Shallow nearshore groundwater and estimates of groundwater seepage were collected at 21 locations along the north and south shores of Lake Spokane beginning in October 2016 and ending in October 2019. Nitrate plus nitrite concentrations in nearshore groundwater ranged from <0.04 to 7.60 milligrams of nitrogen per liter. Nearshore groundwater orthophosphate concentrations ranged from <0.004 to 0.381 milligrams of phosphorus per liter, and, overall, there were no consistent seasonal differences in nearshore groundwater nutrients during this study. Nitrate plus nitrite concentrations were highest at sites located adjacent to nearshore development and similar to concentrations in water collected from nearby drinking water wells. Similarly, samples from locations adjacent to nearshore development were statistically greater than samples collected from other locations for orthophosphate concentrations. Dissolved boron concentrations, elevated values of which are an indicator of household-detergent use, were elevated in spring and summer at some locations, indicating that residential wastewater was reaching the lake. Stable isotope ratios of nitrate (15N and 18O), which were used to identify the source nitrate in sampled groundwater, showed that most data indicated a mix of soil nitrogen and nitrogen sources from human or animal waste.
Generally, median groundwater discharge to the lake was low across all sites and seasons, with most values smaller than 1 centimeter per day (cm/d). Similar to the nutrient-concentration data, seasonal patterns in seepage flux were weak, and, where there were seasonal increases in flux, the increased groundwater discharge did not carry increased nutrients. Localized estimates of groundwater seepage flux were scaled up to the entire length of the lakeshore. The median groundwater flux of 0.34 cm/d scaled to 1.9 cubic feet per second (ft3/s) and the maximum recorded seepage flux of 17.6 cm/d was equivalent to 97 ft3/s. These estimates of groundwater inputs are orders of magnitude less than surface water inputs to the lake.
Nutrient loads were determined from the product of groundwater flow and a representative nutrient concentration. Using the median seepage flux of 1.9 ft3/s, the orthophosphate load ranged from 0.7 to 3.8 pounds of phosphorus per day based on the median and maximum orthophosphate concentrations, respectively. For nitrate plus nitrite, loads ranged from 5.8 to 76.6 pounds of nitrogen per day. Using the maximum value of seepage flux, maximum orthophosphate loads ranged from 35 to 198 pounds of phosphorus per day, and maximum nitrate plus nitrite loads ranged from 296 to 3,943 pound of nitrogen per day. Overall, groundwater nutrient loads are small compared to other sources to the lake. Continued monitoring of future nutrient loads would aid decisions by resource managers as infrastructure within the neighboring residential communities continues to age around Lake Spokane.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215023","collaboration":"Prepared in cooperation with Stevens County Conservation District and Spokane County Conservation District","usgsCitation":"Sheibley, R.W., and Foreman, J.R., 2021, Nitrogen and phosphorus loads from groundwater to Lake Spokane, Spokane, Washington, October 2016–October 2019: U.S. Geological Survey Scientific Investigations Report 2021–5023, 34 p., https://doi.org/10.3133/sir20215023.","productDescription":"Report: vii, 34 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119397","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":397365,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5023/sir20215023.XML"},{"id":385292,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95IQ8HH","text":"USGS data release","description":"USGS data release","linkHelpText":"Water quality and seepage estimates collected at Lake Spokane, Washington, 2016–19."},{"id":385290,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5023/coverthb.jpg"},{"id":385291,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5023/sir20215023.pdf","text":"Report","size":"5.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5023"},{"id":397364,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5023/images"},{"id":402988,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215023/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5023"}],"country":"United States","state":"Washington","otherGeospatial":"Lake Spokane","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.86407470703125,\n 47.76148371616669\n ],\n [\n -117.50976562499999,\n 47.76148371616669\n ],\n [\n -117.50976562499999,\n 47.91173983456231\n ],\n [\n -117.86407470703125,\n 47.91173983456231\n ],\n [\n -117.86407470703125,\n 47.76148371616669\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The Landsat 7 (L7) spacecraft and its instrument, the enhanced thematic mapper plus (ETM+), have been consistently characterized and calibrated since its launch in April of 1999. These performance metrics and calibration updates are determined through the U. S. Geological Survey (USGS) Landsat image assessment system (IAS), which has been performing this function since launch. Starting in 2016, the USGS adopted a tiered collection management structure for its Landsat data products that ensures a consistent method of processing for the Landsat archive within a given collection while allowing a set of calibration updates to be performed between any two given collections. The time frame between 2016 and the end of 2020 was part of the Landsat data Collection 1, in the middle of 2020 was the start of the Landsat Collection 2 data products. The start of a given collection initiates the reprocessing of the Landsat archive, which may involve one or more of a set of updated calibration parameters, improvements in the support data needed for product generation, and improved algorithms used in both the processing flow of products along with the characterization and calibration of the Landsat instruments and spacecraft. This paper discusses only the ETM+ geometric spacecraft and instrument calibration improvements for Collection 2. Three ETM+ calibration updates were made for the ETM+; updates to the thermal band odd-to-even detector alignment, sensor to attitude control system (ACS) alignment, and a cold-to-warm focal plane alignment adjustment. The sensor alignment updates impact only the accuracy of the systematic terrain products (L1GT), which are the products generated before applying any corrections based on the ground control used in registration. The band alignment changes impacted only bands 5, 6, and 7 within the focal plane. Other geometric calibration updates, such as scan mirror alignment, are done on a routine basis and are not part of the Collection 2 updates due to their more dynamic characteristics.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13091638","usgsCitation":"Choate, M., Rengarajan, R., Storey, J.C., and Lubke, M., 2021, Geometric calibration updates to Landsat 7 ETM+ instrument for Landsat Collection 2 products: Remote Sensing, v. 13, no. 9, 1638, 20 p., https://doi.org/10.3390/rs13091638.","productDescription":"1638, 20 p.","ipdsId":"IP-128282","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":452599,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13091638","text":"Publisher Index Page"},{"id":399585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Choate, Mike 0000-0002-8101-4994 choate@usgs.gov","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":4618,"corporation":false,"usgs":true,"family":"Choate","given":"Mike","email":"choate@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":841295,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110 rrengarajan@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":192376,"corporation":false,"usgs":true,"family":"Rengarajan","given":"Rajagopalan","email":"rrengarajan@contractor.usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":841296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":841297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lubke, Mark 0000-0002-7257-2337","orcid":"https://orcid.org/0000-0002-7257-2337","contributorId":261911,"corporation":false,"usgs":false,"family":"Lubke","given":"Mark","email":"","affiliations":[{"id":53079,"text":"KBR, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":841298,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70220269,"text":"70220269 - 2021 - Priority species lists to restore desert tortoise and pollinator habitats in Mojave Desert shrublands","interactions":[],"lastModifiedDate":"2021-04-29T12:26:08.854146","indexId":"70220269","displayToPublicDate":"2021-04-22T07:22:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2821,"text":"Natural Areas Journal","active":true,"publicationSubtype":{"id":10}},"title":"Priority species lists to restore desert tortoise and pollinator habitats in Mojave Desert shrublands","docAbstract":"Mojave Desert shrublands are home to unique plants and wildlife and are experiencing rapid habitat change due to unprecedented large-scale disturbances; yet, established practices to effectively restore disturbed landscapes are not well developed. A priority species list of native plant taxa was developed to guide seed collectors, commercial growers, resource managers, and restoration practitioners in support of the Bureau of Land Management's Mojave Desert Native Plant Program. We identify focal plant taxa that are important for habitats of the threatened Mojave desert tortoise (Gopherus agassizii), a widely distributed herbivore in low and middle elevations, and pollinator taxa, including mostly Lepidopterans and Apoidean bees, some of whose populations are in decline. We identified 201 unique plant taxa in the diets of tortoises, and 49 taxa that provide thermal cover for tortoises with some overlapping taxa that provide both diet and cover. We discuss 134 native pollinators associated with plants used for nectaring, larval hosts, or cover and nesting materials. Detailed plant species accounts describing the status-of-knowledge for 57 plant taxonomic groups including detailed information on life history, ecology, and pollinator syndrome relevant to restoration success, methods of seed harvesting, propagation, and historical use in restoration. Our approach for developing a priority plant species list for the Mojave Desert provides a data-guided listing of species for restoration practitioners and identifies knowledge gaps for future investigation.
