a notable amount of the variability in ET. The monthly ETa models with the highest correlations (August and September) followed a July having almost twice the mean precipitation for July (1981–2010), which indicates that a soil-moisture variable is needed to more accurately model monthly ETa. Further study is needed to better characterize this system, the variables that affect ET and available water, and the partitioning of available water into runoff, soil moisture storage, and groundwater recharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205095","collaboration":"Prepared in cooperation with the Bernalillo County Public Works Division","usgsCitation":"Douglas-Mankin, K.R., McCutcheon, R.J., Mitchell, A.C., and Senay, G.B., 2020, Landscape and climatic influences on actual evapotranspiration and available water using the Operational Simplified Surface Energy Balance (SSEBop) Model in eastern Bernalillo County, New Mexico, 2015: U.S. Geological Survey Scientific Investigations Report 2020–5095, 40 p., https://doi.org/10.3133/sir20205095.","productDescription":"x, 40 p.","numberOfPages":"53","onlineOnly":"Y","ipdsId":"IP-101269","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":380594,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5095/sir20205095.pdf","text":"Report","size":"3.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5095"},{"id":380593,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5095/coverthb.jpg"}],"country":"United States","state":"New Mexico","county":"Bernalillo County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.65252685546875,\n 34.879171662167664\n ],\n [\n -105.88623046874999,\n 34.879171662167664\n ],\n [\n -105.88623046874999,\n 35.35545618392078\n ],\n [\n -106.65252685546875,\n 35.35545618392078\n ],\n [\n -106.65252685546875,\n 34.879171662167664\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
The movement patterns of birds across a landscape are often highly variable and influenced by complex interactions between individuals and environments. Because periods of movement can be marked by high mortality, especially among juvenile birds, understanding these patterns may be vital for the conservation of many bird species. However, these patterns can be challenging to quantify. We used radio‐telemetry to document the movement patterns of ‘Akohekohe (Palmeria dolei), an endangered Hawaiian Honeycreeper endemic to Maui Island, Hawai'i. This species is believed to be highly susceptible to mosquito‐transmitted avian malaria (Plasmodium relictum) and only breeds in high‐elevation wet forests on the windward side of east Maui (> 1715 m) that serve as mosquito‐free refugia. Over a 2‐yr period (2013–2014), we used radio‐telemetry and resightings of color‐banded birds to track the movements of juveniles (N = 11) and adults (N = 24) and quantified home ranges with minimum convex polygons (MCP) and 95% fixed kernels (KHR). Movement patterns and home range sizes of adult and juvenile ‘Akohekohe were significantly different, with adults having relatively small home ranges (0.57 ha, MCP; 1.09 ha, KHR) and juveniles moving greater distances and having larger home ranges (25.02 ha, MCP; 90.56 ha, KHR). Only juveniles moved into lower‐elevation forests that can support mosquito populations, at least seasonally. The absence of adults in this transitional malaria zone suggests that adult ‘Akohekohe cannot maintain long‐term home ranges in areas with an increased risk of malaria infection. In addition, the long‐distance movements of juveniles during the post‐fledging, pre‐breeding period likely increases their risk of contracting avian malaria and could be a key factor limiting the population of this species.
