Double-crested cormorants (Nannopterum auritum) have been implicated as causes of fish population declines in many locations across their breeding range. Two challenges facing managers are identifying fisheries population metrics indicative of cormorant impacts and determining when this evidence becomes actionable. Building upon existing studies, we conducted a meta-analysis of eight data-rich systems across the Laurentian Great Lakes region of the United States for common fish population responses to changes in cormorant abundance. Specifically, we examined trends in mean total female length at age-3 (TL3), female mean length and age at 50 % maturity, and mean age evenness as indicated by Shannon’s Equitability Index. Annual observations for these metrics were independently regressed linearly against cormorant density by system for walleye (Sander vitreus), yellow perch (Perca flavescens), smallmouth bass (Micropterus dolomieu), and northern pike (Esox lucius) populations. TL3 was the most sensitive with 9 of the 14 datasets statistically significant (r2 range 0.29 to 0.86). Maturity metrics were moderately sensitive to trends in cormorant predation with mean total length at 50 % maturity significant in 4 out of 11 datasets (r2 range 0.27–0.41) and mean age at 50 % maturity significant in 3 out of 11 datasets (r2 range 0.12 – 0.51). Least sensitive was age evenness with the Shannon Index significant in 3 out of 12 datasets (r2 typically < 0.25). Of metrics tested, TL3 was the most reliable indicator of changes in cormorant effects despite varying system changes and management responses among locations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2022.08.022","usgsCitation":"Schultz, D.W., Dorr, B.S., Fielder, D.G., Jackson, J.R., and DeBruyne, R.L., 2022, Indicators of fish population responses to avian predation with focus on double-crested cormorants: Journal of Great Lakes Research, v. 48, no. 6, p. 1659-1668, https://doi.org/10.1016/j.jglr.2022.08.022.","productDescription":"10 p.","startPage":"1659","endPage":"1668","ipdsId":"IP-140185","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":467164,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2022.08.022","text":"Publisher Index Page"},{"id":406437,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Minnesota, New York","otherGeospatial":"Bays de Noc, East Lake Ontario, East Vermillion Lake, Lake 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Center","active":true,"usgs":false}],"preferred":false,"id":851265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fielder, David G.","contributorId":127535,"corporation":false,"usgs":false,"family":"Fielder","given":"David","email":"","middleInitial":"G.","affiliations":[{"id":7024,"text":"Michigan Department of Natural Resources, Fisheries Research Station","active":true,"usgs":false}],"preferred":false,"id":851266,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, James R.","contributorId":55709,"corporation":false,"usgs":false,"family":"Jackson","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":851267,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeBruyne, Robin L. 0000-0002-9232-7937 rdebruyne@usgs.gov","orcid":"https://orcid.org/0000-0002-9232-7937","contributorId":4936,"corporation":false,"usgs":true,"family":"DeBruyne","given":"Robin","email":"rdebruyne@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":851268,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70245165,"text":"70245165 - 2022 - Microgravity change during the 2008-2018 Kı̄lauea summit eruption: Nearly a decade of subsurface mass accumulation","interactions":[],"lastModifiedDate":"2023-06-19T18:30:40.041096","indexId":"70245165","displayToPublicDate":"2022-09-01T13:15:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Microgravity change during the 2008-2018 Kı̄lauea summit eruption: Nearly a decade of subsurface mass accumulation","docAbstract":"Results from nine microgravity campaigns from Kı̄lauea, Hawaiʻi, spanning most of the volcano's 2008–2018 summit eruption, indicate persistent mass accumulation at shallow levels. A weighted least squares approach is used to recover microgravity results from a network of benchmarks around Kı̄lauea's summit, eliminate instrumental drift, and restore suspected data tares. A total mass of 1.9 × 1011 kg was determined from these microgravity campaigns to have accumulated below Kı̄lauea Caldera during 2009–2015 at an estimated depth of 1.3 km below sea level. Only a fraction of this mass is reflected in surface deformation, and this is consistent with previously reported discrepancies between subsurface mass accumulation and observed surface deformation. The discrepancy, amongst other independent evidence from gas emissions, seismicity, and continuous gravimetry, indicate densification of magma in the reservoirs below the volcano summit. This densification may have been driven by degassing through the summit vent. It is hypothesized that during the final years of the summit eruption, magma densification resulted in a buildup of pressure in the reservoirs that may have contributed to the lower East Rift Zone outbreak of 2018. The observed mass accumulation beneath Kı̄lauea could not have been detected through other techniques and illustrates the importance of microgravity measurements in volcano monitoring.
","language":"English","publisher":"Wiley","doi":"10.1029/2022JB024739","usgsCitation":"Koymans, M.R., de Zeeuw-van Dalfsen, E., Evers, L.G., and Poland, M.P., 2022, Microgravity change during the 2008-2018 Kı̄lauea summit eruption: Nearly a decade of subsurface mass accumulation: Journal of Geophysical Research B: Solid Earth, v. 127, no. 9, e2022JB024739, 21 p., https://doi.org/10.1029/2022JB024739.","productDescription":"e2022JB024739, 21 p.","ipdsId":"IP-141029","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":446563,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2022jb024739","text":"External Repository"},{"id":418224,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Mount Kı̄lauea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.26044895266855,\n 19.401938903406503\n ],\n [\n -155.25103570167443,\n 19.40864710547018\n ],\n [\n -155.24852550140935,\n 19.418117037297506\n ],\n [\n -155.2541734520058,\n 19.422851796330775\n ],\n [\n -155.25772956904805,\n 19.429756405950712\n ],\n [\n -155.2677703701083,\n 19.432123633120014\n ],\n [\n -155.27467342083736,\n 19.43370176539787\n ],\n [\n -155.28157647156624,\n 19.43015094620857\n ],\n [\n -155.287851972229,\n 19.423049074962933\n ],\n [\n -155.29852032335577,\n 19.41713061180343\n ],\n [\n -155.29998460684362,\n 19.409633582437138\n ],\n [\n -155.29517338966895,\n 19.398979314550886\n ],\n [\n -155.28576013867482,\n 19.395427736863837\n ],\n [\n -155.27069893708423,\n 19.395427736863837\n ],\n [\n -155.26044895266855,\n 19.401938903406503\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"127","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Koymans, Mathijs R.","contributorId":236675,"corporation":false,"usgs":false,"family":"Koymans","given":"Mathijs","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":875727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Zeeuw-van Dalfsen, Elske 0000-0003-2527-4932","orcid":"https://orcid.org/0000-0003-2527-4932","contributorId":217967,"corporation":false,"usgs":false,"family":"de Zeeuw-van Dalfsen","given":"Elske","email":"","affiliations":[{"id":39727,"text":"KNMI","active":true,"usgs":false}],"preferred":false,"id":875728,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evers, Laslo G.","contributorId":310458,"corporation":false,"usgs":false,"family":"Evers","given":"Laslo","email":"","middleInitial":"G.","affiliations":[{"id":39727,"text":"KNMI","active":true,"usgs":false}],"preferred":false,"id":875729,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":875730,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237014,"text":"70237014 - 2022 - Great Lakes spatial priorities study","interactions":[],"lastModifiedDate":"2022-09-28T16:23:24.798526","indexId":"70237014","displayToPublicDate":"2022-09-01T11:17:12","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5134,"text":"NOAA Technical Memorandum","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NOS CS 51","title":"Great Lakes spatial priorities study","docAbstract":"Spatial data about the bathymetry, habitat characteristics, underlying geology, and other features of the ocean and inland seas are essential for decision-making. Marine research and management organizations use these data to help ensure safe navigation, promote sustainable fisheries, extract energy, and protect marine habitats in the coastal and ocean waters of the U.S. Exclusive Economic Zone (EEZ) and Laurentian Great Lakes. Many of these organizations may have overlapping or shared mapping interests without knowing it.
In a multi-jurisdictional planning environment, it can be challenging and cumbersome to determine where other entities have shared or overlapping mapping interests, especially across a transnational region such as the Great Lakes. State and provincial governments, federal governments, academia, tribes and First Nations, and other stakeholders from both the U.S. and Canada all have mapping interests across Great Lakes waters. Identifying and communicating target geographies for new data collection that are shared among multiple organizations can both help to avoid redundancy of new mapping efforts, and create opportunities for greater efficiency through collaboration.
To address this issue, a spatial priorities study was conducted using a geospatial tool developed by the National Ocean Services National Centers for Coastal and Ocean Science (NCCOS). The tool provided an easy-to-use online interface in which programs can identify their priorities in a simple and straightforward way. This study asked representatives of Great Lakes management and science organizations to identify the areas for which they needed maps of lakebed features on a near-term, mid-term, and long-term timeframe, and why. Then, the responses were analyzed and overlaid to determine areas of shared mapping need and opportunity and to determine the types of map products needed.
The analysis revealed high interest among multiple organizations in discrete geographies including the Minnesota and Wisconsin shoreline from Duluth to the eastern extent of the Bayfield Peninsula, Green Bay in Lake Michigan, and the southern coastlines of Lake Erie and Lake Ontario, the St. Marys River, and the northern Lake Superior coastal waters near Grand Portage, MN. Lower priority mapping interest were distributed widely across all lakes, but tended to be concentrated in nearshore areas (<30 m depth).