A full three-dimensional (3D) potential field model of the central and southern Bushveld Complex reveals information about the Complex in areas obscured by younger geological cover. Previously, two-dimensional gravity models and a few magnetic models limited to certain sections of the Bushveld Complex have been used to propose geometries for the Rustenburg Layered Suite, especially in the western and eastern lobes. These models were often used to support different emplacement models. Although these models provided valuable information, two-and-a-half-dimensional (2.5D) potential field modelling is not well suited to modelling complex 3D geology. Also, in most cases, only the magnetic or gravity data were modelled, but jointly modelling both data sets better constrains the results, as was shown recently for a 3D model of the northern lobe. Joint 3D modelling of regional gravity and magnetic data combined with published crustal thickness models derived from broadband seismic tomography studies and constrained by density and susceptibility data, geologic mapping, boreholes and seismic reflection data were used to create a 3D model of the central and southeastern sections of the Bushveld Complex, as well as the southern part of the northern lobe. The model shows a complex geometry with thick continuous Rustenburg Layered Suite S in most of the western and southeastern lobes, but less continuous Rustenburg Layered Suite in the eastern lobe. Large domes or thick granites and granophyre in the latter interrupt the continuity of the Rustenburg Layered Suite and the western and eastern lobes are strictly speaking only partially connected in places. However, they are not separate intrusions, but one disconnected by pre-existing and synmagmatic updoming. Three possible feeders were modelled in the northern, western, and south-eastern lobes.
What is the U.S. Geological Survey National Water Quality Monitoring Network?
Understanding the water quality of U.S. streams and rivers requires consistent data collection and analysis over decades. The U.S. Geological Survey’s (USGS) National Water Quality Network (NWQN) was established to facilitate national-scale understanding of surface-water quality conditions through the collection of comparable data in large rivers and small streams in different geographic and land-use settings. Data collected by the NWQN support the needs of Federal, State, and local stakeholders tasked with managing our Nation’s water resources.
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U.S. Geological Survey
1217 Biltmore Dr.
Lawrence, KS 66049
The southern margin of the Archean Superior Province in the central Upper Peninsula of Michigan was a nexus for key Paleoproterozoic tectonic events involved in the ~2.1 Ga rifting of proposed Archean supercraton Superia and subsequent assembly of Laurentia. Interpretations of the region’s tectonic history have historically been hampered by extensive Pleistocene glacial and Paleozoic sedimentary cover and a lack of appropriate geophysical data. These rifting and orogenic events formed geologic effects that are readily mappable with modern geophysical methods. New aeromagnetic and gravity data provide a critical means of mapping and interpreting the complex geological framework through cover, allowing development of significantly richer geographical and process-based perspectives on all these tectonic events. Interpretations of Precambrian contacts and structure are here, for the first time, carried >30 km eastward under Paleozoic cover. Effects of ~2.1 Ga rifting are strongly expressed geophysically, including the Dickinson Group, perhaps a unique record of the progression of rift-related sedimentation and magmatism, shown here to be a geographically extensive and largely concealed tectonic feature of the southern Superior Province. The geophysical evidence for plausible ~2.1 Ga rift-related intrusive magmatism includes a previously unrecognized swarm of northeast-striking mafic dikes cutting Archean rocks and gravity lows produced by granites. Effects of the ~1.87–1.83 Ga Penokean orogeny include gravity and magnetic gradients and pattern breaks along the Niagara fault zone suture, abundant evidence for thin-skinned thrusting and folding in the Menominee iron district, and speculative emplacement of an allochthonous sedimentary sequence in the Calumet trough. Numerous east–west trending structures imaged geophysically likely originated, or were significantly reactivated by, post-Penokean deformation. Metamorphic events at ~1.76 Ga and ~1.65 Ga may correspond to orogenies involving younger, outboard Paleoproterozoic crustal provinces recognized in southern Laurentia. For example, the previously unrecognized West Branch fault, separating the Dickinson Group from Archean rocks, is shown to be a major structure in the region, and is a proposed expression of ~1.76 Ga thick-skinned deformation. Oblique disruptions of crudely east–west striking structures have robust geophysical expressions and are speculatively connected to transpressive deformation at ~1.65 Ga. These new geophysical observations and interpretations collectively help illuminate a critical period in the tectonic evolution of Laurentia, as it transitioned from a disparate array of Archean cratons to a more coherent, growing continent.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.precamres.2021.106205","usgsCitation":"Drenth, B.J., Cannon, W.F., Schulz, K.J., and Ayuso, R.A., 2021, Geophysical insights into Paleoproterozoic tectonics along the southern margin of the Superior Province, central Upper Peninsula, Michigan, USA: Precambrian Research, v. 359, 106205, 19 p., https://doi.org/10.1016/j.precamres.2021.106205.","productDescription":"106205, 19 p.","ipdsId":"IP-121384","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":452613,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.precamres.2021.106205","text":"Publisher Index Page"},{"id":436400,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99X3X07","text":"USGS data release","linkHelpText":"Data Release - Geologic map of the central Upper Peninsula, Michigan"},{"id":385578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin, Michigan","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.8017578125,\n 43.99281450048989\n ],\n [\n -86.81396484375,\n 43.99281450048989\n ],\n [\n -86.81396484375,\n 47.81315451752768\n ],\n [\n -91.8017578125,\n 47.81315451752768\n ],\n [\n -91.8017578125,\n 43.99281450048989\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"359","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Drenth, Benjamin J. 0000-0002-3954-8124 bdrenth@usgs.gov","orcid":"https://orcid.org/0000-0002-3954-8124","contributorId":1315,"corporation":false,"usgs":true,"family":"Drenth","given":"Benjamin","email":"bdrenth@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":815467,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cannon, William F. 0000-0002-2699-8118","orcid":"https://orcid.org/0000-0002-2699-8118","contributorId":201972,"corporation":false,"usgs":true,"family":"Cannon","given":"William","email":"","middleInitial":"F.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":815468,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schulz, Klaus J. 0000-0003-2967-4765 kschulz@usgs.gov","orcid":"https://orcid.org/0000-0003-2967-4765","contributorId":2438,"corporation":false,"usgs":true,"family":"Schulz","given":"Klaus","email":"kschulz@usgs.gov","middleInitial":"J.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":815469,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ayuso, Robert A. 0000-0002-8496-9534 rayuso@usgs.gov","orcid":"https://orcid.org/0000-0002-8496-9534","contributorId":2654,"corporation":false,"usgs":true,"family":"Ayuso","given":"Robert","email":"rayuso@usgs.gov","middleInitial":"A.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":815470,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248969,"text":"70248969 - 2021 - The paleogeography of Laurentia in its early years: New constraints from the Paleoproterozoic East-Central Minnesota batholith","interactions":[],"lastModifiedDate":"2023-09-27T16:15:21.287912","indexId":"70248969","displayToPublicDate":"2021-04-20T11:02:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"The paleogeography of Laurentia in its early years: New constraints from the Paleoproterozoic East-Central Minnesota batholith","docAbstract":"The ca. 1.83 Ga Trans-Hudson orogeny resulted from collision of an upper plate consisting of the Hearne, Rae, and Slave provinces with a lower plate consisting of the Superior province. While the geologic record of ca. 1.83 Ga peak metamorphism within the orogen suggests that these provinces were a single amalgamated craton from this time onward, a lack of paleomagnetic poles from the Superior province following Trans-Hudson orogenesis has made this coherency difficult to test. We develop a high-quality paleomagnetic pole for northeast-trending diabase dikes of the post-Penokean orogen East-Central Minnesota Batholith (pole longitude: 265.8°; pole latitude: 20.4°; A95: 4.5°; K: 45.6 N: 23) whose age we constrain to be 1,779.1 ± 2.3 Ma (95% CI) with new U-Pb dates. Demagnetization and low-temperature magnetometry experiments establish dike remanence be held by low-Ti titanomagnetite. Thermochronology data constrain the intrusions to have cooled below magnetite blocking temperatures upon initial emplacement with a mild subsequent thermal history within the stable craton. The similarity of this new Superior province pole with poles from the Slave and Rae provinces establishes the coherency of Laurentia following Trans-Hudson orogenesis. This consistency supports interpretations that older discrepant 2.22–1.87 Ga pole positions between the provinces are the result of differential motion through mobile-lid plate tectonics. The new pole supports the northern Europe and North America connection between the Laurentia and Fennoscandia cratons. The pole can be used to jointly reconstruct these cratons ca. 1,780 Ma strengthening the paleogeographic position of these major constituents of the hypothesized late Paleoproterozoic supercontinent Nuna.