Extreme rainfall events in the humid-tropical Luquillo Mountains, Puerto Rico export the bulk of suspended sediment and particulate organic carbon. Using 25 years of river carbon and suspended sediment data, which targeted hurricanes and other large rainstorms, we estimated biogenic particulate organic carbon yields of 65 ± 16 tC km−2 yr−1 for the Icacos and 17.7 ± 5.1 tC km−2 yr−1 for the Mameyes rivers. These granitic and volcaniclastic catchments function as substantial atmospheric carbon-dioxide sinks, largely through export of river biogenic particulate organic carbon during extreme rainstorms. Compared to other regions, these high biogenic particulate organic carbon yields are accompanied by lower suspended sediment yields. Accordingly, particulate organic carbon export from these catchments is underpredicted by previous yield relationships, which are derived mainly from catchments with easily erodible sedimentary rocks. Therefore, rivers that drain petrogenic-carbon-poor bedrock require separate accounting to estimate their contributions to the geological carbon cycle.
Genetic analysis of non-invasively obtained samples is an increasingly affordable option for many wildlife studies, but it has remained difficult to obtain high-quality samples from many species. We modified 8” Belisle foot snares (Belisle Enterprises, Quebec, Canada) to non-invasively obtain mountain lion (Puma concolor) hair samples in unbaited trail sets. We deployed 22 hair traps, monitored by remote cameras, at 66 locations for 1618 active trap nights (x̄= 24.5 nights, SD = 7.2 nights). Photos indicated 20 instances of mountain lions passing within 2 m of a hair trap and we collected 7 mountain lion hair samples, which averaged >20 hairs/sample. All samples contained hair with visible roots and were identifiable to species; 6 of the 7 (85.7%) yielded sufficient DNA for individual identification. We attributed failure to obtain samples to 3 primary causes: individual trap saturation (2 instances), trap failure (2 instances), and non-trigger events (9 instances). Black bears (Ursus americanus) and heavy rains were the primary sources of disturbance to hair trap sets, contributing to individual trap saturation and trap failure. We speculate that low trigger rates were associated with pan tension having been set too high in the first month of the study, as well as disturbance of hair traps or leading foot placements by nontarget species. We discuss strategies to increase hair sample collection rates, including seasonal use of hair traps, more selective placement on the landscape, and altering physical attributes of the hair traps. Taking these strategies and the quality of hair samples collected into account, we believe hair traps are a viable tool for noninvasively collecting genetic material for individual identification of mountain lions and other elusive species. These data can be applied to studies of habitat connectivity, breeding success and relatedness, population density, metapopulation structure, or any others in which a bank of individual genotypes are useful.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1257","usgsCitation":"Rossettie, T., Perry, T., and Cain, J.W., 2022, Noninvasive sampling of mountain lion hair using modified foothold traps: Wildlife Society Bulletin, v. 46, no. 1, e1257, 13 p., https://doi.org/10.1002/wsb.1257.","productDescription":"e1257, 13 p.","ipdsId":"IP-119182","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486524,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","county":"Sierra County","otherGeospatial":"Black Range Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.48798824240478,\n 33.36187929179046\n ],\n [\n -108.48798824240478,\n 32.91656124812863\n ],\n [\n -107.58956320668692,\n 32.91656124812863\n ],\n [\n -107.58956320668692,\n 33.36187929179046\n ],\n [\n -108.48798824240478,\n 33.36187929179046\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"46","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Rossettie, Tricia S.","contributorId":355783,"corporation":false,"usgs":false,"family":"Rossettie","given":"Tricia S.