Mercury (Hg) pollution is an environmental problem that adversely affects human and ecosystem health at local, regional, and global scales—including within New York State. More than two-thirds of the Hg currently released to the environment originates, either directly or indirectly, from human activities. Since the early 1800s, global atmospheric Hg concentrations have increased by three- to eight-fold over natural levels. In the U.S., atmospheric emissions and point-source releases to waterways increased following industrialization into the mid-1980s. Since then, water discharges have largely been curtailed. As a result, Hg emissions, atmospheric concentrations, and deposition over the past few decades have declined across the eastern U.S. Despite these decreases, Hg pollution persists. To inform policy efforts and to advance public understanding, the New York State Energy Research and Development Authority (NYSERDA) sponsored a scientific synthesis of information on Hg in New York State. This effort includes 23 papers focused on Hg in atmospheric deposition, water, fish, and wildlife published in Ecotoxicology. New York State experiences Hg contamination largely due to atmospheric deposition. Some landscapes are inherently sensitive to Hg inputs driven by the transport of inorganic Hg to zones of methylation, the conversion of inorganic Hg to methylmercury, and the bioaccumulation and biomagnification along food webs. Mercury concentrations exceed human and ecological risk thresholds in many areas of New York State, particularly the Adirondacks, Catskills, and parts of Long Island. Mercury concentrations in some biota have declined in the Eastern Great Lakes Lowlands and the Northeastern Highlands over the last four decades, concurrent with decreases in water releases and air emissions from regional and U.S. sources. However, widespread changes have not occurred in other ecoregions of New York State. While the timing and magnitude of the response of Hg levels in biota varies, policies expected to further diminish Hg emissions should continue to decrease Hg concentrations in food webs, yielding benefits to the fish, wildlife, and people of New York State. Anticipated improvements in the Hg status of aquatic ecosystems are likely to be greatest for inland surface waters and should be roughly proportional to declines in atmospheric Hg deposition. Efforts that advance recovery from Hg pollution in recent years have yielded significant progress, but Hg remains a pollutant of concern. Indeed, due to this extensive compilation of Hg observations in biota, it appears that the extent and intensity of the contamination on the New York landscape and waterscape is greater than previously recognized. Understanding the extent of Hg contamination and recovery following decreases in atmospheric Hg deposition will require further study, underscoring the need to continue existing monitoring efforts.
Twelve unnamed, bedrock stratigraphic units are recognized within the Sleetmute A-2 1:63,360-scale quadrangle of southwestern Alaska. These units range in age from late(?) Proterozoic through Devonian and can be divided into two distinct facies belts: (1) a southern facies of dominantly shallow-water platform carbonate and minor siliciclastic rocks (including Early Ordovician–Early Devonian platform edge algal buildups) with subordinate transgressive tongues of deeper-water platy carbonates; and (2) a northern facies belt of approximately age equivalent deep-water carbonate and siliciclastic rocks deposited in slope and basinal environments. Both facies belts belong to the Farewell terrane of Decker and others (1994). Two structural provinces are also recognized, which correspond directly with these belts. The Farewell terrane is interpreted as a continental margin sequence that rifted from Siberia. Many of the bedrock units recognized in the Sleetmute A-2 quadrangle are equivalent to units previously recognized to the east and northeast in the Lime Hills, McGrath, and Medfra quadrangles. Shallow-water carbonate platform rocks make up the majority of the southern facies and occur primarily along the crest and north side (and to a lesser degree along the south side) of a prominent crescentic-shaped, east-west trending anticlinal axis exposed in the southern part of the Sleetmute A-2 quadrangle. Because of the relatively low thermal alteration indices of the rocks of this area and the presence of highly porous dolostone intervals of good reservoir quality in the platform facies, this region elicited interest for petroleum exploration in the 1980s. However, low total organic carbon (TOC) content of potential source rocks within the Ordovician–Silurian basinal facies belt indicates low petroleum resource potential for this area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3450","usgsCitation":"Blodgett, R.B., Wilson, F.H., Shew, N.B., and Clough, J.G., 2020, Bedrock geologic map of the 15' Sleetmute A-2 quadrangle, southwestern Alaska: U.S. Geological Survey Scientific Investigations Map 3450, 18 p., 1 map sheet, scale 1:63,360, https://doi.org/10.3133/sim3450.","productDescription":"Pamphlet: iv, 18 p.; 1 Sheet: 23.20 x 26.17 inches; Table; Spatial Data","onlineOnly":"Y","ipdsId":"IP-098211","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":380271,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3450/sim3450.pdf","text":"Sheet 1","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3450"},{"id":380272,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3450/sim3450_pamphlet.pdf","text":"Pamphlet","size":"1.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3450 Pamphlet"},{"id":380270,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3450/coverthb.jpg"},{"id":380273,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3450/sim3450_table.csv","text":"Table 1","size":"10 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIM 3450 Table csv"},{"id":380274,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3450/sim3450_table.xls","text":"Table 1","size":"39 KB xls","description":"SIM 3450 Table xls"},{"id":380275,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3450/sim3450_spatial_data.zip","text":"SIM 3450 spatial data","size":"2.2 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3450 Spatial Data"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.45,\n 61.000\n ],\n [\n -156.2230,\n 61.000\n ],\n [\n -156.2230,\n 61.15\n ],\n [\n -156.45,\n 61.15\n ],\n [\n -156.45,\n 61.000\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Increasing specific conductance (SC) and chloride concentrations [Cl] negatively affect many stream ecosystems. We characterized spatial variability in SC, [Cl], and exceedances of Environmental Protection Agency [Cl] criteria using nearly 30 million high-frequency observations (2–15 min intervals) for SC and modeled [Cl] from 93 sites across three regions in the eastern United States: Southeast, Mid-Atlantic, and New England. SC and [Cl] increase substantially from south to north and within regions with impervious surface cover (ISC). In the Southeast, [Cl] weakly correlates with ISC, no [Cl] exceedances occur, and [Cl] concentrations are constant with time. In the Mid-Atlantic and New England, [Cl] and [Cl] exceedances strongly correlate with ISC. [Cl] criteria are frequently exceeded at sites with greater than 9–10% ISC and median [Cl] higher than 30–80 mg/L. Tens to hundreds of [Cl] exceedances observed annually at most of these sites help explain previous research where stream ecosystems showed changes at (primarily nonwinter) [Cl] as low as 30–40 mg/L. Mid-Atlantic chronic [Cl] exceedances occur primarily in December–March. In New England, exceedances are common in nonwinter months. [Cl] is increasing at nearly all Mid-Atlantic and New England sites with the largest increases at sites with higher [Cl].