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021TC006751","usgsCitation":"Swanson-Hysell, N.L., Avery, M.S., Zhang, Y., Hodgin, E.B., Sherwood, R.J., Apen, F., Boerboom, T.J., Keller, C.B., and Cottle, J.M., 2021, The paleogeography of Laurentia in its early years: New constraints from the Paleoproterozoic East-Central Minnesota batholith: Tectonics, v. 40, no. 5, e2021TC006751, 22 p., https://doi.org/10.1029/2021TC006751.","productDescription":"e2021TC006751, 22 p.","ipdsId":"IP-126588","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":452615,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/14z7z5fj","text":"External Repository"},{"id":421260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"East-Central Minnesota Batholith","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.2163,\n 45.54\n ],\n [\n -94.2163,\n 45.516\n ],\n [\n -94.255,\n 45.516\n ],\n [\n -94.255,\n 45.54\n ],\n [\n -94.2163,\n 45.54\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Swanson-Hysell, Nicholas L. 0000-0003-3215-4648","orcid":"https://orcid.org/0000-0003-3215-4648","contributorId":330223,"corporation":false,"usgs":false,"family":"Swanson-Hysell","given":"Nicholas","email":"","middleInitial":"L.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":884374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Avery, Margaret Susan 0000-0002-8504-7072","orcid":"https://orcid.org/0000-0002-8504-7072","contributorId":329991,"corporation":false,"usgs":true,"family":"Avery","given":"Margaret","email":"","middleInitial":"Susan","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":884375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Yiming","contributorId":330224,"corporation":false,"usgs":false,"family":"Zhang","given":"Yiming","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":884376,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hodgin, Eben B.","contributorId":330225,"corporation":false,"usgs":false,"family":"Hodgin","given":"Eben","email":"","middleInitial":"B.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":884377,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sherwood, Robert J.","contributorId":330226,"corporation":false,"usgs":false,"family":"Sherwood","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":884378,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Apen, Francisco E.","contributorId":330227,"corporation":false,"usgs":false,"family":"Apen","given":"Francisco E.","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":884379,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boerboom, Terrence J.","contributorId":330228,"corporation":false,"usgs":false,"family":"Boerboom","given":"Terrence","email":"","middleInitial":"J.","affiliations":[{"id":38105,"text":"Minnesota Geological Survey","active":true,"usgs":false}],"preferred":false,"id":884380,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Keller, C. Brenhin 0000-0001-7400-9428","orcid":"https://orcid.org/0000-0001-7400-9428","contributorId":330229,"corporation":false,"usgs":false,"family":"Keller","given":"C.","email":"","middleInitial":"Brenhin","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":884381,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cottle, John M. 0000-0002-3966-6315","orcid":"https://orcid.org/0000-0002-3966-6315","contributorId":330230,"corporation":false,"usgs":false,"family":"Cottle","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":884382,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70220326,"text":"70220326 - 2021 - Shear-wave velocity site characterization in Oklahoma from joint inversion of multi-method surface seismic measurements: Implications for central U.S. Ground Motion Prediction","interactions":[],"lastModifiedDate":"2021-08-03T14:20:20.365226","indexId":"70220326","displayToPublicDate":"2021-04-20T09:25:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8581,"text":"Bulletin Seismological Society America","active":true,"publicationSubtype":{"id":10}},"title":"Shear-wave velocity site characterization in Oklahoma from joint inversion of multi-method surface seismic measurements: Implications for central U.S. Ground Motion Prediction","docAbstract":"We analyze multimethod shear (SH)‐wave velocity (
Assessing the scope and severity of threats is necessary for evaluating impacts on populations to inform conservation planning. Quantitative threat assessment often requires monitoring programs that provide reliable data over relevant spatial and temporal scales, yet such programs can be difficult to justify until there is an apparent stressor. Leveraging efforts of wildlife management agencies to record winter counts of hibernating bats, we collated data for 5 species from over 200 sites across 27 U.S. states and 2 Canadian provinces from 1995 to 2018 to determine the impact of white-nose syndrome (WNS), a deadly disease of hibernating bats. We estimated declines of winter counts of bat colonies at sites where the invasive fungus that causes WNS (Pseudogymnoascus destructans) had been detected to assess the threat impact of WNS. Three species undergoing species status assessment by the U.S. Fish and Wildlife Service (Myotis septentrionalis, Myotis lucifugus, and Perimyotis subflavus) declined by more than 90%, which warrants classifying the severity of the WNS threat as extreme based on criteria used by NatureServe. The scope of the WNS threat as defined by NatureServe criteria was large (36% of Myotis lucifugus range) to pervasive (79% of Myotis septentrionalis range) for these species. Declines for 2 other species (Myotis sodalis and Eptesicus fuscus) were less severe but still qualified as moderate to serious based on NatureServe criteria. Data-sharing across jurisdictions provided a comprehensive evaluation of scope and severity of the threat of WNS and indicated regional differences that can inform response efforts at international, national, and state or provincial jurisdictions. We assessed the threat impact of an emerging infectious disease by uniting monitoring efforts across jurisdictional boundaries and demonstrated the importance of coordinated monitoring programs, such as the North American Bat Monitoring Program (NABat), for data-driven conservation assessments and planning.
Hoary bats (Lasiurus cinereus) are among the bat species most commonly killed by wind turbine strikes in the midwestern United States. The impact of this mortality on species census size is not understood, due in part to the difficulty of estimating population size for this highly migratory and elusive species. Genetic effective population size (Ne) could provide an index of changing census population size if other factors affecting Ne are stable.
We used the NeEstimator package to derive effective breeding population size (Nb) estimates for two temporally spaced cohorts: 93 hoary bats collected in 2009–2010 and an additional 93 collected in 2017–2018. We sequenced restriction-site associated polymorphisms and generated a de novo genome assembly to guide the removal of sex-linked and multi-copy loci, as well as identify physically linked markers.
Analysis of the reference genome with psmc suggested at least a doubling of Ne in the last 100,000 years, likely exceeding Ne = 10,000 in the Holocene. Allele and genotype frequency analyses confirmed that the two cohorts were comparable, although some samples had unusually high or low observed heterozygosities. Additionally, the older cohort had lower mean coverage and greater variability in coverage, and batch effects of sampling locality were observed that were consistent with sample degradation. We therefore excluded samples with low coverage or outlier heterozygosity, as well as loci with sequence coverage far from the mode value, from the final data set. Prior to excluding these outliers, contemporary Nb estimates were significantly higher in the more recent cohort, but this finding was driven by high values for the 2018 sample year and low values for all other years. In the reduced data set, Nb did not differ significantly between cohorts. We found base substitutions to be strongly biased toward cytosine to thymine or the complement, and further partitioning loci by substitution type had a strong effect on Nb estimates. Minor allele frequency and base quality bias thresholds also had strong effects on Nb estimates. Instability of Nb with respect to common data filtering parameters and empirically identified factors prevented robust comparison of the two cohorts. Given that confidence intervals frequently included infinity as the stringency of data filtering increased, contemporary trends in Nb of North American hoary bats may not be tractable with the linkage disequilibrium method, at least using the protocol employed here.