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":938152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Travis W.","contributorId":355784,"corporation":false,"usgs":false,"family":"Perry","given":"Travis W.","affiliations":[{"id":84836,"text":"fu","active":true,"usgs":false}],"preferred":false,"id":938153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938151,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230602,"text":"ofr20211030K - 2022 - System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","interactions":[{"subject":{"id":70230602,"text":"ofr20211030K - 2022 - System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","indexId":"ofr20211030K","publicationYear":"2022","noYear":false,"chapter":"K","displayTitle":"System Characterization Report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","title":"System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2022-04-19T10:54:07.62676","indexId":"ofr20211030K","displayToPublicDate":"2022-04-18T15:29:12","publicationYear":"2022","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":"2021-1030","chapter":"K","displayTitle":"System Characterization Report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","title":"System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)","docAbstract":"This report addresses system characterization of the Italian Space Agency’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (band to band and image to image), radiometric, and spatial performances. Results of these analyses indicate that PRISMA has a band-to-band geometric performance in the range of −0.046 to 0.040 pixel; an image-to-image geometric performance (relative to the Landsat 8 Operational Land Imager) in the range of −60.791 meters (m; −2.03 pixels) to 299.541 m (9.98 pixels); a radiometric performance in the range of −0.037 to −0.001 in offset and 1.026 to 1.274 in slope; and a spatial performance with a relative edge response in the range of 0.56 to 0.63, full width at half maximum in the range of 1.84 to 1.97 pixels, and a modulation transfer function at a Nyquist frequency in the range of 0.054 to 0.096. Regarding fairly large geometric accuracy, the following explanation is provided to help the reader. The geometric accuracy required for PRISMA is a 200-m circular error at 90 percent (CE90) without ground control points (GCPs), a 15-m CE90 using GCPs is documented in the PRISMA mission overview (Agenzia Spaziale Italiana, 2021). The PRISMA images used for the current system characterization were georeferenced without using any GCPs; thus, the 200-m geometric accuracy requirement is applied. Beginning in 2022, a worldwide GCP database will be used in the PRISMA product processing chain, which will improve georeferencing accuracy to meet the 15-m CE90 requirement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030K","usgsCitation":"Kim, M., Park, S., Anderson, C., and Stensaas, G.L., 2022, System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA), chap. K of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 28 p., https://doi.org/10.3133/ofr20211030K.","productDescription":"iv, 28 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-129829","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":398958,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/k/coverthb.jpg"},{"id":398959,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/k/ofr20211030k.pdf","text":"Report","size":"14.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-K"},{"id":398960,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/k/ofr20211030k.XML"},{"id":398961,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/k/images"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Willamette River is home to at least 69 species of fish, 33 of which are native, including Chinook salmon (Oncorhynchus tshawytscha) and steelhead (Oncorhynchus mykiss). These fish need suitable hydraulic conditions, such as water depth and velocity, to fulfill various stages of their life. Hydraulic conditions are driven by interactions between channel morphology and streamflow, which throughout the Willamette River are strongly influenced by the operation of flood-control dams in upstream tributaries. To assess how streamflow management at these dams affects downstream fish habitat, the U.S. Geological Survey has developed high-resolution bathymetric datasets to support the development of two-dimensional hydraulic models. The datasets were created by combining data collected by airborne topo-bathymetric Light Detection and Ranging with boat-based sonar to create a seamless modeling surface over which a computational mesh with a resolution of roughly 5 by 5 meters was overlaid using the U.S. Army Corps of Engineers Hydraulic Engineering Center’s River Analysis System 5.0.7 hydraulic modeling software. Models were developed for about 200 river kilometers, separated into five modeling reaches, and hydraulic conditions were simulated at flows