The analysis also indicated that the top mapping justifications were Habitat/biota/natural area, Benthic exploration, Commercial and recreational fishing, and Scientific research. The top desired map product types were Elevation, Substrate/sub-bottom geologic characterization, and Habitat map/characterization, although participants on some lakes noted other less prevalent product types.
Following from previously conducted NOAA and non-NOAA Federal spatial prioritization exercises, the results of this regional focus can help mapping organizations better understand how their priorities align with the needs of regional organizations, allow for more efficient coordination and funding, and enable partners to leverage assets and resources to fill their most pressing data and information gaps across Great Lakes waters. The U.S. Mapping Coordination Site hosts the results of this study and other spatial prioritization studies. Through this website, one can interact with the study results along with recent and planned mapping efforts.
NOAA intends to update their spatial priorities on a three- to five-year basis. Future studies should strive to expand participation of federal agencies, state and local governments, federally-recognized tribes, academia, and private industry (among other stakeholders) to seek out ocean mapping partnerships in conjunction with the National Ocean Mapping, Exploration and Characterization (NOMEC) goals map once, use many times.”
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Xiaofan","contributorId":297012,"corporation":false,"usgs":false,"family":"Zhang","given":"Xiaofan","email":"","affiliations":[{"id":64275,"text":"GLOS","active":true,"usgs":false}],"preferred":false,"id":853081,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Krumwiede, Brandon","contributorId":297013,"corporation":false,"usgs":false,"family":"Krumwiede","given":"Brandon","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":853082,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Buja, Ken","contributorId":297014,"corporation":false,"usgs":false,"family":"Buja","given":"Ken","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":853083,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70236510,"text":"70236510 - 2022 - Geologic characterization and depositional history of the Uteland Butte member, Green River Formation, southwestern Uinta Basin, Utah","interactions":[],"lastModifiedDate":"2022-09-09T13:39:43.82889","indexId":"70236510","displayToPublicDate":"2022-09-01T08:33:14","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geologic characterization and depositional history of the Uteland Butte member, Green River Formation, southwestern Uinta Basin, Utah","docAbstract":"The 15- to 65-m-thick informal Uteland Butte member of the Eocene Green River Formation represents the first widespread transgression of Lake Uinta in the Uinta Basin, Utah. This study assesses the spatial and temporal variation of Uteland Butte member deposits along a 40-km transect in the southwestern margin of the Uinta Basin using detailed measured sections, organic and inorganic geochemical data, and outcrop gamma ray logs. Fourteen lithofacies are identified, which comprise seven facies associations linked to with lacustrine, palustrine, and deltaic depositional settings. Facies associations are traceable laterally across the study area, where five 4- to 12-m-thick depositional cycles are identified. Each shallowing upwards cycle is defined by a >1.5-m-thick basal package of organic-rich, argillaceous laminated mudstone, and is capped by thick packages of bedded carbonate. In the far western study area (Kyune Creek Canyon), thick deposits of organic-rich mudstone are present and represent the most distal outcrop section; time-equivalent strata in the eastern study area (Minnie Maud Creek Canyon) are relatively organic lean with higher silt and clay content, interpreted to represent proximal lake margin deposits influenced by a nearby delta. The outcrop belt is correlated to more distal cores and well logs across the western Uinta Basin. Similar lithological and petrophysical patterns across the western Uinta Basin are used to subdivide stratigraphy into nine laterally contiguous sub-units based on nomenclature from the oil-producing area of the central basin (from base to top: lower Uteland Butte, D Bench, D Shale, C Bench, C Shale, B Bench, B Shale, A Bench, and A Shale). Siliciclastic clay-rich and carbonaterich intervals are correlated across the region and indicate distinct siliciclastic- and carbonate-dominated lake phases during Uteland Butte member deposition. Climate is interpreted to be the dominant driver of these claycarbonate cycles, in which relatively humid periods resulted in increased fluvially derived siliciclastic sediment into the basin (clay-rich periods), and arid periods resulted in evaporative conditions with decreased fluvial sediment input that favor carbonate accumulation. Climatically driven depositional cycles within the Uteland Butte member reflect, to a smaller degree, the larger scale climatically driven depositional cycles observed at the member- and formation levels of Paleocene and Eocene Uinta Basin stratigraphy. Importantly, the Uteland Butte member clay-carbonate cycles showcase how relatively small-scale climate shifts can impact basin-scale lacustrine deposition.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The lacustrine Green River Formation: Hydrocarbon potential and Eocene climate record","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Utah Geological Association","doi":"10.31711/ugap.v50i.106","usgsCitation":"Gall, R.D., Birdwell, J.E., Brinkerhoff, R., and Vanden Berg, M.D., 2022, Geologic characterization and depositional history of the Uteland Butte member, Green River Formation, southwestern Uinta Basin, Utah, chap. of The lacustrine Green River Formation: Hydrocarbon potential and Eocene climate record, v. 50, p. 37-62, https://doi.org/10.31711/ugap.v50i.106.","productDescription":"26 p.","startPage":"37","endPage":"62","ipdsId":"IP-127906","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":446587,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31711/ugap.v50i.106","text":"Publisher Index Page"},{"id":435703,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X66RQ4","text":"USGS data release","linkHelpText":"Geochemical and spectroscopic data on outcrop samples from the informal Uteland Butte member of the Eocene Green River Formation in Uinta Basin, Utah"},{"id":406449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Uinta Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.786376953125,\n 39.757879992021756\n ],\n [\n -109.281005859375,\n 40.283716270542584\n ],\n [\n -109.49523925781249,\n 40.56389453066509\n ],\n [\n -110.2972412109375,\n 40.543026009955014\n ],\n [\n -110.8740234375,\n 40.35073056591789\n ],\n [\n -110.687255859375,\n 40.057052221322\n ],\n [\n -110.28076171875,\n 39.85915479295669\n ],\n [\n -109.8797607421875,\n 39.73253798438173\n ],\n [\n -109.786376953125,\n 39.757879992021756\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","noUsgsAuthors":false,"publicationDate":"2022-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gall, Ryan D.","contributorId":296324,"corporation":false,"usgs":false,"family":"Gall","given":"Ryan","email":"","middleInitial":"D.","affiliations":[{"id":17626,"text":"Utah Geological Survey","active":true,"usgs":false}],"preferred":false,"id":851281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":851282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brinkerhoff, Riley","contributorId":296326,"corporation":false,"usgs":false,"family":"Brinkerhoff","given":"Riley","email":"","affiliations":[{"id":64017,"text":"Wasatch Energy Management","active":true,"usgs":false}],"preferred":false,"id":851283,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vanden Berg, Michael D.","contributorId":177609,"corporation":false,"usgs":false,"family":"Vanden Berg","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":851284,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236442,"text":"70236442 - 2022 - Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA","interactions":[],"lastModifiedDate":"2022-09-07T12:10:54.664669","indexId":"70236442","displayToPublicDate":"2022-09-01T07:07:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA","docAbstract":"Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic macroinvertebrate index of biotic integrity for small, non-tidal streams (upstream area ≤200 km2) in the Chesapeake Bay watershed (CBW) of the mid-Atlantic coast of North America. We utilized several global and local model interpretation tools to improve average and site-specific model inferences, respectively. The model was used to predict condition for 95,867 individual catchments for eight periods (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019). Predicted conditions were classified as Poor, FairGood, or Uncertain to align with management needs and individual reach lengths and catchment areas were summed by condition class for the CBW for each period. Global permutation and local Shapley importance values indicated percent of forest, development, and agriculture in upstream catchments had strong impacts on predictions. Development and agriculture negatively influenced stream condition for model average (partial dependence [PD] and accumulated local effect [ALE] plots) and local (individual condition expectation and Shapley value plots) levels. Friedman's H-statistic indicated large overall interactions for these three land covers, and bivariate global plots (PD and ALE) supported interactions among agriculture and development. Total stream length and catchment area predicted in FairGood conditions decreased then increased over the 19-years (length/area: 66.6/65.4% in 2001, 66.3/65.2% in 2011, and 66.6/65.4% in 2019). Examination of individual catchment predictions between 2001 and 2019 showed those predicted to have the largest decreases in condition had large increases in development; whereas catchments predicted to exhibit the largest increases in condition showed moderate increases in forest cover. Use of global and local interpretative methods together with watershed-wide and individual catchment predictions support conservation practitioners that need to identify widespread and localized patterns, especially acknowledging that management actions typically take place at individual-reach scales.