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.11285","usgsCitation":"Cornman, R.S., Fike, J., Oyler-McCance, S.J., and Cryan, P.M., 2021, Historical effective population size of North American hoary bat (Lasiurus cinereus) and challenges to estimating trends in contemporary effective breeding population size from archived samples: PeerJ, v. 9, e11285, 27 p., https://doi.org/10.7717/peerj.11285.","productDescription":"e11285, 27 p.","ipdsId":"IP-125432","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":452631,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.11285","text":"Publisher Index Page"},{"id":436402,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VSG54Z","text":"USGS data release","linkHelpText":"Genetic variation in hoary bats (Lasiurus cinereus) assessed from archived samples"},{"id":385284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":814602,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":814603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":814604,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cryan, Paul M. 0000-0002-2915-8894 cryanp@usgs.gov","orcid":"https://orcid.org/0000-0002-2915-8894","contributorId":147942,"corporation":false,"usgs":true,"family":"Cryan","given":"Paul","email":"cryanp@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":814605,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224635,"text":"70224635 - 2021 - Global resorption efficiencies of trace elements in leaves of terrestrial plants","interactions":[],"lastModifiedDate":"2021-10-01T13:12:13.744382","indexId":"70224635","displayToPublicDate":"2021-04-19T08:10:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1711,"text":"Functional Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Global resorption efficiencies of trace elements in leaves of terrestrial plants","docAbstract":"Researchers at the U.S. Geological Survey (USGS) and their collaborators conducted a study of the geochemical properties of coals currently produced for electric power generation in the Illinois Basin in Illinois and Indiana. The study follows from recommendations by an expert panel for the USGS to investigate the distribution and controls of trace constituents such as mercury (Hg) in Illinois Basin coals and the behavior of these constituents in coal preparation. A total of 72 new samples were collected by USGS collaborators between 2015 and 2017. These samples include raw coals, prepared coals, and waste coals from coal preparation. To understand the geochemistry and cleaning behavior of these coals, these samples were subjected to an integrated series of analyses described here, including microanalysis of coal constituents and bulk sample chemical analysis. Of the procedures used, whole-sample Hg analysis quantified overall mercury contents and its reduction by coal preparation. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) of pyrite in coal quantified Hg and other potentially harmful elements contained in pyrite, the most likely host of these constituents. Trace elements investigated include those whose emissions are regulated under the U.S. Environmental Protection Agency Mercury and Air Toxics Standards. This report and the corresponding data release, serve as an archive for geochemical data obtained in our study of the geochemistry of Illinois Basin coals. Material included in this report also define approaches used by the USGS over the period of study to characterize coal samples, requiring combined use of results from USGS and non-USGS laboratories.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1135","usgsCitation":"Kolker, A., Scott, C., Lefticariu, L., Mastalerz, M., Drobniak, A., and Scott, A., 2021, Geochemical data for Illinois Basin coal samples, 2015–2018: U.S. Geological Survey Data Series 1135, 14 p., https://doi.org/10.3133/ds1135.","productDescription":"Report: vii, 14 p.; Data Release","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112300","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":383452,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1135/coverthb.jpg"},{"id":383454,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GUURCK","text":"USGS data release","linkHelpText":"Geochemical data for Illinois Basin coal samples, 2015–2018"},{"id":383453,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1135/ds1135.pdf","text":"Report","size":"4.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1135"}],"country":"United States","state":"Illinois","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.56103515625,\n 40.91351257612758\n ],\n [\n -87.71484375,\n 41.57436130598913\n ],\n [\n -88.76953125,\n 42.13082130188811\n ],\n [\n -89.53857421875,\n 42.114523952464246\n ],\n [\n -90.15380859375,\n 41.376808565702355\n ],\n [\n -91.07666015625,\n 40.79717741518766\n ],\n [\n -91.38427734374999,\n 40.04443758460856\n ],\n [\n -90.76904296874999,\n 39.095962936305476\n ],\n [\n -90.24169921875,\n 38.53097889440024\n ],\n [\n -90.19775390625,\n 38.13455657705411\n ],\n [\n -89.56054687499999,\n 37.70120736474139\n ],\n [\n -89.36279296875,\n 37.10776507118514\n ],\n [\n -89.31884765624999,\n 37.020098201368114\n ],\n [\n -88.70361328125,\n 37.125286284966805\n ],\n [\n -88.26416015625,\n 37.31775185163688\n ],\n [\n -88.06640625,\n 37.735969208590504\n ],\n [\n -87.5830078125,\n 38.58252615935333\n ],\n [\n -87.5390625,\n 39.26628442213066\n ],\n [\n -87.56103515625,\n 40.91351257612758\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Geology, Energy & Minerals Science Center
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12201 Sunrise Valley Drive
954 National Center
Reston, VA 20192
Coking coal, or metallurgical coal, has been produced in the United States for nearly 200 years. Coking coal is primarily used in the production of coke for use in the steel industry, and for other uses (for example, foundries, blacksmithing, heating buildings, and brewing). Currently, U.S. coking coal is produced in Alabama, Arkansas, Pennsylvania, Virginia , and West Virginia. Historically, coking coal has also been produced in 15 other states (Alaska, Colorado, Georgia, Illinois, Indiana, Kentucky, Maryland, Montana, New Mexico, Ohio, Oklahoma, Tennessee, Utah, Washington, and Wyoming), but currently is not. Coals from the Appalachian, Arkoma, and Illinois basins are Pennsylvanian in age, while coals in Alaska, Colorado, Montana, New Mexico, Utah, Washington, and Wyoming range in age from Early Cretaceous through Eocene.
This Open-File Report presents the geographic locations of current and historical coking coal deposits of the United States, with additional information about recent and historical mining and exploration activities. Chemical, rheological, petrographic, and other criteria for evaluating the coking potential of coals are discussed, and historical data for coking coals in the United States are presented. In addition, new coking coal samples from Alabama, Arkansas, Kentucky, and Oklahoma were collected and analyzed for this report, and the data are presented in multiple tables, including proximate and ultimate analyses; calorific value; sulfur forms; major-, minor-, and trace-element abundances; Free-Swelling Index; Gieseler Plastometer analyses; American Society for Testing and Materials (ASTM) dilatation; coal petrography; and predicted values of Coal Stability Factor and Coal Strength after Reaction with CO2 (pCSF and pCSR, respectively). Data from previously analyzed coking coal samples in Kentucky, Pennsylvania, Virginia, and West Virginia were supplied by three companies, including results from all the tests listed above, plus oxidation, Hardgrove Grindability Index, and ash fusion (in a reducing environment) temperatures are also presented in tables in the report.
Geographic Information System (GIS) data compiled for this project are available for download for public and private utilization and may be used to create maps for a variety of energy resource studies. These GIS data are in shapefile format, and metadata files are included describing all GIS processing. Additional geographic information about coking coal areas of the United States are also presented in tabular format in the report, including the following: names of coal basins, fields, regions, districts, and areas; coal beds or zones; geographic locations including States, counties, towns, rivers, mountains, etc.; stratigraphic hierarchy and age of the coal-bearing interval; coking characteristics including sulfur content, ash yield, volatile matter, moisture, calorific value, and Free-Swelling Index; coal rank; names of coal mines and coal-mining companies; current and past mining activity; and references for reports about the coal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201113","usgsCitation":"Trippi, M.H., Ruppert, L.F., Eble, C.F., and Hower, J.C., 2021, Coking coal of the United States—Modern and historical coking coal mining locations and chemical, rheological, petrographic, and other data from modern samples: U.S. Geological Survey Open-File Report 2020–1113, 112 p., https://doi.org/10.3133/ofr20201113.","productDescription":"Report: xi, 112 p.; Tables 1.1-21.1; Data Release; Metadata; Spatial Data","numberOfPages":"112","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-111543","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":381899,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1113/ofr20201113_appendix_tables_csv.zip","text":"Tables","size":"88.9 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Zip file of tables 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U.S. Geological Survey
12201 Sunrise Valley Drive
954 National Center
Reston, VA 20192
Salinity in the Colorado River Basin causes an estimated $300 to $400 million per year in economic damages in the U.S. To inform and improve salinity‐control efforts, this study quantifies long‐term trends in salinity (dissolved solids) across the Upper Colorado River Basin (UCRB), including time periods prior to the construction of large dams and preceding the implementation of salinity‐control projects. Weighted Regressions on Time, Discharge, and Season was used with datasets of dissolved‐solids and specific‐conductance measurements, collected as early as 1929, to evaluate long‐term trends in dissolved‐solids loads and concentrations in streams from 1929 to 2019 (n=14). Results indicate that large, widespread, and sustained downward trends in dissolved‐solids concentrations and loads occurred over the last 50 to 90 years. For 12 of the 14 stream sites with significant downward change, median declines of ‐38% (range of ‐14 to ‐57%) and ‐40% (range of ‐9 to ‐65%) were observed for flow‐normalized concentration and load, respectively. Steepest rates of decline occurred from 1980 to 2000, coincident with the initiation of salinity‐control efforts in the 1980s. However, there was a consistent slowing or reversing of downward trends after 2000 even though salinity‐control efforts continued. Significant decreases in salinity occurred as early as the 1940s at some streams, indicating that, in addition to salinity‐control projects, changes in land cover, land use, and/or climate substantially affect salinity transport in the UCRB. Observed dissolved‐solids trends are likely the result of changes to watershed‐related processes, not due to changes in the streamflow regime.