ranging from extremely low values to annual peak flows. Results of the simulations highlight distinct patterns of inundation extents, water depths, and velocities that vary longitudinally along the Willamette River. In the two farthest upstream model reaches, from Eugene to Corvallis, the river is slower, shallower, and inundates more area at similar seasonal flows than in reaches downstream from Corvallis, where the river generally is deeper and faster. These findings align with previous geomorphic analysis of the Willamette River showing the upper reaches of the river to be geomorphically more dynamic compared to the largely single-thread channel farther downstream. Results of simulations made with these hydraulic models can be used to drive fish-habitat models to further inform flow-management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225025","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"White, J.S., and Wallick, J.R., 2022, Development of continuous bathymetry and two-dimensional hydraulic models for the Willamette River, Oregon: U.S. Geological Survey Scientific Investigations Report 2022–5025, 67 p., https://doi.org/10.3133/sir20225025.","productDescription":"viii, 67 p.","onlineOnly":"Y","ipdsId":"IP-112990","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":435872,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NB0KUT","text":"USGS data release","linkHelpText":"Two-dimensional HEC-RAS models and topo-bathymetric datasets for the Willamette River, Oregon"},{"id":435871,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92TTY4R","text":"USGS data release","linkHelpText":"Single-beam Echosounder Bathymetry of the Willamette River, Oregon 2015-2018"},{"id":398793,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5025/coverthb.jpg"},{"id":502381,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112938.htm","linkFileType":{"id":5,"text":"html"}},{"id":398796,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5025/sir20225025.XML"},{"id":398795,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5025/images"},{"id":398794,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5025/sir20225025.pdf","text":"Report","size":"20.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5025"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.59619140625001,\n 43.94537239244209\n ],\n [\n -121.904296875,\n 43.94537239244209\n ],\n [\n -121.904296875,\n 45.521743896993634\n ],\n [\n -123.59619140625001,\n 45.521743896993634\n ],\n [\n -123.59619140625001,\n 43.94537239244209\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The conceptual models of groundwater flow outlined herein synthesize what is known and hypothesized about the groundwater-flow systems that discharge to the Grand Canyon of Arizona. These models interpret the hydrogeologic characteristics and hydrologic dynamics of the physical systems into a framework for understanding key aspects of the physical systems as they relate to groundwater flow and contaminant transport. This report describes five individual groundwater-flow systems draining to the Grand Canyon: Kaibab, Uinkaret-Kanab, Marble-Shinumo, Cataract, and Blue Spring. These systems are present in the saturated parts of the lower Paleozoic carbonate section exposed on the walls of the Grand Canyon; specifically, the Mississippian Redwall Limestone down through the Cambrian Muav Limestone of Tonto Group. Together, the systems described in this report compose the regional groundwater-flow system. Local to subregional flow systems in the sedimentary units of the overlying Permian section could provide transport pathways from the land surface to the regional flow system. Despite the potential importance of the local systems, the focus of this report is on the systems present in the lower Paleozoic section because all major springs in the Grand Canyon discharge from those units.
The most important hydrogeologic characteristics include system boundaries imposed by major tectonic structures, and the degree to which karstification influences the magnitude and direction of flow in each system. Important hydrologic dynamics include locations and rates of potential groundwater recharge, vertical pathways to the regional aquifer, and the locations, magnitude, geochemical signature, and hydrostratigraphic setting of groundwater discharge from springs. Unknown properties or conditions that represent the greatest uncertainties in our current understanding of the regional groundwater-flow system are identified for additional consideration.