Reduced abundance of non-native Tamarix shrubs in western U.S. riparian systems following biological control by a defoliating beetle has led to concerns that replacement plant communities could be dominated by other invasive species and/or not provide some of the ecosystem services that Tamarix was providing. In previous studies, Tamarix decline following biocontrol was accompanied by small increases in native and non-native herbaceous species, with variable responses of woody vegetation. However, none of these studies spanned periods longer than a decade since beetle release. This is an important caveat, given the cyclical nature of plant-herbivore interactions and potential lags in vegetation recovery. We report plant community response to an eight-year-long second cycle of Tamarix defoliation-refoliation in two reaches of the upper Colorado River in eastern Utah, 11–13 years after beetle arrival. Tamarix cover across sites initially declined an average of ca. 50% in response to the beetle, but then recovered. Changes in the associated plant community were small but supported common management goals, including a 47% average increase in cover of a native shrub (Salix exigua), and no secondary invasions by other non-native plants. We suggest that the effectiveness of biocontrol programs must be assessed case-by-case, and on a long-term basis.
Winter low‐flow (LF) conditions in streams provide a potential opportunity to evaluate the importance of legacy nitrate in catchments due to the dominance of slow‐flow transport pathways and lowered biotic activity. In this study, the concentration, flux, and trend of nitrate in streams during winter low‐flow conditions were analyzed at 320 sites in the conterminous United States. LF flow‐normalized nitrate concentrations varied from <0.1 to >20 mg‐N L‐1 and LF conditions contributed between 2% and 98% of the winter nitrate flux. LF nitrate concentrations generally exceeded 2.5 mg‐N L‐1 in the upper Midwest, with smaller regions of high LF nitrate concentrations in eastern Texas and along the northern mid‐Atlantic coast. Groundwater was inferred to be the primary or sole contributor of nitrate to streams during winter LF conditions at 140 of our 320 sites. Among these 140 sites, nitrate from groundwater comprised 45% or more of the winter nitrate flux at a quarter of the sites. Among the same 140 sites, concentrations of nitrate in streams during winter LF conditions generally increased between 2002 and 2012 at sites where 40% or more of the winter flux was from groundwater, suggesting that concentrations of nitrate in the contributing groundwater system were increasing. Using metrics developed herein, we characterize the potential importance of legacy nitrate at sites in this study and discuss methods to characterize sites with fewer samples than required by our models or at sites without continuous stream discharge measurements.
The city of Summerset is a growing community in west South Dakota. The Sun Valley Estates subdivision in the north part of the city was developed on unconsolidated deposits surrounded by steep terrain. During years with greater than normal precipitation, particularly in 2019, groundwater levels increased in the unconsolidated deposits and caused damage to stormwater systems, sewer infrastructure, and houses with basements. The U.S. Geological Survey, in cooperation with the City of Summerset, completed a study of the hydrogeology and groundwater flow in the alluvial aquifer part of the unconsolidated deposits in north Summerset to understand the groundwater system in the area and to provide hydrogeologic information in support of future development planning.