Archival geolocators have transformed the study of small, migratory organisms but analysis of data from these devices requires bias correction because tags are only recovered from individuals that survive and are re-captured at their tagging location. We show that integrating geolocator recovery data and mark–resight data enables unbiased estimates of both migratory connectivity between breeding and nonbreeding populations and region-specific survival probabilities for wintering locations. Using simulations, we first demonstrate that an integrated Bayesian model returns unbiased estimates of transition probabilities between seasonal ranges. We also used simulations to determine how different sampling designs influence the estimability of transition probabilities. We then parameterized the model with tracking data and mark–resight data from declining Painted Bunting (Passerina ciris) populations breeding in the eastern United States, hypothesized to be threatened by the illegal pet trade in parts of their Caribbean, nonbreeding range. Consistent with this hypothesis, we found that male buntings wintering in Cuba were 20% less likely to return to the breeding grounds than birds wintering elsewhere in their range. Improving inferences from archival tags through proper data collection and further development of integrated models will advance our understanding of the full annual cycle ecology of migratory species.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duab010","usgsCitation":"Rushing, C.S., Van Tatenhove, A.M., Sharp, A., Ruiz-Gutierrez, V., Freeman, M., Sykes, P.W., Given, A.M., and Sillett, T., 2021, Integrating tracking and resight data enables unbiased inferences about migratory connectivity and winter range survival from archival tags: Ornithological Applications, v. 123, no. 2, duab010, https://doi.org/10.1093/ornithapp/duab010.","productDescription":"duab010","ipdsId":"IP-118948","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":452649,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duab010","text":"Publisher Index Page"},{"id":387261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Rushing, Clark S","contributorId":237020,"corporation":false,"usgs":false,"family":"Rushing","given":"Clark","email":"","middleInitial":"S","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":819483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Tatenhove, Aimee M","contributorId":261211,"corporation":false,"usgs":false,"family":"Van Tatenhove","given":"Aimee","email":"","middleInitial":"M","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":819484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sharp, Andrew","contributorId":261213,"corporation":false,"usgs":false,"family":"Sharp","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":819485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruiz-Gutierrez, Viviana","contributorId":261212,"corporation":false,"usgs":false,"family":"Ruiz-Gutierrez","given":"Viviana","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":819486,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819488,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sykes, Paul W.","contributorId":214917,"corporation":false,"usgs":false,"family":"Sykes","given":"Paul","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":819489,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Given, Aaron M.","contributorId":49474,"corporation":false,"usgs":true,"family":"Given","given":"Aaron","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":819490,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sillett, T. Scott","contributorId":80788,"corporation":false,"usgs":false,"family":"Sillett","given":"T. Scott","affiliations":[{"id":7035,"text":"Smithsonian Conservation Biology Institute, National Zoological Park","active":true,"usgs":false}],"preferred":false,"id":819487,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70223435,"text":"70223435 - 2021 - Landscape characterization of floral resources for pollinators in the Prairie Pothole Region of the United States","interactions":[],"lastModifiedDate":"2021-08-26T16:05:22.364676","indexId":"70223435","displayToPublicDate":"2021-04-17T10:22:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1006,"text":"Biodiversity and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Landscape characterization of floral resources for pollinators in the Prairie Pothole Region of the United States","docAbstract":"Across agricultural areas of the Prairie Pothole Region (PPR), floral resources are primarily found on public grasslands, roadsides, and private grasslands used as pasture or enrolled in federal conservation programs. Little research has characterized the availability of flowers across the region or identified the primary stakeholders managing lands supporting pollinators. We explored spatial and temporal variability in flower abundance and richness across multiple grassland categories (i.e. general grassland, conservation grassland, and engineered pollinator habitat) in the PPR from 2015 to 2018 and used these data to estimate the number of flowering stems present across the region on private and public land holdings. Both flowering plant abundance and richness were greatest on engineered pollinator habitat, but this land category encompassed <0.01% of the total grassland area in the PPR. There was a steady decrease in flower abundance over the growing season across all land categories. We detected considerable variation in flower abundance and richness across grassland categories, indicating that not all natural or semi-natural covers provide similar value to pollinators. At a landscape scale, large land holdings such as privately-owned grasslands and Conservation Reserve Program lands contributed the greatest number of flowers by an order of magnitude, though these lands collectively did not support the greatest abundance of flowers per unit area. Our research depicts spatial and temporal variation in pollinator resources across the region. Further, our research will assist managers and policy makers in understanding the role of public and private lands and conservation programs in supporting pollinators.
","language":"English","publisher":"Springer","doi":"10.1007/s10531-021-02177-9","usgsCitation":"Smart, A.H., Otto, C., Gallant, A.L., and Simanonok, M., 2021, Landscape characterization of floral resources for pollinators in the Prairie Pothole Region of the United States: Biodiversity and Conservation, v. 30, p. 1991-2015, https://doi.org/10.1007/s10531-021-02177-9.","productDescription":"25 p.","startPage":"1991","endPage":"2015","ipdsId":"IP-123388","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":388547,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, North Dakota, South Dakota","otherGeospatial":"Prairie Pothole Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.0625,\n 48.019324184801185\n ],\n [\n -103.02978515625,\n 48.06339653776211\n ],\n [\n -102.76611328125,\n 47.82790816919329\n ],\n [\n -102.45849609375,\n 47.45780853075031\n ],\n [\n -101.689453125,\n 47.47266286861342\n ],\n [\n -100.8984375,\n 46.63435070293566\n ],\n [\n -100.6787109375,\n 45.55252525134013\n ],\n [\n -101.09619140625,\n 44.653024159812\n ],\n [\n -99.580078125,\n 43.91372326852401\n ],\n [\n -99.60205078124999,\n 43.59630591596548\n ],\n [\n -98.32763671875,\n 42.76314586689492\n ],\n [\n -97.80029296875,\n 42.8115217450979\n ],\n [\n -96.6796875,\n 42.58544425738491\n ],\n [\n -96.3720703125,\n 43.58039085560784\n ],\n [\n -92.30712890625,\n 43.50075243569041\n ],\n [\n -93.75732421875,\n 45.38301927899065\n ],\n [\n -97.2509765625,\n 48.99463598353405\n ],\n [\n -104.08447265624999,\n 49.009050809382046\n ],\n [\n -104.0625,\n 48.019324184801185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","noUsgsAuthors":false,"publicationDate":"2021-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Smart, Autumn H. 0000-0003-0711-3035","orcid":"https://orcid.org/0000-0003-0711-3035","contributorId":228828,"corporation":false,"usgs":true,"family":"Smart","given":"Autumn","email":"","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":822058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":822036,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":822059,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simanonok, Michael P. 0000-0002-4710-4515","orcid":"https://orcid.org/0000-0002-4710-4515","contributorId":229685,"corporation":false,"usgs":true,"family":"Simanonok","given":"Michael P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":822060,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219983,"text":"ofr20211033 - 2021 - Connectivity of Mojave Desert tortoise populations—Management implications for maintaining a viable recovery network","interactions":[],"lastModifiedDate":"2021-04-19T11:44:39.479074","indexId":"ofr20211033","displayToPublicDate":"2021-04-16T12:10:46","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1033","displayTitle":"Connectivity of Mojave Desert Tortoise Populations: Management Implications for Maintaining a Viable Recovery Network","title":"Connectivity of Mojave Desert tortoise populations—Management implications for maintaining a viable recovery network","docAbstract":"The historic distribution of Mojave desert tortoises (Gopherus agassizii) was relatively continuous across the range, and the importance of tortoise habitat outside of designated tortoise conservation areas (TCAs) to recovery has long been recognized for its contributions to supporting gene flow between TCAs and to minimizing impacts and edge effects within TCAs. However, connectivity of Mojave desert tortoise populations has become a concern because of recent and proposed development of large tracts of desert tortoise habitat that cross, fragment, and surround designated conservation areas. This paper summarizes the underlying concepts and importance of connectivity for Mojave desert tortoise populations by reviewing current information on connectivity and providing information to managers for maintaining or enhancing desert tortoise population connectivity as they consider future proposals for development and management actions.
Maintaining an ecological network for the Mojave desert tortoise, with a system of core habitats (TCAs) connected by linkages, is necessary to support demographically viable populations and long-term gene flow within and between TCAs. There are four points for wildlife and land-management agencies to consider when making decisions that could affect connectivity of Mojave desert tortoise populations (for example, in updating actions in resource management plans or amendments that could help maintain or restore functional connectivity in light of the latest information):
Each TCA has unique strengths and weaknesses regarding its ability to support minimum sustainable populations based on areal extent and its ability to support population increases based on landscape connection with adjacent populations. Considering how proposed projects (inside or outside of TCAs) affect connectivity and the ability of TCAs to support at least 5,000 adult tortoises (the numerical goal for each TCA) could help managers to maintain the resilience of TCAs to population declines. The same project, in an alternative location, could have very different impacts on local and regional populations. For example, within the habitat matrix surrounding TCAs, narrowly delineated corridors may not allow for natural population dynamics if they do not accommodate overlapping home ranges along most of their widths so that tortoises reside, grow, find mates, and produce offspring that can replace older tortoises. In addition, most habitat outside TCAs may receive more surface disturbance than habitat within TCAs. Therefore, managing the entire remaining matrix of desert tortoise habitat for permeability may be better than delineating fixed corridors. These concepts apply, especially given uncertainty about long-term condition of habitat, within and outside of TCAs under a changing climate.