Groundwater data are sparse owing to geographic remoteness and extreme depth to water throughout much of the study area. This paucity of information was diminished with the development of a structural contour map of the top and bottom surfaces of the regional aquifer, and a Soil-Water-Balance model that produces spatial distributions of rates of potential recharge. Investigation of the five groundwater-flow systems reveals important, though mostly qualitative, characteristics controlling the rates and directions of groundwater flow. Karstification has produced dissolution-enhanced conduit flow pathways to various degrees in each of the systems. Parts of each system exhibit relative structural uplift or downdropping of the hydrostratigraphic units of the regional aquifer, with some uplifted sections dipping inward toward the Grand Canyon and others dipping outward. The Kaibab groundwater system is archetypical of an uplifted, inward-dipping karst system, whereas the Blue Spring groundwater system and most of the Cataract groundwater system are representative instances of a downdropped or basin karst system. The Uinkaret-Kanab groundwater-flow system is structurally similar to the basin karst systems but karstification has not progressed to nearly the same degree. The Marble-Shinumo groundwater system does not fall cleanly into either category and its boundaries are the most uncertain of all the groundwater systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225037","usgsCitation":"Knight, J.E., and Huntoon, P.W., 2022, Conceptual models of groundwater flow in the Grand Canyon region, Arizona: U.S. Geological Survey Scientific Investigation Report 2022–5037, 51 p., https://doi.org/10.3133/sir20225037.","productDescription":"Report: vi, 51 p.; Data Release","numberOfPages":"51","onlineOnly":"Y","ipdsId":"IP-097904","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":502392,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112937.htm","linkFileType":{"id":5,"text":"html"}},{"id":398737,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FQ7BSY","text":"Soil-Water-Balance (SWB) model archive used to simulate potential mean annual recharge in the Grand Canyon region, Arizona","description":"Knight, J.E., and Jones, C.J., 2022, Soil-Water-Balance (SWB) model archive used to simulate potential mean annual recharge in the Grand Canyon region, Arizona: U.S. Geological Survey data release, https://doi.org/10.5066/P9FQ7BSY."},{"id":398739,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5037/sir20225037.pdf","text":"Report","size":"24 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":398738,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5037/covrthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.64257812499999,\n 34.79576153473033\n ],\n [\n -110.58837890625,\n 34.79576153473033\n ],\n [\n -110.58837890625,\n 36.96744946416934\n ],\n [\n -113.64257812499999,\n 36.96744946416934\n ],\n [\n -113.64257812499999,\n 34.79576153473033\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Grouse and ptarmigan (Galliformes) harbor fairly diverse helminth faunas that can impact the host's health, including filarial nematodes in the genus Splendidofilaria. As host and parasite distributions are predicted to shift in response to recent climate change, novel parasites may be introduced into a region and impose additional stressors on bird populations. Limited information is available on the prevalence of filariasis in Alaska galliforms. To date, no molecular surveys have been completed. Past studies relied on examining blood smears or total body necropsies, which are time-consuming and may not detect filarial parasites with low prevalence in hosts. Therefore, we developed a TaqMan probe-based real-time PCR assay targeting the cytochrome c oxidase 1 gene (COI) of Splendidofilaria to decrease processing times and increase sensitivity as well as provide baseline data on the diversity of filariid infections in galliform species in Alaska. We screened a combined total of 708 galliform samples (678 unique individual birds) from different tissues (blood, muscle, and lung) for the presence of filarial DNA across the state of Alaska. Real-time PCR screening revealed an overall prevalence of filarial infection of 9.5% across species: Bonasa umbellus (0%, n = 23), Dendragapus fuliginosus (0%, n = 8), Falcipennis canadensis (26.8%, n = 198), Lagopus lagopus (2.6%, n = 274), Lagopus leucura (0%, n = 23), Lagopus muta (3%, n = 166), and Tympanuchus phasianellus (12.5%, n = 16). We observed microfilarial infections throughout most of Alaska except in Arctic regions and the Aleutian Islands where viable vectors may not be present.
Harvest regulations are important tools that fisheries professionals use to impact fish abundance, alter population size structure, and improve fishing opportunities. Fisheries professionals often assume that specialized harvest regulations will have specific effects on target fish populations, but these predictions are not always realized because theory and practice do not always match (literature indicates that predictions are not met in about half of the cases). To identify trends that can improve the future success of harvest regulations, we reviewed a representative sample of harvest regulation evaluations for inland sport fish (i.e., 62 evaluations from 41 studies). Our review revealed gaps related to quantitative predictions, evaluation duration, statistical design, researcher–manager collaboration, and data standardization. Fisheries professionals can benefit from shared and thoughtful data collection designs and protocol standardizations. These designs can transform assessment sampling into empirical regulation evaluations that provide generality across locations and time periods with similar effort and cost.