Fish behavior after passage or transfer around dams is a critical component in determining whether the goals of these efforts are achieved, but these behaviors are often poorly understood. An elevator was constructed in the lowermost hydroelectric dam on the Menominee River, Wisconsin–Michigan; it is the first elevator specifically designed to capture Lake Sturgeon Acipenser fulvescens for upstream transfer above two dams, providing access to high-quality spawning and early life habitat. Our objectives were to determine whether (1) Lake Sturgeon transferred upstream remained upstream for at least one spawning opportunity; (2) spawning opportunity, time to reach the next dam upstream, and residency in different segments of the river were related to sex, capture method (elevator versus electrofishing), and season of transfer; and (3) the probability of fish transitioning back downstream of the two dams varied among months. We evaluated posttransfer behaviors of 139 Lake Sturgeon that were captured in the elevator or by electrofishing, implanted with acoustic transmitters, transferred upstream (in spring or fall) from fall 2014 to spring 2017, and monitored until fall 2018 using 20–23 stationary acoustic receivers deployed throughout the river. Most Lake Sturgeon (91%) remained upstream for at least one spawning opportunity. The probability of remaining for one spawning opportunity was not related to sex, fish capture method, or season of transfer. Residency times within the two impoundments and time to reach the next dam upstream varied among individual fish. A multistate model indicated that monthly survival after upstream transfer was high and that Lake Sturgeon typically remained above both dams in late fall to early spring, with most downstream movements occurring in April and May. Our results indicate that Lake Sturgeon transferred upstream have the potential to contribute offspring that may help to bolster the Lake Sturgeon population in Lake Michigan, but additional research may help in determining whether these contributions occur.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10379","usgsCitation":"Isermann, D.A., Raabe, J., Easterly, E., Schulze, J., Porter, N., Dembkowski, D., Donofrio, M., Kramer, D., and Elliott, R., 2022, Lake Sturgeon movement after trap and transfer around two dams on the Menominee River, Wisconsin-Michigan: Transactions of the American Fisheries Society, v. 151, no. 5, p. 611-629, https://doi.org/10.1002/tafs.10379.","productDescription":"19 p.","startPage":"611","endPage":"629","ipdsId":"IP-137127","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480742,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","otherGeospatial":"Menominee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.52923848272457,\n 45.083121335445355\n ],\n [\n -87.52923848272457,\n 45.431368318822194\n ],\n [\n -87.97927795298747,\n 45.431368318822194\n ],\n [\n -87.97927795298747,\n 45.083121335445355\n ],\n [\n -87.52923848272457,\n 45.083121335445355\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"151","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Raabe, Joshua K.","contributorId":348735,"corporation":false,"usgs":false,"family":"Raabe","given":"Joshua K.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923727,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Easterly, Emma G.","contributorId":348736,"corporation":false,"usgs":false,"family":"Easterly","given":"Emma G.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schulze, Joshua C.","contributorId":348738,"corporation":false,"usgs":false,"family":"Schulze","given":"Joshua C.","affiliations":[{"id":83404,"text":"USDA Forest Service Region 1","active":true,"usgs":false}],"preferred":false,"id":923729,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Porter, Nicholas J.","contributorId":348741,"corporation":false,"usgs":false,"family":"Porter","given":"Nicholas J.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923730,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dembkowski, Daniel J.","contributorId":348743,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel J.","affiliations":[{"id":65894,"text":"Wisconsin Cooperative Fishery Research Unit","active":true,"usgs":false}],"preferred":false,"id":923731,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Donofrio, Michael C.","contributorId":348744,"corporation":false,"usgs":false,"family":"Donofrio","given":"Michael C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923732,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kramer, Darren R.","contributorId":348745,"corporation":false,"usgs":false,"family":"Kramer","given":"Darren R.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923733,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Elliott, Robert F.","contributorId":348746,"corporation":false,"usgs":false,"family":"Elliott","given":"Robert F.","affiliations":[{"id":12428,"text":"U. 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Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":923734,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70236623,"text":"70236623 - 2022 - Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2023-03-24T16:46:52.398893","indexId":"70236623","displayToPublicDate":"2022-08-30T06:44:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region","title":"Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region","docAbstract":"Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments.
This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog (Rana pipiens) to (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation.
Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity.
STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation.
While the R. pipiens populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01515-8","usgsCitation":"Waraniak, J.M., Mushet, D., and Stockwell, C.A., 2022, Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region: Landscape Ecology, v. 37, p. 2877-2893, https://doi.org/10.1007/s10980-022-01515-8.","productDescription":"17 p.","startPage":"2877","endPage":"2893","ipdsId":"IP-137156","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446618,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-022-01515-8","text":"Publisher Index Page"},{"id":406585,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.39257812499999,\n 45.920587344733654\n ],\n [\n -96.85546875,\n 45.920587344733654\n ],\n [\n -96.85546875,\n 48.574789910928864\n ],\n [\n -102.39257812499999,\n 48.574789910928864\n ],\n [\n -102.39257812499999,\n 45.920587344733654\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Waraniak, Justin M.","contributorId":211882,"corporation":false,"usgs":false,"family":"Waraniak","given":"Justin","email":"","middleInitial":"M.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":851527,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248468,"corporation":false,"usgs":true,"family":"Mushet","given":"David M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":851528,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stockwell, Craig A.","contributorId":194252,"corporation":false,"usgs":false,"family":"Stockwell","given":"Craig","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":851529,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255280,"text":"70255280 - 2022 - Incorporating habitat suitability, landscape distance, and resistant kernels to estimate conservation units for an imperiled terrestrial snake","interactions":[],"lastModifiedDate":"2024-06-17T13:49:07.864148","indexId":"70255280","displayToPublicDate":"2022-08-25T08:43:11","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating habitat suitability, landscape distance, and resistant kernels to estimate conservation units for an imperiled terrestrial snake","docAbstract":"Wildlife distributions are often subdivided into discrete conservation units to aid in implementing management and conservation objectives. Habitat suitability models, resistance surfaces, and resistant kernels provide tools for delineating spatially explicit conservation units but guidelines for parameterizing resistant kernels are generally lacking.
We used the federally threatened eastern indigo snake (Drymarchon couperi) as a case study for calibrating resistant kernels using observed movement data and resistance surfaces to help delineate habitat-based conservation units.
We simulated eastern indigo snake movements under different resistance surface and resistant kernel parameterizations and selected the scenario that produced simulated movement distances that best approximated the maximum observed annual movement distance. We used our calibrated resistant kernel to model range-wide connectivity and compared delineated conservation units to Euclidean distance-based population units from the recent eastern indigo snake species status assessment (SSA).
We identified a total of 255 eastern indigo snake conservation units, with numerous large (2500–5000 ha of suitable habitat) conservation units across the eastern indigo snake distribution. There was substantial variation in the degree of overlap with the SSA population units likely reflecting the spatial heterogeneity in habitat suitability and landscape resistance.
Our calibration approach is widely applicable to other systems for parameterizing biologically meaningful resistant kernels. Our conservation units can be used to prioritize future eastern indigo snake conservation efforts, identify areas where more survey work is needed, or identify small, isolated populations with high extinction risks.
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C.","contributorId":339318,"corporation":false,"usgs":false,"family":"Chandler","given":"H.","email":"","middleInitial":"C.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":904089,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elmore, M.","contributorId":339320,"corporation":false,"usgs":false,"family":"Elmore","given":"M.","email":"","affiliations":[{"id":81289,"text":"Georgia Ecological Services","active":true,"usgs":false}],"preferred":false,"id":904090,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jenkins, C. L.","contributorId":339321,"corporation":false,"usgs":false,"family":"Jenkins","given":"C.","email":"","middleInitial":"L.","affiliations":[{"id":13223,"text":"The Orianne Society","active":true,"usgs":false}],"preferred":false,"id":904091,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70235807,"text":"70235807 - 2022 - Magnetotelluric investigations of the Kīlauea Volcano, Hawaii","interactions":[],"lastModifiedDate":"2022-08-22T14:35:53.522926","indexId":"70235807","displayToPublicDate":"2022-08-22T09:35:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7167,"text":"Journal of Geophysical Research: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Magnetotelluric investigations of the Kīlauea Volcano, Hawaii","docAbstract":"In 2002 and 2003 a collaborative effort was undertaken between Lawrence Berkeley National Laboratory, Sandia National Laboratories, the U.S. Geological Survey (USGS) Menlo Park, the USGS Hawaiian Volcano Observatory, and Electromagnetic Instruments Inc. to study the Kīlauea volcano in Hawaii using the magnetotelluric (MT) technique. The work was motivated by a desire to improve understanding of the magma reservoirs and conduits within Kīlauea and the East and Southwest Rift zones, which has implications for understanding Kīlauea's plumbing system. An improved understanding of the rift zones has implications in understanding large-scale landslides that are generated in the Hilina Slump, which produce significant impacts on coastal communities. Up to eight stations operated simultaneously, with multiple remote reference sites, and data were processed using multi-station robust processing techniques. In total, data were acquired at 70 sites over the Southwest and East rift zones. Good to excellent quality data were obtained even in the harshest conditions, such as those encountered on the fresh lava flows of the East Rift Zone, where electrical contact resistances are on the order of 100 kΩ. A three-dimensional (3D) MT model study was done to guide interpretation of the observed MT measurements. Synthetic modeling demonstrates that conductive bodies in the upper 3 km can be spatially resolved where MT station sampling is good. Resistivity anomalies in the 3D inversions have a high degree of spatial correlation with previously published seismic velocity anomalies beneath Kīlauea. Melt fractions between 0.096 and 0.117 are calculated for the Kīlauea and Puʻuʻōʻō low resistivity anomalies, respectively.