Ultimately, questions such as “What are the critical linkages that need to be protected?” could be better framed as “How can we manage the remaining habitat matrix in ways that sustain ecological processes and habitat suitability for special status species?” Land-management decisions made in the context of the latter question may be more conducive to maintenance of a functional ecological network.
In California, the Bureau of Land Management established 0.1–1.0 percent caps on new surface-disturbance for TCAs and mapped linkages that address the issues described in number 1 of this list.
Nevada, Utah, and Arizona currently do not have surface-disturbance limits. Limits comparable to those in the Desert Renewable Energy Conservation Plan (DRECP) would be 0.5 percent within TCAs and 1 percent within the linkages modeled by Averill-Murray and others (2013). Limits in some areas of California within the Desert Renewable Energy Conservation Plan, such as Ivanpah Valley, are more restrictive, at 0.1 percent. Continuity across the state line in Nevada could be achieved with comparable limits in the adjacent portion of Ivanpah Valley, as well as the Greater Trout Canyon Translocation Area and the Stump Springs Regional Augmentation Site. These more restrictive limits would help protect remaining habitat in the major interstate connectivity pathway through Ivanpah Valley and focal areas of population augmentation that provide additional population connectivity along the western flank of the Spring Mountains.
In a recent study that analyzed 13 years of desert tortoise monitoring data, nearly all desert tortoise observations were at sites in which 5 percent or less of the surrounding landscape within 1 kilometer was disturbed (Carter and others, 2020a). To help maintain tortoise habitability and permeability across all other non-conservation-designated tortoise habitat, all surface disturbance could be limited to less than 5-percent development per square kilometer because the 5-percent threshold for development is the point at which tortoise occupation drops precipitously (Carter and others, 2020a). However, although individual desert tortoises were observed at development levels up to 5 percent, we do not know the fitness or reproductive characteristics of these individuals. This level of development also may not allow for long-term persistence of healthy populations that are of adequate size needed for demographic or functional connectivity; therefore, a conservative interpretation suggests that, ideally, development could be lower. Lower development levels would be particularly useful in areas within the upper 5th percentile of connectivity values modeled by Gray and others (2019).
Reducing ancillary threats in places where connectivity is restricted to narrow strips of habitat, for example, narrow mountain passes or vegetated strips between solar development, could enhance the functionality of these vulnerable linkages. In such areas, maintaining multiple, redundant linkages could further enhance overall connectivity.
Minimization of mortality from roads and maximization of passage under roads. Roads pose a significant threat to the long-term persistence of local tortoise populations, and roads of high traffic volume lead to severe population declines, which ultimately fragments populations farther away from the roads. Three points (a.–c.) pertain to reducing direct mortality of tortoises on the many paved roads that cross desert tortoise habitat and to maintaining a minimal level of permeability across these roads:
Tortoise-exclusion fencing tied into culverts, underpasses, overpasses, or other passages below roads in desert tortoise habitat, would limit vehicular mortality of tortoises and provide opportunities for movement across the roads. Installation of shade structures on the habitat side of fences installed in areas with narrow population-depletion zones would limit overheating of tortoises that may pace the fence.
Passages below highways could be maintained or retrofitted to ensure safe tortoise access, for example, by filling eroded drop-offs or modifying erosion-control features such as rip-rap to make them safer and more passable for tortoises. Wildlife management agencies could work with transportation departments to develop construction standards that are consistent with hydrologic/erosion management goals, while also incorporating a design and materials consistent with tortoise survival and passage and make the standards widely available. The process would be most effective if the status of passages was regularly monitored and built into management plans.
Healthy tortoise populations along fenced highways could be supported by ensuring that land inside tortoise-exclusion fences is not so degraded that it leads to degradation of tortoise habitat outside the exclusion areas. For example, severe invasive plant infestations inside a highway exclusion could cause an increase of invasive plants outside the exclusion area and degrade habitat; therefore, invasive plants inside road rights of way could be mown or treated with herbicide to limit their spread into adjacent tortoise habitat and minimize the risk of these plants carrying wildfires into adjacent habitat.
Adaptation of management based on new information. Future research will continue to build upon and refine models related to desert tortoise population connectivity and develop new ones. New models could consider landscape levels of development and be constructed such that they share common foundations to support future synthesis efforts. If model development was undertaken in partnership with entities that are responsible for management of desert tortoise habitat, it would facilitate incorporation of current and future modeling results into their land management decisions. There are specific topics that may be clarified with further evaluation:
The effects of climate change on desert tortoise habitat, distribution, and population connectivity;
The effects of large-scale fires, especially within repeatedly burned habitat, on desert tortoise distribution and population connectivity;
The ability of solar energy facilities or similar developments to support tortoise movement and presence by leaving washes intact; leaving native vegetation intact whenever possible, or if not possible, mowing the site, allowing vegetation to re-sprout, and managing weeds; and allowing tortoises to occupy the sites; and
The design and frequency of underpasses necessary to maintain functional demographic and genetic connectivity across linear features, like highways.
Wildlife Program
Prepared in cooperation with the U.S. Fish and Wildlife Service
","usgsCitation":"Averill-Murray, R.C., Esque, T.C., Allison, L.J., Bassett, S., Carter, S.K., Dutcher, K.E., Hromada, S.J., Nussear, K.E., and Shoemaker, K., 2021, Connectivity of Mojave Desert tortoise populations—Management implications for maintaining a viable recovery network: U.S. Geological Survey Open-File Report 2021–1033, 23 p., https://doi.org/10.3133/ofr20211033.","productDescription":"vi, 23 p.","numberOfPages":"23","onlineOnly":"Y","ipdsId":"IP-125269","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":385161,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1033/covrthb.jpg"},{"id":385162,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1033/ofr20211033.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":385163,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1033/images"},{"id":385164,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1033/ofr20211033.xml"}],"country":"United States","state":"Arizona, California, Nevada","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.71923828124999,\n 33.669496972795535\n ],\n [\n -113.8623046875,\n 33.578014746143985\n ],\n [\n -112.69775390625,\n 33.50475906922609\n ],\n [\n -111.51123046875,\n 33.284619968887675\n ],\n [\n -111.73095703125,\n 34.10725639663118\n ],\n [\n -111.9287109375,\n 35.51434313431818\n ],\n [\n -113.00537109375,\n 36.24427318493909\n ],\n [\n -114.3896484375,\n 36.73888412439431\n ],\n [\n -115.86181640625001,\n 37.07271048132943\n ],\n [\n -117.42187500000001,\n 37.68382032669382\n ],\n [\n -118.27880859375001,\n 37.579412513438385\n ],\n [\n -117.7734375,\n 35.97800618085566\n ],\n [\n -117.72949218749999,\n 35.44277092585766\n ],\n [\n -118.76220703125001,\n 34.75966612466248\n ],\n [\n -117.99316406249999,\n 34.488447837809304\n ],\n [\n -116.74072265625,\n 34.288991865037524\n ],\n [\n -114.71923828124999,\n 33.669496972795535\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Interactions between climate and hydrogeologic settings contribute to the hydrologic and chemical variability among depressional wetlands, which influences their aquatic communities. These interactions and resulting variability have led to inconsistent results in terms of identifying reliable predictors of aquatic-macroinvertebrate community composition for depressional wetlands. This is especially true in the Prairie Pothole Region of North America where, in addition to pronounced climate variability, studies are often confounded by fish introductions. We used environmental monitoring data collected over a 24-year period from a complex of sixteen depressional wetlands and structural equation modeling techniques that incorporated theoretical and empirical relationships outlined in the Wetland Continuum to identify key environmental (climate and hydrogeologic setting) and biotic (competition and predation) drivers of aquatic-macroinvertebrate community composition for prairie-pothole wetlands. Uplands in the study area were primarily native prairie, thus, embedded wetlands were impacted minimally by agricultural influences. Additionally, study wetlands were predominately fishless. In the absence of the overwhelming influence of fishes, major drivers influencing aquatic-macroinvertebrate communities were revealed through the use of data spanning multidecadal-long climate cycles. We found variables related to the placement of wetlands along axes of the Wetland Continuum, e.g., hydrogeologic setting (relative wetland elevation) and hydroclimatic setting (proportion of wetland ponded), to be influential drivers of within-wetland habitat characteristics, such as the proportion of open-water area, which in turn was the strongest predictor of macroinvertebrate community composition. In contrast, predatory invertebrate and salamander abundance and non-predatory invertebrate biomass (i.e., predation and competition) were found to have minimal influence on community composition.