The U.S. Geological Survey worked in cooperation with the Concerned Citizens of Montauk and the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering Lake Montauk, an artificial embayment on the tip of the southern fork of Suffolk County, Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the harbor and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, and surrounding land use where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment.
Overall, human and waterfowl markers were infrequently and sporadically present in source and receptor samples at low concentrations. By evaluating the microbial source tracking markers alongside fecal coliform data and land-use information, geographical sources of fecal contamination discharging from various source sites, such as culverts and ponds, were better differentiated. Analysis revealed that stormwater runoff and pond drainage were the most likely transport mechanisms for fecal contamination to Lake Montauk. When considering Lake Montauk as a whole, the highest frequency of fecal coliform detections in source site samples was found to be under wet summer conditions, as evidenced by the high fecal coliform concentrations at the South Beach, Stepping Stones Pond, and Stepping Stones Pond Culvert sites (300, 220, and more than 16,000 most probable number per 100 milliliters, respectively). No point sources of fecal coliform contamination to Lake Montauk were identified; however, receptor site samples adjacent to marinas (Lake Montauk Inlet and Star Island North sites) had a high frequency of human marker detections but were associated with fecal coliform concentrations at or below the reporting limit. The absence of fecal coliform and human microbial source tracking markers in groundwater samples indicated that water from septic systems did not influence the lake during this study. Further, the sandy sediment sample collected at the South Beach site was negative for all microbial source tracking markers and is unlikely to contribute fecal coliform from the tested host organisms when resuspended in the water column through tidal shifts or boat activity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225038","collaboration":"Prepared in cooperation with Concerned Citizens of Montauk and New York State Department of Environmental Conservation","usgsCitation":"Tagliaferri, T.N., Fisher, S.C., Kephart, C.M., Cheung, N., Reed, A.P., and Welk, R.J., 2022, Using microbial source tracking to identify fecal contamination sources in Lake Montauk on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2022–5038, 16 p., https://doi.org/10.3133/sir20225038.","productDescription":"Report: vi, 16 p.; Database","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-129971","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":502393,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112933.htm","linkFileType":{"id":5,"text":"html"}},{"id":398849,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225038/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5038"},{"id":398823,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.er.usgs.gov/publication/sir20215033","text":"Scientific Investigations Report 2021–5033","linkHelpText":"- Overview and Methodology for a Study To Identify Fecal Contamination Sources Using Microbial Source Tracking in Seven Embayments on Long Island, New York"},{"id":398822,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":398821,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5038/images/"},{"id":398820,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5038/sir20225038.XML"},{"id":398819,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5038/sir20225038.pdf","text":"Report","size":"1.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5038"},{"id":398818,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5038/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Lake Montauk","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.95701599121094,\n 41.03715373847282\n ],\n [\n -71.87461853027344,\n 41.03715373847282\n ],\n [\n -71.87461853027344,\n 41.084009326420926\n ],\n [\n -71.95701599121094,\n 41.084009326420926\n ],\n [\n -71.95701599121094,\n 41.03715373847282\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180-8349
The global decline of songbird populations is a well-recognized conservation issue. Domestic cats kill an estimated 2.4 billion birds each year in the United States alone—more than most other anthropogenic threats combined. As many pet owners are reluctant to keep their cats inside, collar-mounted antipredation devices for domestic cats may be an important conservation tool. We examined the effectiveness of the Birdsbesafe® collar cover (BCC), a sleeve of brightly patterned fabric worn over a typical breakaway collar. The BBC's designers intend for the collar's bright colors to alert potential prey to the cat's presence. By combining data from two studies in New York (2014 and 2019) and one in Florida (2019), all of which used similar methods, we tested the hypothesis that the BCC effectively reduces avian mortality caused by cats of different ages and sexes in different hunting environments. We tested 94 cats over a 12-wk period in New York in 2014 or 8-wk periods in Florida and New York in 2019 during the bird breeding seasons; cats alternated 2-wk periods with and without the collar. Across studies, we recovered 2.7 times fewer birds per cat with the BCC than without (P = 0.006). The BCC was more effective at a temperate latitude than a subtropical one (P = 0.047). There was no difference in the effectiveness of the BCC for cats of varying ages, sexes, or hunting environments. Our results suggest that the BCC could be one tool within a larger effort to decrease domestic cat predation of songbirds.