The study area included most of the Sun Valley Estates subdivision in the north part of the city of Summerset in the east Black Hills of west South Dakota. About 0.7 square mile of water-bearing alluvial deposits is included in the study area. Precipitation in the study area from 2017 to 2019 was compared to the monthly normal values at a nearby climate site. The largest departure from normal was in May 2019 with precipitation exceeding the monthly normal by about 5 inches (in.). All months in 2019, except March, exceeded the monthly normal precipitation. Cumulative departure from normal precipitation in 2019 increased from about 4 in. greater than normal in January to about 18 in. greater than normal in December.
The geologic setting of the study area is characterized by the surrounding Black Hills. Unconsolidated Quaternary-age deposits overlie consolidated to partially consolidated Mesozoic-age and Paleozoic-age shales, sandstones, and limestones. Surficial deposits of alluvium and other unconsolidated deposits are the primary surficial geologic units in the study area and form the components of the alluvium hydrogeologic unit of the study area. Results from previous studies of alluvium along nearby Rapid Creek estimated hydraulic conductivity to range from 89 to 2,292 feet per day (ft/d), transmissivity to range from 1,001 to 32,083 feet squared per day, and storage coefficients to range from 0.0002 to 0.16. Hydraulic conductivity and transmissivity generally decreased downstream along Rapid Creek (west to east). Slug tests were completed August 16, 2019, at two observation wells completed in the alluvial aquifer in the Sun Valley Estates subdivision to determine hydraulic conductivity. Hydraulic conductivity estimated from AQTESOLV curve-fitting analysis using the Bouwer-Rice method for all slug-in and slug-out trials from two observation wells in the study ranged from 0.20 to 0.26 ft/d for well 441318103220001 (SunValley1 well) and from 0.54 to 14 ft/d for well 441319103215701 (SunValley2 well). The mean, median, and standard deviation of all trials at both wells were 4.3 ft/d, 0.8 ft/d, and 5.6 ft/d, respectively.
The extent of the alluvial aquifer was determined by geologic maps and lithologic logs. Alluvial deposits in the study area extend to about 1 mile in the north–south direction and about 1.5 miles in the southeast–northwest direction. The direction of groundwater flow was estimated using water-level records and topographic maps. The resulting potentiometric map indicated that groundwater in the alluvial aquifer under the Sun Valley Estates subdivision originates from higher elevations of the west part of the area of interest and from streams in the southeast part. Recharge and evapotranspiration estimates were results from a Soil-Water Balance model that calculated a matrix of recharge for 2019 with values ranging from 0 to 11.4 in. and an annual mean value of 5.1 in. across the study area. Soil-Water Balance-estimated potential evapotranspiration for 2019 ranged from 28.90 to 28.75 in. and the estimated annual mean was 28.86 in. across the study area. Estimated groundwater budget components for the alluvial aquifer in the area of interest included inflows and outflows. Total estimated groundwater budget components for inflows for 2019 were about 66 percent from recharge, 33 percent from streamflow, and 1 percent from inflow from adjacent aquifers. Total estimated outflows were about 99-percent evapotranspiration and less than 1-percent outflow to adjacent aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205097","issn":"2328-0328","usgsCitation":"Eldridge, W.G., and Anderson, T.M., 2020, Hydrogeology and groundwater flow in alluvial deposits, north Summerset, South Dakota: U.S. Geological Survey Scientific Investigations Report 2020–5097, 31 p., https://doi.org/10.3133/sir20205097.","productDescription":"Report: vii, 31 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-116994","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":379700,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5097/coverthb.jpg"},{"id":379703,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","description":"USGS data release","linkHelpText":"USGS Water Data for the Nation"},{"id":379702,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TKVMXU","text":"USGS data release","description":"USGS data release","linkHelpText":"Soil-Water Balance model for alluvial deposits in Summerset, South Dakota"},{"id":379701,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5097/sir20205097.pdf","text":"Report","size":"5.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5097"}],"country":"United States","state":"South Dakota","city":"Sommerset","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.37310791015625,\n 44.15856343854312\n ],\n [\n -103.28109741210938,\n 44.15856343854312\n ],\n [\n -103.28109741210938,\n 44.203866109361435\n ],\n [\n -103.37310791015625,\n 44.203866109361435\n ],\n [\n -103.37310791015625,\n 44.15856343854312\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702