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.107678","usgsCitation":"McLean, K., Mushet, D.M., Newton, W.E., and Sweetman, J.N., 2021, Long-term multidecadal data from a prairie-pothole wetland complex reveal controls on aquatic-macroinvertebrate communities: Ecological Indicators, v. 126, 107678, 11 p., https://doi.org/10.1016/j.ecolind.2021.107678.","productDescription":"107678, 11 p.","ipdsId":"IP-094142","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":452658,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.107678","text":"Publisher Index Page"},{"id":396116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Cottonwood Lake Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.70600509643555,\n 47.85014598272475\n ],\n [\n -100.60781478881836,\n 47.85014598272475\n ],\n [\n -100.60781478881836,\n 47.9002325297653\n ],\n [\n -100.70600509643555,\n 47.9002325297653\n ],\n [\n -100.70600509643555,\n 47.85014598272475\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248538,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Newton, Wesley E. 0000-0002-1377-043X wnewton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-043X","contributorId":3661,"corporation":false,"usgs":true,"family":"Newton","given":"Wesley","email":"wnewton@usgs.gov","middleInitial":"E.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sweetman, Jon N.","contributorId":279537,"corporation":false,"usgs":false,"family":"Sweetman","given":"Jon","email":"","middleInitial":"N.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":835109,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70220193,"text":"70220193 - 2021 - Changes in seabed mining","interactions":[],"lastModifiedDate":"2021-04-26T12:42:12.84965","indexId":"70220193","displayToPublicDate":"2021-04-16T07:37:09","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"18","title":"Changes in seabed mining","docAbstract":"Chapter 23 of the First World Ocean Assessment (WOA I) focused on marine mining, and particularly on established extractive industries, which are predominantly confined to near-shore areas, where shallow-water, near-shore aggregate and placer deposits, and somewhat deeper water phosphate deposits are found (United Nations, 2017a). At the time of publication, there were no commercially developed deep-water seabed mining (DSM) deposits but an assessment of mining leases and exploration activity was included. Since WOA I, the number of deep-water (depths greater than 200 m below the ocean surface) seabed exploration licenses has increased both within national jurisdictions of coastal, island and archipelagic States, and beyond in the Area (the seabed, ocean floor and subsoil thereof beyond the limits of national jurisdiction) under the administration of the International Seabed Authority (ISA). For the first time, in 2017 deep-water seabed test-mining was carried out by Japan at a water depth of 1,600 m within its exclusive economic zone (EEZ) (METI, 2017). The update in the present Chapter will focus on the nascent deep-water seabed mining industry and mineral deposits. Hereafter, we use seabed for deep-water seabed. \n\nEnvironmental issues focused on impacts from dredging activities and a list of references for some mining operations were provided. However, WOA I could not provide an environmental baseline for DSM and considered that environmental, social and economic aspects were often not adequately understood with available data. Data on potential environmental impacts are still scarce and can differ greatly between mineral extraction from near-shore and seabed mining sites. Information on economic benefits, and to some extent social impacts, of mining is becoming progressively more accessible due to several initiatives promoting an increase in transparency of extractive industries. \n\nIn 2015, the 2030 Agenda for Sustainable Development was adopted by all United Nations Member States. It includes 17 Sustainable Development Goals (SDGs) to be addressed on the basis of a global partnership. DSM activities may have implications for the achievement of SDGs 1, 5, 7–10, 12–14, and 17.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"United Nations World Ocean Assessment II","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"United Nations","usgsCitation":"Hein, J.R., Madureira, P., Bebianno, M.J., Colaço, A., Pinheiro, L.M., Roth, R., Singh, P.K., Strati, A., and Tuhumwire, J.T., 2021, Changes in seabed mining, chap. 18 of United Nations World Ocean Assessment II, p. 257-280.","productDescription":"24 p.","startPage":"257","endPage":"280","ipdsId":"IP-120664","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":385302,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385298,"type":{"id":15,"text":"Index Page"},"url":"https://www.un.org/regularprocess/woa2launch"}],"edition":"II","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hein, James R. 0000-0002-5321-899X jhein@usgs.gov","orcid":"https://orcid.org/0000-0002-5321-899X","contributorId":140835,"corporation":false,"usgs":true,"family":"Hein","given":"James","email":"jhein@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":814690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Madureira, Pedro","contributorId":257595,"corporation":false,"usgs":false,"family":"Madureira","given":"Pedro","email":"","affiliations":[{"id":52062,"text":"Estrutura de Missão para a Extensão da Plataforma Continental","active":true,"usgs":false}],"preferred":false,"id":814691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bebianno, Maria Joao","contributorId":257599,"corporation":false,"usgs":false,"family":"Bebianno","given":"Maria","email":"","middleInitial":"Joao","affiliations":[],"preferred":false,"id":814701,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Colaço, Ana","contributorId":257596,"corporation":false,"usgs":false,"family":"Colaço","given":"Ana","affiliations":[{"id":52063,"text":"IMAR-Institute of Marine Research, Okeanos - Univ. dos Açores","active":true,"usgs":false}],"preferred":false,"id":814692,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pinheiro, Luis M.","contributorId":201962,"corporation":false,"usgs":false,"family":"Pinheiro","given":"Luis","email":"","middleInitial":"M.","affiliations":[{"id":36309,"text":"University of Aveiro, Portugal","active":true,"usgs":false}],"preferred":false,"id":814693,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roth, Richard","contributorId":257597,"corporation":false,"usgs":false,"family":"Roth","given":"Richard","affiliations":[{"id":52064,"text":"Materials Systems Lab, MIT","active":true,"usgs":false}],"preferred":false,"id":814694,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Singh, Pradeep K.","contributorId":215599,"corporation":false,"usgs":false,"family":"Singh","given":"Pradeep","email":"","middleInitial":"K.","affiliations":[{"id":39293,"text":"Rock Excavation Engineering, CSIR-Central Institute of Mining and Fuel Research, Barwa road campus, Dhanbad, India","active":true,"usgs":false}],"preferred":false,"id":814695,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Strati, Anastasia","contributorId":257600,"corporation":false,"usgs":false,"family":"Strati","given":"Anastasia","email":"","affiliations":[],"preferred":false,"id":814702,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tuhumwire, Joshua T.","contributorId":257601,"corporation":false,"usgs":false,"family":"Tuhumwire","given":"Joshua","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":814703,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70223175,"text":"70223175 - 2021 - Implications of tagging effects for interpreting the performance of sea lamprey traps in a large river","interactions":[],"lastModifiedDate":"2021-08-17T13:29:20.381265","indexId":"70223175","displayToPublicDate":"2021-04-15T08:20:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Implications of tagging effects for interpreting the performance of sea lamprey traps in a large river","docAbstract":"Abundance estimates can be crucial for managing species of economic concern. The accuracy of these estimates can depend on the methods used to track animals and to estimate abundance from tracking data. We tested experimentally if disparate estimates of trapping efficiency calculated for sea lamprey (Petromyzon marinus) in the St. Marys River near Sault Ste. Marie, Canada could be explained by effects related to the invasiveness and handling involved in tagging or the tag size used in the marking procedures. Trapping is used to gauge adult abundance, trapping efficiency, and success of a binational sea lamprey control program in the Laurentian Great Lakes, North America. Our experiment compared nightly catches of sea lamprey marked with external fin clips, surgically-implanted passive integrated transponder tags (PIT-only), and surgically-implanted PIT and acoustic tags (PIT+acoustic). We found no evidence that the probability of being trapped was affected by the added invasiveness and handling of internal tagging. Nightly recaptures of PIT-only tagged sea lamprey, relative to fin-clipped sea lamprey, were not different from expectations based on the numbers of individuals released from each treatment group. Conversely, there was evidence of effects related to tag size. Nightly recaptures of PIT+acoustic tagged sea lamprey, relative to PIT-only tagged sea lamprey, were lower than expected based on numbers of individuals released from each treatment group. Effects related to tag size partially explain the disparate estimates in trapping efficiency observed for sea lamprey.