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University","active":true,"usgs":false}],"preferred":false,"id":908759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willson, S.K.","contributorId":341669,"corporation":false,"usgs":false,"family":"Willson","given":"S.K.","email":"","affiliations":[{"id":39266,"text":"St. Lawrence University","active":true,"usgs":false}],"preferred":false,"id":908760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powell, Abby 0000-0002-9783-134X abby_powell@usgs.gov","orcid":"https://orcid.org/0000-0002-9783-134X","contributorId":176843,"corporation":false,"usgs":true,"family":"Powell","given":"Abby","email":"abby_powell@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":908761,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230678,"text":"70230678 - 2022 - The role of satellite telemetry data in 21st century conservation of polar bears (Ursus maritimus)","interactions":[],"lastModifiedDate":"2022-04-21T13:55:39.932309","indexId":"70230678","displayToPublicDate":"2022-04-14T08:46:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The role of satellite telemetry data in 21st century conservation of polar bears (Ursus maritimus)","title":"The role of satellite telemetry data in 21st century conservation of polar bears (Ursus maritimus)","docAbstract":"Satellite telemetry (ST) has played a critical role in the management and conservation of polar bears (Ursus maritimus) over the last 50 years. ST data provide biological information relevant to subpopulation delineation, movements, habitat use, maternal denning, health, human-bear interactions, and accurate estimates of vital rates and abundance. Given that polar bears are distributed at low densities over vast and remote habitats, much of the information provided by ST data cannot be collected by other means. Obtaining ST data for polar bears requires chemical immobilization and application of a tracking device. Although immobilization has not been found to have negative effects beyond a several-day reduction in activity, over the last few decades opposition to immobilization and deployment of satellite-linked radio collars has resulted in a lack of current ST data in many of the 19 recognized polar bear subpopulations. Here, we review the uses of ST data for polar bears and evaluate its role in addressing 21st century conservation and management challenges, which include estimation of sustainable harvest rates, understanding the impacts of climate warming, delineating critical habitat, and assessing potential anthropogenic impacts from tourism, resource development and extraction. We found that in subpopulations where ST data have been consistently collected, information was available to estimate vital rates and subpopulation density, document the effects of sea-ice loss, and inform management related to subsistence harvest and regulatory requirements. In contrast, a lack of ST data in some subpopulations resulted in increased bias and uncertainty in ecological and demographic parameters, which has a range of negative consequences. As sea-ice loss due to climate warming continues, there is a greater need to monitor polar bear distribution, habitat use, abundance, and subpopulation connectivity. We conclude that continued collection of ST data will be critically important for polar bear management and conservation in the 21st century and that the benefits of immobilizing small numbers of individual polar bears in order to deploy ST devices significantly outweigh the risks.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2022.816666","usgsCitation":"Laidre, K.L., Durner, G.M., Lunn, N.J., Regehr, E.V., Atwood, T.C., Rode, K.D., Aars, J., Routti, H., Wiig, O., Dyck, M., Richardson, E.S., Atkinson, S., Belikov, S., and Stirling, I., 2022, The role of satellite telemetry data in 21st century conservation of polar bears (Ursus maritimus): Frontiers in Marine Science, v. 9, 816666, 22 p., https://doi.org/10.3389/fmars.2022.816666.","productDescription":"816666, 22 p.","ipdsId":"IP-135225","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":448110,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2022.816666","text":"Publisher Index Page"},{"id":399396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Greenland, Russia, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -179.9,\n 56.75272287205736\n ],\n [\n 179.9,\n 56.75272287205736\n ],\n [\n 179.9,\n 89\n ],\n [\n -179.9,\n 89\n ],\n [\n -179.9,\n 56.75272287205736\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2022-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Laidre, Kristin