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB024418","usgsCitation":"Hoversten, G., Gasperikova, E., Mackie, R., Myer, D., Kauahikaua, J.P., Newman, G.A., and Cuevas, N., 2022, Magnetotelluric investigations of the Kīlauea Volcano, Hawaii: Journal of Geophysical Research: Solid Earth, v. 127, no. 8, e2022JB024418, 24 p., https://doi.org/10.1029/2022JB024418.","productDescription":"e2022JB024418, 24 p.","ipdsId":"IP-135899","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":446701,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2022jb024418","text":"External Repository"},{"id":405386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.29131889343262,\n 19.396901484778134\n ],\n [\n -155.28496742248535,\n 19.39892544698541\n ],\n [\n -155.2786159515381,\n 19.399087362874425\n ],\n [\n -155.2730369567871,\n 19.39827778181811\n ],\n [\n -155.2676296234131,\n 19.400949384016776\n ],\n [\n -155.26385307312012,\n 19.403944764615613\n ],\n [\n -155.25887489318848,\n 19.40629245679785\n ],\n [\n -155.2511501312256,\n 19.4109877394962\n ],\n [\n -155.24694442749023,\n 19.408882974361152\n ],\n [\n -155.24042129516602,\n 19.408478208711944\n ],\n [\n -155.23784637451172,\n 19.410906787494763\n ],\n [\n -155.23836135864258,\n 19.414306736852605\n ],\n [\n -155.24102210998535,\n 19.414306736852605\n ],\n [\n -155.2434253692627,\n 19.418758943963656\n ],\n [\n -155.24943351745605,\n 19.42353481237166\n ],\n [\n -155.25372505187985,\n 19.427662992293143\n ],\n [\n -155.25853157043457,\n 19.4298484568423\n ],\n [\n -155.26239395141602,\n 19.43081976498137\n ],\n [\n -155.2672004699707,\n 19.430576938491143\n ],\n [\n -155.2708911895752,\n 19.4313863587134\n ],\n [\n -155.27406692504883,\n 19.432033891986865\n ],\n [\n -155.2799892425537,\n 19.429605628899985\n ],\n [\n -155.28857231140134,\n 19.42013505603468\n ],\n [\n -155.29492378234863,\n 19.416492381036047\n ],\n [\n -155.2946662902832,\n 19.413416280797698\n ],\n [\n -155.29646873474118,\n 19.410663931248664\n ],\n [\n -155.29698371887207,\n 19.407021044033193\n ],\n [\n -155.29131889343262,\n 19.396901484778134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoversten, G.M.","contributorId":295409,"corporation":false,"usgs":false,"family":"Hoversten","given":"G.M.","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":849391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gasperikova, Erika","contributorId":193561,"corporation":false,"usgs":false,"family":"Gasperikova","given":"Erika","affiliations":[],"preferred":false,"id":849392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mackie, Randall","contributorId":295410,"corporation":false,"usgs":false,"family":"Mackie","given":"Randall","email":"","affiliations":[{"id":63861,"text":"CGG Multiphysics","active":true,"usgs":false}],"preferred":false,"id":849393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Myer, David","contributorId":206497,"corporation":false,"usgs":false,"family":"Myer","given":"David","email":"","affiliations":[],"preferred":false,"id":849394,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kauahikaua, James P. 0000-0003-3777-503X jimk@usgs.gov","orcid":"https://orcid.org/0000-0003-3777-503X","contributorId":2146,"corporation":false,"usgs":true,"family":"Kauahikaua","given":"James","email":"jimk@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":849395,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Newman, Greg A.","contributorId":295412,"corporation":false,"usgs":false,"family":"Newman","given":"Greg","email":"","middleInitial":"A.","affiliations":[{"id":63862,"text":"Lawrence Berkeley National Laboratory, Sandia National Laboratory","active":true,"usgs":false}],"preferred":false,"id":849396,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cuevas, Nestor","contributorId":295414,"corporation":false,"usgs":false,"family":"Cuevas","given":"Nestor","email":"","affiliations":[{"id":63864,"text":"Electromagnetic Instruments","active":true,"usgs":false}],"preferred":false,"id":849397,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70235725,"text":"sim3493 - 2022 - Colored shaded-relief bathymetric map and surrounding aerial imagery of Whiskeytown Lake, California","interactions":[],"lastModifiedDate":"2026-04-01T15:26:29.678467","indexId":"sim3493","displayToPublicDate":"2022-08-16T12:19:22","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3493","displayTitle":"Colored Shaded-Relief Bathymetric Map and Surrounding Aerial Imagery of Whiskeytown Lake, California","title":"Colored shaded-relief bathymetric map and surrounding aerial imagery of Whiskeytown Lake, California","docAbstract":"The Carr wildfire began on July 23, 2018, and burned almost 300,000 acres (approximately half on Federal lands) in northern California during the subsequent 6-week period. Over 97 percent of the area within Whiskeytown National Recreation Area, California, burned during the 2018 Carr wildfire, including the entire landscape that surrounds and drains into Whiskeytown Lake. Shortly after the Carr wildfire ended, the U.S. Geological Survey began investigations into the landscape responses, such as changes in erosion and sediment deposition, that occurred after the fire. This study focused on the collection and processing of bathymetric data and onshore aerial imagery in and around Whiskeytown Lake, California, to support wildfire science after the fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3493","usgsCitation":"Dartnell, P., Logan, J.B., and East, A.E., 2022, Colored shaded-relief bathymetric map and surrounding aerial imagery of Whiskeytown Lake, California: U.S. Geological Survey Scientific Investigations Map 3493, scale 1:8,900, https://doi.org/10.3133/sim3493.","productDescription":"1 Sheet: 35.00 x 35.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-132510","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":501934,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113395.htm","linkFileType":{"id":5,"text":"html"}},{"id":405195,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HEDYNT","text":"Bathymetry, topography and orthomosaic imagery for Whiskeytown Lake, northern California (ver. 2.0, July 2021)","description":"Logan, J.B., Dartnell, P., East, A.E., and Ritchie, A.C., 2020, Bathymetry, topography and orthomosaic imagery for Whiskeytown Lake, northern California (ver. 2.0, July 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P9HEDYNT."},{"id":405194,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3493/sim3493.pdf","size":"25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3493"},{"id":405193,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3493/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Whiskeytown Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.63214111328125,\n 40.59257812608644\n ],\n [\n -122.51609802246092,\n 40.59257812608644\n ],\n [\n -122.51609802246092,\n 40.660066379630365\n ],\n [\n -122.63214111328125,\n 40.660066379630365\n ],\n [\n -122.63214111328125,\n 40.59257812608644\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
The U.S. Geological Survey worked in cooperation with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering South Oyster Bay, a shallow embayment on the southern shore of Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the bay and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, and surrounding land use where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment.
Analysis revealed that the most substantial source of fecal contamination to South Oyster Bay was stormwater, particularly during the summer months. The highest frequency of fecal coliform detections in source sites were under wet summer conditions, and the highest fecal coliform concentrations were under wet summer conditions at the Cedar Creek near Bay Place and Unqua Lake Culvert sites (more than 16,000 most probable number per 100 milliliters each). The human-associated Bacteroides marker was the most frequently detected microbial source tracking marker in South Oyster Bay (50 percent positive detections). The human marker was detected at least twice in all surface water source and receptor sites, except for the Massapequa Lake East Culvert source site that did not have any positive human marker detections. Canine contamination was prolific at source sites but was associated with low fecal coliform concentrations in the winter months. All detections of the canine-associated Bacteroides marker were in samples collected during the winter season and were associated with fecal coliform concentrations below the reporting limit, indicating that birds are not a persistent source of fecal coliform to South Oyster Bay. The absence of fecal coliform and human markers in groundwater samples collected throughout the larger study area indicates that water from cesspools or septic tanks do not contribute fecal coliform to the bay. Further, microbial source tracking markers were not detected in the sandy sediment collected at Zachs Bay. Based a classification scheme developed to convey the degree of fecal contamination to stakeholders and resource managers, the Cedar Creek near Bay Place and Unqua Lake Culvert sites were identified as locations that contribute substantial fecal contamination to South Oyster Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225082","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Tagliaferri, T.N., Fisher, S.C., Kephart, C.M., Cheung, N., Reed, A.P., and Welk, R.J., 2022, Using microbial source tracking to identify fecal contamination sources in South Oyster Bay on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2022–5082, 15 p., https://doi.org/10.3133/sir20225082.","productDescription":"Report: vi, 15 p.; Dataset","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130129","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":404962,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5082/coverthb.jpg"},{"id":404963,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5082/sir20225082.pdf","text":"Report","size":"2.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5082"},{"id":404964,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":404965,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5082/sir20225082.XML"},{"id":404966,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5082/images/"},{"id":404967,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225082/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5082"},{"id":503402,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113394.htm","linkFileType":{"id":5,"text":"html"}},{"id":405037,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20215033","text":"Scientific Investigations Report 2021–5033","linkHelpText":"- Overview and Methodology for a Study To Identify Fecal Contamination Sources Using Microbial Source Tracking in Seven Embayments on Long Island, New York"}],"country":"United States","state":"New York","otherGeospatial":"Long Island, South Oyster Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.50883483886719,\n 40.58606020705239\n ],\n [\n -73.37596893310547,\n 40.58606020705239\n ],\n [\n -73.37596893310547,\n 40.70016219564594\n ],\n [\n -73.50883483886719,\n 40.70016219564594\n ],\n [\n -73.50883483886719,\n 40.58606020705239\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Stream morphology is affected by changes on the surrounding landscape. Understanding the effects of urbanization on stream morphology is a critical factor for land managers to maintain and improve vulnerable stream corridors in urbanizing landscapes. Stormwater practices are used in urban landscapes to manage runoff volumes and peak flows, potentially mitigating alterations to the flow regime that drive changes in channel morphology. However, there remains a paucity of long-term studies assessing watershed-scale relationships between urbanization and effects on stream morphology where green stormwater infrastructure exists in high densities. This paper evaluates the geomorphic changes across four headwater catchments in the Chesapeake Bay Watershed over the course of >10 yr and relates these changes to urban development. Annual cross-sectional surveys conducted from 2002 to 2019 in one forested catchment, one agricultural catchment, and two treatment catchments were used to understand the relationship between urbanization and changes in stream morphology. Six cross-sectional geomorphic metrics were calculated and compared with development timelines and high flow events. A channel evolution model was then used to understand the status of morphologic stability at sites within the study. Results suggest downstream environments in developing areas are more impacted during early phases of suburban construction. Channel change during construction could be a result of sediment and erosion control efforts' limitations on preventing and controlling overland sediment mobilization or of increased discharge causing widening and thus bank-derived sediment to move to the streambed. Results demonstrate that geomorphic metrics are highly variable within a small area and are not always accurate representations of broader landscape changes but rather of the more localized environment at a specific stream segment. Despite a high density of stormwater management facilities in urban catchments, substantial alterations to cross sections were found at multiple locations in each catchment including the controls.