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.03.008","usgsCitation":"Nelson, J., Rous, A.M., McLean, A.R., Barber, J., Bravener, G.A., Holbrook, C., and McLaughlin, R.L., 2021, Implications of tagging effects for interpreting the performance of sea lamprey traps in a large river: Journal of Great Lakes Research, v. 47, no. 4, p. 1200-1208, https://doi.org/10.1016/j.jglr.2021.03.008.","productDescription":"9 p.","startPage":"1200","endPage":"1208","ipdsId":"IP-127006","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":387992,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"Ontario","otherGeospatial":"Clergue Generating Station, St Marys River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.34995889663696,\n 46.51220131819224\n ],\n [\n -84.34195518493652,\n 46.51220131819224\n ],\n [\n -84.34195518493652,\n 46.51570102523837\n ],\n [\n -84.34995889663696,\n 46.51570102523837\n ],\n [\n -84.34995889663696,\n 46.51220131819224\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nelson, Jessica","contributorId":264242,"corporation":false,"usgs":false,"family":"Nelson","given":"Jessica","email":"","affiliations":[{"id":54408,"text":"Department of Integrative Biology, University of Guelph","active":true,"usgs":false}],"preferred":false,"id":821239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rous, Andrew M.","contributorId":203583,"corporation":false,"usgs":false,"family":"Rous","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":36663,"text":"Department of Integrative Biology, University of Guelph, Guelph, ON N1G 2W1, Canada","active":true,"usgs":false}],"preferred":false,"id":821240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McLean, Adrienne R.","contributorId":203584,"corporation":false,"usgs":false,"family":"McLean","given":"Adrienne","email":"","middleInitial":"R.","affiliations":[{"id":36664,"text":". Department of Integrative Biology, University of Guelph, Guelph, ON N1G 2W1, Canada","active":true,"usgs":false}],"preferred":false,"id":821241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barber, Jessica","contributorId":173133,"corporation":false,"usgs":false,"family":"Barber","given":"Jessica","affiliations":[{"id":6584,"text":"United States Fish and Wildlife Service–Bozeman Fish Technology","active":true,"usgs":false}],"preferred":false,"id":821242,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bravener, Gale A","contributorId":174546,"corporation":false,"usgs":false,"family":"Bravener","given":"Gale","email":"","middleInitial":"A","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":821243,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":821244,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McLaughlin, Robert L.","contributorId":143707,"corporation":false,"usgs":false,"family":"McLaughlin","given":"Robert","email":"","middleInitial":"L.","affiliations":[{"id":12660,"text":"University of Guelph","active":true,"usgs":false}],"preferred":false,"id":821245,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70221215,"text":"70221215 - 2021 - Investigating vegetation responses to underground nuclear explosions through integrated analyses","interactions":[],"lastModifiedDate":"2021-06-07T13:08:15.404703","indexId":"70221215","displayToPublicDate":"2021-04-15T08:04:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7359,"text":"Journal of Geophysical Research Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Investigating vegetation responses to underground nuclear explosions through integrated analyses","docAbstract":"Vegetation has the potential to respond to underground nuclear explosions, yet these links have not been fully explored. Given the lack of previously described signatures, the changes in vegetation are possibly subtle. The integration of multiple different data streams is potentially a useful approach to improve signal detection. Here, we investigate whether semi-arid vegetation growth patterns responded to eight legacy underground nuclear tests at the Nevada National Security Site in southern Nevada, USA. We tested for spatial and temporal changes in vegetation cover, tree growth patterns, and tree leaf spectral properties using ground-based measurements, including those from tree-rings and hyperspectral surface vegetation reflectance, as well as space-based measurements of Normalized Difference Vegetation Index (NDVI) from Landsat. Multiple data streams suggest a localized (<1.2 km) spatial pattern whereby tree growth is enhanced closer to the source of the underground test relative to sites further away. We also observed a more regional (>1.2–9 km) pattern whereby tree growth is suppressed coincident with a drought beginning 1 year before the 1989 tests, but continuing in the 5 years following the tests, which is anomalous relative to what is expected based on the response of tree growth to previous droughts. Quantification of the relative effects of the tests on vegetation remains a challenge due to the coincident drought and the potential for other disturbances to have impacted tree growth at this time, but the integration of these data reveals a more nuanced growth response than any other one data set indicates alone.
Wetland methane (CH4) emissions (FCH4) are important in global carbon budgets and climate change assessments. Currently, FCH4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent FCH4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that FCH4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between FCH4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between FCH4 and temperature, suggesting larger FCH4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.
","language":"English","publisher":"Springer","doi":"10.1038/s41467-021-22452-1","usgsCitation":"Chang, K., Riley, W.J., Knox, S.H., Jackson, R.B., McNicol, G., Poulter, B., Aurela, M., Baldocchi, D., Bansal, S., Bohrer, G., Campbell, D.I., Cescatti, A., Chu, H., Delwiche, K.B., Desai, A.R., Euskirchen, E.S., Goeckede, M., Friborg, T., Hemes, K.S., Hirano, T., Iwata, H., Helbig, M., Keenan, T.F., Kang, M., Krauss, K., Lohila, A., Mitra, B., Mammarella, I., Miyata, A., Nilsson, M.B., Oechel, W.C., Noormets, A., Peichl, M., Reba, M.L., Rinne, J., Papale, D., Runkle, B.R., Ryu, Y., Sachs, T., Schafer, K.V., Schmid, H.P., Shurpali, N., Sonnentag, O., Tang, A., Torn, M.S., Tuittila, E., Trotta, C., Ueyama, M., Vargas, R., Vesala, T., Windham-Myers, L., Zhang, Z., and Zona, D., 2021, Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions: Nature Communications, v. 12, 2266, 10 p., https://doi.org/10.1038/s41467-021-22452-1.","productDescription":"2266, 10 p.","ipdsId":"IP-115813","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":452679,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-021-22452-1","text":"Publisher Index Page"},{"id":386648,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2021-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Chang, Kuang-Yu 0000-0002-7859-5871","orcid":"https://orcid.org/0000-0002-7859-5871","contributorId":260439,"corporation":false,"usgs":false,"family":"Chang","given":"Kuang-Yu","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":817946,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Riley, William J. 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We built the 3D map by integrating the results from detailed geologic mapping, seismic-reflection, potential-field-geophysical, and lithologic well-logging investigations completed in the study area. This effort was undertaken to investigate the geologic structure in the geothermal field and geologic controls on hydrothermal circulation. This characterization of the controls on hydrothermal circulation is applicable to the assessment, exploration, and development of analogous geothermal resources. The 3D map area is 4 kilometers (km) wide along the west-northwest-to-east-southeast axis and 6 km wide along the north-northeast-to-south-southwest axis and extends to 1.0 km below sea level, approximately 2.5 km below the land surface. We describe the geologic units and structures in the map area, discuss the methods used to integrate the geologic and geophysical information into the 3D geologic interpretation, and calculate several geologic factors that may aid in our understanding of hydrothermal circulation. Map sheet 1 provides horizontal and vertical section views and oblique perspective views from several angles of the 3D geologic map. Map sheet 2 provides views of derivative calculations based on the 3D geologic data, 3D density of faults, 3D density of fault intersections and terminations, slip tendency on 3D faults, and dilation tendency on 3D faults. We provide digital data for all elements of the map, such as individual 3D fault and stratigraphic surfaces, 3D fault density, 3D fault intersection density, 3D slip tendency on fault surfaces, and 3D dilation tendency on fault surfaces. A brief movie displaying the 3D map is available at https://doi.org/10.3133/sim3469.
Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
The potential for widespread landslides is generally increased when extraordinary wet periods occur during times of elevated subsurface hydrologic conditions. A series of storms in early 2018 in Pittsburgh, Pennsylvania, overlapped with a period of increased shallow soil moisture and rising bedrock groundwater levels resulting from seasonally diminished evapotranspiration and induced widespread landslides in the region. Most of the landslides were shallow slope failures in colluvium, landslide deposits, and/or fill. However, deep-seated landslide activity also occurred and corresponded with record cumulative precipitation from late February to April and bedrock groundwater levels rising to an annual high. Landslides blocked or damaged roads, adversely affected multiple houses, disrupted electrical service, crushed vehicles, and resulted in considerable economic losses. The initial landslides occurred during or immediately after a rare period of three successive days of heavy rain that began on February 14. Subsequent landslides between late February and April were induced by multiday storms with smaller rainfall totals. As shallow soil moisture at a monitoring site rose above a volumetric water content of 32%, the mean rainfall intensities necessary to induce slope failure in colluvium and other surficial deposits decreased. Deep-seated landslide movement occurred in the region mostly when the groundwater level in a bedrock observation well was shallower than 1.7 m. The availability of hydrologic and landslide movement monitoring data during this extraordinary series of storms highlighted the evolution of the landslide hazard with changing moisture conditions and yielded insights into potential hydrologic criteria for anticipating future widespread landslides in the region.
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