L.","contributorId":191798,"corporation":false,"usgs":false,"family":"Laidre","given":"Kristin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":841130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":841131,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lunn, Nicholas J","contributorId":198991,"corporation":false,"usgs":false,"family":"Lunn","given":"Nicholas","email":"","middleInitial":"J","affiliations":[],"preferred":false,"id":841132,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Regehr, Eric V. 0000-0003-4487-3105","orcid":"https://orcid.org/0000-0003-4487-3105","contributorId":66364,"corporation":false,"usgs":false,"family":"Regehr","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":12428,"text":"U. 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The density of C. shasta actinospores in the water column is typically highest in the spring (March–June), and directly influences infection rates for outmigrating juvenile salmonids. Current management approaches require quantities of C. shasta density to assess disease risk and estimate survival of juvenile salmonids. Therefore, we developed a model to simulate the density of waterborne C. shasta actinospores using a mechanistic framework based on abiotic drivers and informed by empirical data. The model quantified factors that describe the key features of parasite abundance during the period of juvenile salmon outmigration, including the week of initial detection (onset), seasonal pattern of spore density, and peak density of C. shasta. Spore onset was simulated by a bio-physical degree-day model using the timing of adult salmon spawning and accumulation of thermal units for parasite development. Normalized spore density was simulated by a quadratic regression model based on a parabolic thermal response with river water temperature. Peak spore density was simulated based on retained explanatory variables in a generalized linear model that included the prevalence of infection in hatchery-origin Chinook juveniles the previous year and the occurrence of flushing flows (≥171 m3/s). The final model performed well, closely matched the initial detections (onset) of spores, and explained inter-annual variations for most water years. Our C. shasta model has direct applications as a management tool to assess the impact of proposed flow regimes on the parasite, and it can be used for projecting the effects of alternative water management scenarios on disease-induced mortality of juvenile salmonids such as with an altered water temperature regime or with dam removal.
In arid ecosystems, available water is a critical, yet limited resource for human consumption, agricultural use, and ecosystem processes—highlighting the importance of developing management strategies to meet the needs of multiple users. Here, we evaluated how water availability influences stream thermal regimes and life-history expressions of Lahontan cutthroat trout (Oncorhynchus clarkii henshawi) in the arid Truckee River basin in the western United States. We integrated air temperature and stream discharge data to quantify how water availability drives stream temperature during annual spawning and rearing of Lahontan cutthroat trout. We then determined how in situ stream discharge and temperature affected adult spawning migrations, juvenile growth opportunities, and duration of suitable thermal conditions. Air temperatures had significant, large effects (+) on stream temperature across months; the effects of discharge varied across months, with significant effects (−) during May through August, suggesting increased discharge can help mitigate temperatures during seasonally warm months. Two models explained adult Lahontan cutthroat trout migration, and both models indicated that adult Lahontan cutthroat trout avoid migration when temperatures are warmer (~ > 12 °C) and discharge is higher (~ > 50 m3*s−1). Juvenile size was best explained by a quadratic relationship with cumulative degree days (CDD; days>4 °C) as size increased with increasing CDDs but decreased at higher CDDs. We also found an interaction between CDDs and discharge explaining juvenile size: when CDDs were low, higher discharge was associated with larger size, but when CDDs were high, higher discharge was associated with smaller size. Stream temperatures also determined the duration of juvenile rearing, as all juvenile emigration ceased at temperatures >24.4 °C. Together, our results illustrated how stream discharge and temperature shape the life-history of Lahontan cutthroat trout at multiple stages and can inform management actions to offset warming temperatures and facilitate life-history diversity and population resilience.