Studying the movements of organisms that live underground for at least a portion of their life history is challenging, given the state of current technology. Passive integrated transponders (PIT tags) provide a way to individually identify and, more recently, study the movement of smaller animals, including those that make subterranean movements. However, there are widespread assumptions of the use of PIT tags that remain problematic. We tested the effects of PIT-tag implantation on growth and survival, along with the effects of electromagnetic fields for reading PIT tags on behavior, of the smallest salamander that has been PIT-tagged: the Red-Backed Salamander. We found no effect of PIT tags on growth or survival. Using a mesocosm experiment, we also found that electromagnetic effects associated with reading PIT tags, had no effect on salamander behavior. Further, we describe a novel PIT antenna and soil mesocosm experimental arena for studying belowground movements of woodland salamanders. Collectively, these studies suggest that the use of PIT tags do not influence the growth, survival, or behavior of Red-Backed Salamanders. Given the challenges of studying salamanders that live underground and the impending changes in climate and landscapes, this research suggests that PIT tags remain a viable tool for studying the movement ecology of salamanders under global change.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","doi":"10.1670/20-006","usgsCitation":"Sterrett, S., Dubreuil, T.L., O'Donnell, M.J., Brand, A., and Campbell Grant, E.H., 2022, Testing assumptions in the use of PIT tags to study movement of Plethodon salamanders: Journal of Herpetology, v. 56, no. 2, p. 146-152, https://doi.org/10.1670/20-006.","productDescription":"7 p.","startPage":"146","endPage":"152","ipdsId":"IP-115968","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":405456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sterrett, Sean C 0000-0003-1356-2785","orcid":"https://orcid.org/0000-0003-1356-2785","contributorId":242972,"corporation":false,"usgs":false,"family":"Sterrett","given":"Sean C","affiliations":[{"id":38445,"text":"Monmouth University","active":true,"usgs":false}],"preferred":false,"id":849501,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dubreuil, Todd L. 0000-0003-0189-4336 tdubreuil@usgs.gov","orcid":"https://orcid.org/0000-0003-0189-4336","contributorId":5552,"corporation":false,"usgs":true,"family":"Dubreuil","given":"Todd","email":"tdubreuil@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":849549,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Donnell, Matthew J. 0000-0002-9089-2377","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":295467,"corporation":false,"usgs":true,"family":"O'Donnell","given":"Matthew","middleInitial":"J.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":849504,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brand, Adrianne 0000-0003-2664-0041","orcid":"https://orcid.org/0000-0003-2664-0041","contributorId":295466,"corporation":false,"usgs":true,"family":"Brand","given":"Adrianne","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":849503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849502,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70235889,"text":"70235889 - 2022 - Winter severity affects occupancy of spring- and summer-breeding anurans across the eastern United States","interactions":[],"lastModifiedDate":"2022-09-27T16:58:22.989788","indexId":"70235889","displayToPublicDate":"2022-08-09T06:40:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Winter severity affects occupancy of spring- and summer-breeding anurans across the eastern United States","docAbstract":"Climate change is an increasingly important driver of biodiversity loss. The ectothermic nature of amphibians may make them particularly sensitive to changes in temperature and precipitation regimes, adding to declines from other threats. While active season environmental conditions can influence growth and survival, effects of variation in winter conditions on population dynamics are less well-studied. Given that extreme winter temperatures can influence amphibian survival and fitness, we expected that increased winter severity—as measured by variability in winter temperatures and snow cover—would be associated with decreased occupancy, and that populations that experience more severe winters would have the largest sensitivities and show the greatest declines.
Eastern United States.
2001–2015.
Anurans.
We used large-scale citizen science data from the eastern half of the United States, a diverse biogeographic and climatic region, to assess how variation in winter severity influenced occupancy dynamics (i.e. presence or absence of species across sites and years) of 11 spring and summer breeding anuran species.
Most species had increased occupancy in years with greater than average snow cover and warmer than average mean winter temperatures. Surprisingly, climatic conditions in winter affected occupancy dynamics of species with varying life history characteristics, including both spring and summer breeding species, those that overwinter under the soil, and those that overwinter in ponds and stream beds. For two wide-ranging species (Lithobates catesbeianus and Lithobates clamitans), colder winter temperatures reduced occupancy more at northern latitudes, while the association between days of snow cover and latitude was equivocal.
As the climate continues to change, expected reductions in snowpack may reduce occupancy of already declining anuran populations, while milder winters may improve overwinter survival for some species. The contradictory impacts of temperature and snow cover illustrate the importance of considering multi-dimensional impacts of climate change on anuran populations.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13620","usgsCitation":"Weiskopf, S.R., Shiklomanov, A.N., Thompson, L., Wheedleton, S., and Campbell Grant, E.H., 2022, Winter severity affects occupancy of spring- and summer-breeding anurans across the eastern United States: Diversity and Distributions, v. 28, no. 10, p. 2187-2199, https://doi.org/10.1111/ddi.13620.","productDescription":"13 p.","startPage":"2187","endPage":"2199","ipdsId":"IP-127531","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":446854,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13620","text":"Publisher Index Page"},{"id":405526,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.28320312499999,\n 25.24469595130604\n ],\n [\n -66.88476562499999,\n 25.24469595130604\n ],\n [\n -66.88476562499999,\n 49.26780455063753\n ],\n [\n -100.28320312499999,\n 49.26780455063753\n ],\n [\n -100.28320312499999,\n 25.24469595130604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiskopf, Sarah R. 0000-0002-5933-8191","orcid":"https://orcid.org/0000-0002-5933-8191","contributorId":207699,"corporation":false,"usgs":true,"family":"Weiskopf","given":"Sarah","email":"","middleInitial":"R.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":849614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shiklomanov, Alexey N. 0000-0003-4022-5979","orcid":"https://orcid.org/0000-0003-4022-5979","contributorId":245541,"corporation":false,"usgs":false,"family":"Shiklomanov","given":"Alexey","email":"","middleInitial":"N.","affiliations":[{"id":49218,"text":"Boston University Department of Earth and Environment","active":true,"usgs":false}],"preferred":false,"id":849615,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":207364,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":849616,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wheedleton, Sarah","contributorId":295508,"corporation":false,"usgs":false,"family":"Wheedleton","given":"Sarah","email":"","affiliations":[{"id":63897,"text":"Smithsonian Conservation Commons","active":true,"usgs":false}],"preferred":false,"id":849617,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849618,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239138,"text":"70239138 - 2022 - RNA-seq reveals potential gene biomarkers in fathead minnows (Pimephales promelas) for exposure to treated wastewater effluent","interactions":[],"lastModifiedDate":"2022-12-29T13:16:42.17673","indexId":"70239138","displayToPublicDate":"2022-08-08T07:09:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9161,"text":"Environmental Science: Processes & Impacts","active":true,"publicationSubtype":{"id":10}},"displayTitle":"RNA-seq reveals potential gene biomarkers in fathead minnows (Pimephales promelas) for exposure to treated wastewater effluent","title":"RNA-seq reveals potential gene biomarkers in fathead minnows (Pimephales promelas) for exposure to treated wastewater effluent","docAbstract":"Discharged wastewater treatment plant (WWTP) effluent greatly contributes to the generation of complex mixtures of contaminants of emerging concern (CECs) in aquatic environments which often contain neuropharmaceuticals and other emerging contaminants that may impact neurological function. However, there is a paucity of knowledge on the neurological impacts of these exposures to aquatic organisms. In this study, caged fathead minnows (Pimephales promelas) were exposed in situ in a temperate-region effluent-dominated stream (i.e., Muddy Creek) in Coralville, Iowa, USA upstream and downstream of a WWTP effluent outfall. The pharmaceutical composition of Muddy Creek was recently characterized by our team and revealed many compounds there were at a low microgram to high nanogram per liter concentration. Total RNA sequencing analysis on brain tissues revealed 280 gene isoforms that were significantly differentially expressed in male fish and 293 gene isoforms in female fish between the upstream and downstream site. Only 66 (13%) of such gene isoforms overlapped amongst male and female fish, demonstrating sex-dependent impacts on neuronal gene expression. By using a systems biology approach paired with functional enrichment analyses, we identified several potential novel gene biomarkers for treated effluent exposure that could be used to expand monitoring of environmental effects with respect to complex CEC mixtures. Lastly, when comparing the results of this study to those that relied on a single-compound approach, there was relatively little overlap in terms of gene-specific effects. This discovery brings into question the application of single-compound exposures in accurately characterizing environmental risks of complex mixtures and for gene biomarker identification.
The lesser prairie-chicken (Tympanuchus pallidicinctus) is a species of prairie grouse that occupies grassland ecosystems in the Southern and Central High Plains of the Great Plains. Reduced abundance and occupied ranges have led to increased conservation efforts throughout the species’ range. Habitat loss is considered the predominant cause of these declines. In the Southern High Plains of Texas and New Mexico, lesser prairie-chicken habitat corresponds to the Sand Shinnery Oak Prairie Ecoregion, which is comprised of a mixture of sand shinnery oak (Quercus havardii)-dominated grasslands, sand sagebrush (Artemisia filifolia)-dominated grasslands, and mixed grasslands. In sand shinnery oak–grassland communities, conversion to row-crop agriculture, continuous unmanaged livestock grazing, restriction of natural fire, invasive plant species (e.g., mesquite (Prosopis spp.)), extensive use of herbicides, energy development, and a variety of other factors have also negatively affected ecosystem extent and function. We integrated historical maps and remote sensing-derived information to measure trends in the extent and geographical distribution of sand shinnery oak prairies in eastern New Mexico and northwest Texas. Potential lesser prairie-chicken habitat was reduced by 56% from a potential of 43,258 km2 to 18,908 km2 in ~115 years (since pre-settlement). Our assessment indicated both mixed grasslands and sand shinnery oak-dominated grasslands were transformed from large parcels of existing vegetation communities to urban settlements, row crops, roads, and industrial land uses by the 1970s. Currently, potential habitat is highly fragmented and restricted to isolated locations in Texas and New Mexico, with an increasing dominance in mixed grasslands, especially in the southeastern portion of the lesser prairie-chicken range. Sand shinnery oak-dominated grasslands have been declining rapidly, from 69% of its potential extent in 1985, 65% in 1995, 54% in 2005, to 42% in 2015. Mixed grasslands drastically declined to 50% of its potential distribution by 1985. Since then, it has been stable until the 2005–2015 period when it declined to 45% of its potential extent. Based on the 2015 assessment, the current potential habitat for lesser prairie chicken is estimated at 18,908 km2 (1,890,800 ha or 4.6 million acres), where 13,126 km2 corresponds to mixed grasslands and 5782 km2 corresponds to sand shinnery oak-dominated grasslands.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14153780","usgsCitation":"Portillo-Quintero, C., Grisham, B., Haukos, D.A., Boal, C.W., Christian A. Hagen, Wan, Z., Subedi, M., and Menkiti, N., 2022, Trends of lesser prairie-chicken habitat extent and distribution on the Southern High Plains: Remote Sensing, v. 14, no. 15, 3780, 21 p., https://doi.org/10.3390/rs14153780.","productDescription":"3780, 21 p.","ipdsId":"IP-134001","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":446881,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14153780","text":"Publisher Index Page"},{"id":429754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.1173828305202,\n 34.97867213600597\n ],\n [\n -105.1173828305202,\n 31.262556094417107\n ],\n [\n -101.82148439302048,\n 31.262556094417107\n ],\n [\n -101.82148439302048,\n 34.97867213600597\n ],\n [\n -105.1173828305202,\n 34.97867213600597\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"15","noUsgsAuthors":false,"publicationDate":"2022-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Portillo-Quintero, Carlos","contributorId":198384,"corporation":false,"usgs":false,"family":"Portillo-Quintero","given":"Carlos","email":"","affiliations":[],"preferred":false,"id":902666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grisham, Blake","contributorId":337771,"corporation":false,"usgs":false,"family":"Grisham","given":"Blake","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":902870,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":902665,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":902671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christian A. 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Hagen","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":902871,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wan, Zhanming","contributorId":211684,"corporation":false,"usgs":false,"family":"Wan","given":"Zhanming","email":"","affiliations":[],"preferred":false,"id":902669,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Subedi, Mukti","contributorId":337996,"corporation":false,"usgs":false,"family":"Subedi","given":"Mukti","email":"","affiliations":[],"preferred":false,"id":902872,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Menkiti, Nwasinachi","contributorId":337772,"corporation":false,"usgs":false,"family":"Menkiti","given":"Nwasinachi","email":"","affiliations":[{"id":40367,"text":"Utah Valley University","active":true,"usgs":false}],"preferred":false,"id":902670,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70234234,"text":"70234234 - 2022 - Freshwater unionid mussels threatened by predation of Round Goby (Neogobius melanostomus)","interactions":[],"lastModifiedDate":"2022-08-04T14:05:53.114941","indexId":"70234234","displayToPublicDate":"2022-08-04T08:57:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Freshwater unionid mussels threatened by predation of Round Goby (Neogobius melanostomus)","title":"Freshwater unionid mussels threatened by predation of Round Goby (Neogobius melanostomus)","docAbstract":"Indigenous freshwater mussels (Unionidae) are integral to riverine ecosystems, playing a pivotal role in aquatic food webs and providing ecological services. With populations on the decline worldwide, freshwater mussels are of conservation concern. In this study, we explore the propensity of the invasive Round Goby (Neogobius melanostomus) fish to prey upon indigenous freshwater mussels. First, we conducted lab experiments where Round Gobies were given the opportunity to feed on juvenile unionid mussels and macroinvertebrates, revealing rates and preferences of consumption. Several Round Gobies consumed whole freshwater mussels during these experiments, as confirmed by mussel counts and x-ray images of the fishes. Next, we investigated Round Gobies collected from stream habitats of the French Creek watershed, which is renowned for its unique and rich aquatic biodiversity. We developed a novel DNA metabarcoding method to identify the specific species of mussels consumed by Round Goby and provide a new database of DNA gene sequences for 25 indigenous unionid mussel species. Several of the fishes sampled had consumed indigenous mussels, including the Elktoe (non-endangered), Creeper (non-endangered), Long Solid (state endangered), and Rayed Bean (federally endangered) species. The invasive Round Goby poses a growing threat to unionid mussels, including species of conservation concern. The introduction of the invasive Round Goby to freshwaters of North America is shaping ecosystem transitions within the aquatic critical zone having widespread implications for conservation and management.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-022-16385-y","usgsCitation":"Clark, K., Iwanowicz, D.D., Iwanowicz, L., Mueller, S., Wisor, J., Bradshaw-Wilson, C., Schill, W., Stauffer, J.R., and Boyer, E.W., 2022, Freshwater unionid mussels threatened by predation of Round Goby (Neogobius melanostomus): Scientific Reports, v. 12, 12859, 11 p., https://doi.org/10.1038/s41598-022-16385-y.","productDescription":"12859, 11 p.","ipdsId":"IP-137170","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446924,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-022-16385-y","text":"Publisher Index Page"},{"id":404822,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, New York, Pennsylvania, West Virginia","otherGeospatial":"Allegheny River Basin, Monongahela River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.815673828125,\n 38.37611542403604\n ],\n [\n -79.70581054687499,\n 38.634036452919226\n ],\n [\n -78.99169921875,\n 39.01064750994083\n ],\n [\n -78.607177734375,\n 39.47860556892209\n ],\n [\n -78.64013671875,\n 39.985538414809746\n ],\n [\n -78.2666015625,\n 40.64730356252251\n ],\n [\n -77.34374999999999,\n 41.21998578493921\n ],\n [\n -77.2119140625,\n 42.27730877423709\n ],\n [\n -77.662353515625,\n 42.771211138625894\n ],\n [\n -78.914794921875,\n 42.78733853171998\n ],\n [\n -80.518798828125,\n 41.9921602333763\n ],\n [\n -80.13427734374999,\n 41.15384235711447\n ],\n [\n -80.628662109375,\n 38.77978137804918\n ],\n [\n -80.035400390625,\n 38.40194908237822\n ],\n [\n -79.815673828125,\n 38.37611542403604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2022-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Clark, Kyle","contributorId":294537,"corporation":false,"usgs":false,"family":"Clark","given":"Kyle","email":"","affiliations":[{"id":63593,"text":"Penn State University, Pennsylvania Fish & Boat Commission","active":true,"usgs":false}],"preferred":false,"id":848276,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iwanowicz, Deborah D. 0000-0002-9613-8594 diwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-9613-8594","contributorId":2253,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Deborah","email":"diwanowicz@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":848278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iwanowicz, Luke R. 0000-0002-1197-6178","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":79382,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":848277,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mueller, Sara","contributorId":294538,"corporation":false,"usgs":false,"family":"Mueller","given":"Sara","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":848279,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wisor, Joshua","contributorId":294539,"corporation":false,"usgs":false,"family":"Wisor","given":"Joshua","email":"","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":848280,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bradshaw-Wilson, Casey","contributorId":294540,"corporation":false,"usgs":false,"family":"Bradshaw-Wilson","given":"Casey","email":"","affiliations":[{"id":63066,"text":"Allegheny College","active":true,"usgs":false}],"preferred":false,"id":848281,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schill, W. Bane 0000-0002-9217-984X","orcid":"https://orcid.org/0000-0002-9217-984X","contributorId":213903,"corporation":false,"usgs":true,"family":"Schill","given":"W. Bane","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":848282,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stauffer, Jay R. Jr.","contributorId":119700,"corporation":false,"usgs":false,"family":"Stauffer","given":"Jay","suffix":"Jr.","email":"","middleInitial":"R.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":848283,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Boyer, Elizabeth W.","contributorId":44659,"corporation":false,"usgs":false,"family":"Boyer","given":"Elizabeth","email":"","middleInitial":"W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":848284,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70235849,"text":"70235849 - 2022 - Living with wildfire in Grand County, Colorado: 2021 data report","interactions":[],"lastModifiedDate":"2022-08-23T14:44:06.307743","indexId":"70235849","displayToPublicDate":"2022-08-01T09:41:02","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":72,"text":"Research Note","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"RMRS-RN-94","title":"Living with wildfire in Grand County, Colorado: 2021 data report","docAbstract":"Wildfire affects hundreds of wildland-urban interface communities each year, and yet most communities lack data reflecting the conditions before an event. This study was conducted before the devastating 2020 East Troublesome Fire1, which spread across 193,812 acres and resulted in two lives lost and 366 homes and 214 other structures burned. The fire’s dramatic run threatened over 7,000 structures and led to a mandatory evacuation of over 35,000 people in Grand and Larimer Counties. The data reported here serve as baseline data to aid in understanding the parcel and social conditions before the fire. This report presents results from WiRē Rapid Wildfire Risk Assessment (WiRē RA) data, collected from 1,162 private residential properties in six communities in five fire protection districts (FPDs), the majority (72%) of which were characterized as high, very high, or extreme risk.
This report also presents results from household surveys sent to homeowners in the study area. Household survey respondents underestimated their risk compared to the conditions observed through the professional risk assessment. Respondents consistently overestimated the amount of defensible space and the distance from their homes to nonvegetative combustibles. Respondents also overestimated the availability of driveway clearance that would enable access for response vehicles and for safe passing of residents evacuating and responders arriving to their homes.
This chapter introduces seismic monitoring of structural systems for buildings and begins with a historical background of this topic in the United States. After providing the historical context, the chapter reviews common seismic instrumentation issues such as utilization of data, code versus extensive instrumentation, free-field instrumentation, record synchronization requirements and more. Recent developments in damage detection is examined including damage detection based on changes in natural frequencies, permanent deformations, and interstory drift. Finally, applications in Europe, the Middle East, and Japan of seismic monitoring of structural systems for buildings are discussed.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Sensor technologies for civil infrastructures: Applications in structural health monitoring","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-08-102706-6.00004-0","usgsCitation":"Celebi, M., and Kaya, Y., 2022, Seismic monitoring solutions for buildings, chap. 3 of Sensor technologies for civil infrastructures: Applications in structural health monitoring, v. 2, p. 63-101, https://doi.org/10.1016/B978-0-08-102706-6.00004-0.","productDescription":"39 p.","startPage":"63","endPage":"101","ipdsId":"IP-113785","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":406964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":852254,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaya, Yavuz","contributorId":296700,"corporation":false,"usgs":false,"family":"Kaya","given":"Yavuz","email":"","affiliations":[{"id":64148,"text":"BC Ministry of Transportation and Infrastructure","active":true,"usgs":false}],"preferred":false,"id":852255,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70234181,"text":"70234181 - 2022 - Spatiotemporal changes in influenza A virus prevalence among wild waterfowl inhabiting the continental United States throughout the annual cycle","interactions":[],"lastModifiedDate":"2022-08-03T12:09:42.114227","indexId":"70234181","displayToPublicDate":"2022-07-29T07:05:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal changes in influenza A virus prevalence among wild waterfowl inhabiting the continental United States throughout the annual cycle","docAbstract":"Avian influenza viruses can pose serious risks to agricultural production, human health, and wildlife. An understanding of viruses in wild reservoir species across time and space is important to informing surveillance programs, risk models, and potential population impacts for vulnerable species. Although it is recognized that influenza A virus prevalence peaks in reservoir waterfowl in late summer through autumn, temporal and spatial variation across species has not been fully characterized. We combined two large influenza databases for North America and applied spatiotemporal models to explore patterns in prevalence throughout the annual cycle and across the continental United States for 30 waterfowl species. Peaks in prevalence in late summer through autumn were pronounced for dabbling ducks in the genera Anas and Spatula, but not Mareca. Spatially, areas of high prevalence appeared to be related to regional duck density, with highest predicted prevalence found across the upper Midwest during early fall, though further study is needed. We documented elevated prevalence in late winter and early spring, particularly in the Mississippi Alluvial Valley. Our results suggest that spatiotemporal variation in prevalence outside autumn staging areas may also represent a dynamic parameter to be considered in IAV ecology and associated risks.
Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water (Burow and Belitz, 2014). The stream-valley aquifers constitute one of the important aquifer systems being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223037","collaboration":"National Water-Quality Assessment Project","programNote":"National Water Quality Program","usgsCitation":"Kingsbury, J.A., 2022, Groundwater quality in selected Stream Valley aquifers, eastern United States: U.S. Geological Survey Fact Sheet 2022-3037, 4 p., https://doi.org/10.3133/fs20223037.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-135420","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":404450,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3037/covrthb.jpg"},{"id":404451,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3037/fs20223037.pdf","text":"Report","size":"3.41 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":404452,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3037/fs20223037.xml"},{"id":404453,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3037/images"},{"id":501492,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113350.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois, Indiana, Kentucky, Missouri, New York, Ohio, Pennsylvania, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.2529296875,\n 36.59788913307022\n ],\n [\n -87.890625,\n 36.94989178681327\n ],\n [\n -85.517578125,\n 37.71859032558816\n ],\n [\n -83.54003906250001,\n 38.13455657705411\n ],\n [\n -82.44140625,\n 37.92686760148135\n ],\n [\n -80.85937499999999,\n 38.30718056188316\n ],\n [\n -80.1123046875,\n 38.75408327579141\n ],\n [\n -78.92578124999999,\n 39.977120098439634\n ],\n [\n -77.82714843749999,\n 40.713955826286046\n ],\n [\n -76.4208984375,\n 41.409775832009565\n ],\n [\n -76.4208984375,\n 41.902277040963696\n ],\n [\n -76.728515625,\n 42.58544425738491\n ],\n [\n -77.82714843749999,\n 42.58544425738491\n ],\n [\n -78.486328125,\n 41.96765920367816\n ],\n [\n -79.89257812499999,\n 41.27780646738183\n ],\n [\n -81.650390625,\n 40.91351257612758\n ],\n [\n -83.14453125,\n 40.44694705960048\n ],\n [\n -84.375,\n 39.9434364619742\n ],\n [\n -86.484375,\n 39.33429742980725\n ],\n [\n -88.11035156249999,\n 38.58252615935333\n ],\n [\n -88.9453125,\n 38.13455657705411\n ],\n [\n -89.8681640625,\n 37.37015718405753\n ],\n [\n -89.736328125,\n 36.70365959719456\n ],\n [\n -89.2529296875,\n 36.59788913307022\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Chief Scientist
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12201 Sunrise Valley Drive, MS 413
Reston, VA 20192-0002
Geological reservoir characterization is essential for accurate evaluation of gas production performance from gas hydrate reservoirs. Particularly, the understanding of reservoir architecture and heterogeneity is of great importance since these are considered as major controls on fluid hydrodynamic and thermodynamic conditions. This study deals with well log and three-dimensional (3-D) vertical seismic profile (VSP) data acquired from the Hydrate-01 Stratigraphic Test Well within the 7-11-12 prospect, Prudhoe Bay Unit, Alaska North Slope and reports on the results of geological/geophysical evaluation related to the geological structure and reservoir properties of the 7-11-12 prospect. The structural trends of the target reservoirs, based on well correlations, are mostly consistent with the predrill prediction using the surface seismic data, and infer the existence of subseismic faults cutting through the Hydrate-01 well. The 3-D VSP data confirm a down-to-the-east normal fault that offsets the reservoir units across the Hydrate-01 well, which is concordant with the well identification of the same fault, and indicate a northeast-dipping relay structure associated with the overstepping normal faults. The edge enhancement attribute associated with discontinuity generated from the 3-D VSP data shows small faults/fractures, possibly as part of a complex fault network within the imaged normal fault system. These results reveal that the 3-D VSP data provide detailed structural information that is not present from the surface seismic data. The Hydrate-01 well log data confirm the occurrence of gas hydrate at high saturation in the two targeted sand units (B1 and D1 sands), and the comparison to a nearby pre-existing well (7-11-12 well) shows the same general trend in gas hydrate saturation as a map of seismic impedance generated from surface seismic data. The well log data also suggest that the base of gas hydrate occurrence in the Hydrate-01 and 7-11-12 wells is almost aligned at the same depth in both of the targeted B1 and D1 sand reservoirs. Especially for the D1 sand in the Hydrate-01 well, the resistivity logs show a sharp transition from high gas hydrate saturation to fully water-saturated within the D1 sand, suggesting a common gas hydrate/water contact. The results of this study will be used to construct the geological models needed for reservoir simulation studies and they can provide important insights into the geological factors that control the occurrence of gas hydrate on the Alaska North Slope.
New lithologic and detrital zircon (DZ) U-Pb data from Devonian–Triassic strata on St. Lawrence Island in the Bering Sea and from the western Brooks Range of Alaska suggest affinities between these two areas. The Brooks Range constitutes part of the Arctic Alaska–Chukotka microplate, but the tectonic and paleogeographic affinities of St. Lawrence Island are unknown or at best speculative. Strata on St. Lawrence Island form a Devonian–Triassic carbonate succession and a Mississippian(?)–Triassic clastic succession that are subdivided according to three distinctive DZ age distributions. The Devonian–Triassic carbonate succession has Mississippian-age quartz arenite beds with Silurian, Cambrian, Neoproterozoic, and Mesoproterozoic DZ age modes, and it exhibits similar age distributions and lithologic and biostratigraphic characteristics as Mississippian-age Utukok Formation strata in the Kelly River allochthon of the western Brooks Range. Consistent late Neoproterozoic, Cambrian, and Silurian ages in each of the Mississippian-age units suggest efficient mixing of the DZ prior to deposition, and derivation from strata exposed by the pre-Mississippian unconformity and/or Endicott Group strata that postdate the unconformity. The Mississippian(?)–Triassic clastic succession is subdivided into feldspathic and graywacke subunits. The feldspathic subunit has a unimodal DZ age mode at 2.06 Ga, identical to Nuka Formation strata in the Nuka Ridge allochthon of the western Brooks Range, and it records a distinctive depositional episode related to late Paleozoic juxtaposition of a Paleoproterozoic terrane along the most distal parts of the Arctic Alaska–Chukotka microplate. The graywacke subunit has Triassic maximum depositional ages and abundant late Paleozoic grains, likely sourced from fringing arcs and/or continent-scale paleorivers draining Eurasia, and it has similar age distributions to Triassic strata from the Lisburne Peninsula (northwestern Alaska), Chukotka and Wrangel Island (eastern Russia), and the northern Sverdrup Basin (Canadian Arctic), but, unlike the Devonian–Triassic carbonate succession and feldspathic subunit of the Mississippian(?)–Triassic clastic succession, it has no obvious analogue in the western Brooks Range allochthon stack. These correlations establish St. Lawrence Island as conclusively belonging to the Arctic Alaska–Chukotka microplate, thus enhancing our understanding of the circum-Arctic region in late Paleozoic–Triassic time.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02490.1","usgsCitation":"Amato, J.M., Dumoulin, J.A., Gottlieb, E.S., and Moore, T.E., 2022, Detrital zircon ages from upper Paleozoic–Triassic clastic strata on St. Lawrence Island, Alaska: An enigmatic component of the Arctic Alaska–Chukotka microplate: Geosphere, v. 18, no. 5, p. 1492-1523, https://doi.org/10.1130/GES02490.1.","productDescription":"32 p.","startPage":"1492","endPage":"1523","ipdsId":"IP-134575","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":447026,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02490.1","text":"Publisher Index Page"},{"id":435756,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PILIK","text":"USGS data release","linkHelpText":"Location Data for Petrographic Samples and Isotopic and Age Data from Detrital Zircon Grains from Selected Rock Samples from St. Lawrence Island and the Western Brooks Range, Alaska"},{"id":408489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"St. Lawrence Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -171.968994140625,\n 62.88520467163244\n ],\n [\n -168.50830078125,\n 62.88520467163244\n ],\n [\n -168.50830078125,\n 63.86487567533106\n ],\n [\n -171.968994140625,\n 63.86487567533106\n ],\n [\n -171.968994140625,\n 62.88520467163244\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Amato, Jeffrey M.","contributorId":247883,"corporation":false,"usgs":false,"family":"Amato","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[{"id":49682,"text":"Dept of Geolgical Sciences, New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":854897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dumoulin, Julie A. 0000-0003-1754-1287 dumoulin@usgs.gov","orcid":"https://orcid.org/0000-0003-1754-1287","contributorId":203209,"corporation":false,"usgs":true,"family":"Dumoulin","given":"Julie","email":"dumoulin@usgs.gov","middleInitial":"A.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":854898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gottlieb, Eric S. 0000-0002-4904-9492","orcid":"https://orcid.org/0000-0002-4904-9492","contributorId":291239,"corporation":false,"usgs":false,"family":"Gottlieb","given":"Eric","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":854899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, Thomas E. 0000-0002-0878-0457 tmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-0878-0457","contributorId":127538,"corporation":false,"usgs":true,"family":"Moore","given":"Thomas","email":"tmoore@usgs.gov","middleInitial":"E.","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854900,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70233533,"text":"sir20225060 - 2022 - Trends in groundwater levels, and orthophosphate and nitrate concentrations in the Middle Snake River Region, south-central Idaho","interactions":[],"lastModifiedDate":"2026-04-23T16:44:54.255731","indexId":"sir20225060","displayToPublicDate":"2022-07-22T09:58:04","publicationYear":"2022","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":"2022-5060","displayTitle":"Trends in Groundwater Levels, and Orthophosphate and Nitrate Concentrations in the Middle Snake River Region, South-Central Idaho","title":"Trends in groundwater levels, and orthophosphate and nitrate concentrations in the Middle Snake River Region, south-central Idaho","docAbstract":"The U.S. Geological Survey (USGS) evaluated nitrate and orthophosphate concentrations in groundwater for temporal trends (monotonic and step trends) for the middle Snake River region (Cassia, Gooding, Jerome, Lincoln, Minidoka, and Twin Falls Counties) in south-central Idaho using the Regional Kendall test (monotonic trends) and the Wilcoxon signed rank test (step trends). The study evaluated two trend periods: 2000–09 and 2010–19/20. The study area was divided into six hydrogeologic zones (HZs) that had similar geologic and hydrologic characteristics and that correlated with county boundaries where possible. Two well networks sampled by the USGS National Water Quality Program within the HZs were also evaluated.
The northern Gooding County HZ had statistically significant increasing nitrate concentration trends for both the monotonic and step trends in the early trend period, while the Cassia and Jerome/Southern Gooding County HZs only had one of the statistical tests with statistically significant increasing nitrate concentrations. The Minidoka County HZ had conflicting results between the two statistical tests for the early time period with a statistically significant increasing monotonic trend in nitrate concentration and a statistically significant decreasing step trend. The differing results between these two statistical tests indicates the significance of concentration data during the middle of the time period. Both the Lincoln and Twin Falls County HZs did not have statistically significant trends for either test during either time period as well as the Northern Gooding County HZ for the latter time period. The Minidoka County HZ had statistically significant nitrate trends for both tests in the latter time period along with one of the trend tests for the Cassia and Jerome/Southern Gooding County HZ. Most of the nitrate concentration trend rates are low from 0.01 to 0.12 milligram per liter per year (mg/L/year) with the northern Gooding County HZ having the highest trend rate during the early time period of 0.28 mg/L/year for the step trend and 0.55 mg/L/year for the monotonic trend.
All the HZs and both well networks had statistically significant increasing orthophosphate-concentrations trends in groundwater for the early time period except for the Lincoln County HZ and the step-trend for the Minidoka County HZ. Orthophosphate concentration trend rates for the early period were low, ranging from 0.001 to 0.015 mg/L/year. Only two HZs and the well networks had enough orthophosphate concentration data available in the latter time period to do statistical analysis. The two HZs (Minidoka and Southern Gooding/Jerome County) both have decreasing orthophosphate concentration trends, with only the monotonic trend for the Southern Gooding/Jerome County HZ being statistically significant at 90 percent with a rate of −0.001 mg/L/year.
Groundwater levels in two well networks in the eastern Snake River Plain aquifer were also evaluated for trends (monotonic and step), with both networks having statistically significant declining groundwater levels for the 1993–2009 trend period. The latter trend period (2010–20) had statistically significant declining groundwater levels for the A&B well network and statistically significant increasing groundwater levels for the Jerome/Gooding well network, which is downgradient from an aquifer recharge area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225060","collaboration":"Prepared in cooperation with the Idaho Department of Environmental Quality and the Middle Snake Regional Water Resource Commission","usgsCitation":"Skinner, K.D., 2022, Trends in groundwater levels, and orthophosphate and nitrate concentrations in the Middle Snake River Region, south-central Idaho: U.S. Geological Survey Scientific Investigations Report 2022–5060, 18 p., https://doi.org/10.3133/sir20225060.","productDescription":"vii, 18 p.","onlineOnly":"Y","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":404367,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5060/coverthb.jpg"},{"id":404368,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5060/sir20225060.pdf","text":"Report","size":"2.1 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Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
From the flat, rich soil of western Tennessee to the Appalachian Mountains in the east, and rolling hills in between, “the Volunteer State” enjoys a wealth of natural resources.
The Tennessee, Cumberland, and Mississippi Rivers supply economically crucial navigation routes, along with recreation for residents and visitors. Additionally, 14 million acres of hardwood and softwood forests cover roughly one-half of the State, contributing an estimated $24 billion and nearly 100,000 jobs to Tennessee’s economy. Within a span of more than 400 miles, the State’s diverse agricultural products include cotton, corn, soybeans, poultry, horses, cattle, goats, hay, vegetables, nursery crops, and tobacco.
Energy production is important to Tennessee and the region, and power sources range from coal and nuclear to hydroelectric sources. Tourism also is a key industry, and music attractions and historical sites are balanced by natural features such as the Great Smoky Mountains National Park, which recorded 14.1 million visits and ranked second for most visited National Park Service site in the United States in 2021.
Landsat imagery’s broad geographic scale and rich historical archive have proven useful to land managers and State agencies for monitoring natural resources. Here are several ways Landsat has benefited Tennessee.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192