The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, performed a study to inform the development of the department’s guidelines for the collection and use of water-chemistry data to support calculation of site-dependent aluminum criteria values. The U.S. Geological Survey collected and analyzed discrete water-quality samples at four wastewater-treatment facilities and seven water-treatment facilities in eastern and central Massachusetts from April 2018 through May 2019.
For each of the 11 facilities considered, water-quality samples were collected from treatment-plant effluent and receiving-water bodies. Samples were collected for laboratory analysis of major ions (calcium and magnesium ions are used to calculate total hardness), dissolved organic carbon (DOC), total organic carbon (TOC), and total recoverable aluminum. Field parameters for pH, temperature, and specific conductance were measured in situ concurrently with sample collection.
Water-quality conditions differed among monitoring stations. The highest pH values were observed for stations on the Assabet River that receive effluent discharges from wastewater-treatment facilities (the Westborough, Marlborough, Hudson, and Maynard wastewater-treatment facilities). High DOC concentrations (greater than 10 mg/L) were measured in water bodies associated with large areas of riparian wetlands—Lily Pond (Cohasset) and Third Herring Brook (Hanover), and low DOC concentrations (less than 2.5 mg/L) were measured at three water bodies in central Massachusetts—Hocomonco Pond (Westborough), Wyman Pond (Fitchburg), and Monoosnoc Brook (Leominster). Wyman Pond (Fitchburg), Monoosnoc Brook (Leominster), and Lily Pond (Cohasset) also had low pH values and low total hardness concentrations.
The monthly discrete pH, DOC, and total hardness data for selected stations on receiving-water bodies were used in the U.S. Environmental Protection Agency Aluminum Criteria Calculator Version 2.0 to estimate site-dependent total recoverable aluminum concentrations that—if not exceeded—would be expected to protect fish, invertebrates, and other aquatic life from adverse effects associated with acute and chronic aluminum exposures. The U.S. Environmental Protection Agency Calculator output provides values for the acute criterion, defined as the criterion maximum concentration (CMC), an estimate of the highest aluminum concentration in surface water to which an aquatic community can be exposed briefly without resulting in an unacceptable effect. This output also provides values for the chronic criterion, defined as the criterion continuous concentration (CCC), an estimate of the highest concentration of aluminum in surface water to which an aquatic community can be exposed indefinitely without resulting in an unacceptable effect. To determine aluminum criteria values typically evaluated for use as protective water-quality criteria, the monthly instantaneous CMC and CCC values were used to calculate the minimum, 5th percentile, and 10th percentile CMC and CCC values for selected monitoring stations.
The monthly instantaneous aluminum CMC and CCC values generated using the EPA Calculator varied among stations. Aluminum CMC and CCC values were highest for four ambient (upstream) stations on the Assabet River associated with wastewater-treatment facilities (Westborough, Marlboro, Hudson, and Maynard). Aluminum CMC and CCC values were lower for stations associated with water-treatment facilities, and lowest for selected ambient stations on Lily Pond, Monoosnoc Brook, and Wyman Pond associated with water-treatment facilities in Cohasset, Leominster, and Fitchburg, respectively. For many stations, the highest CMC and CCC instantaneous aluminum criteria values generated using the U.S. Environmental Protection Agency Calculator were for months during the growing season for algae and aquatic macrophytes (April or May through September or October) and the lowest values were for months during the nongrowing season (October or November through March or April), indicating the importance of collecting water-quality data during the nongrowing season.
Aluminum CMC and CCC values generated by the U.S. Environmental Protection Agency Calculator are sensitive to variations in the input parameters (pH, DOC, and total hardness). Aluminum solubility is particularly affected by pH. To characterize diel and seasonal variations in pH, multiparameter water-quality monitors recording continuous (15-minute interval) water temperature and pH were installed in the receiving-water body for one station near each facility upstream from the effluent discharge (in rivers) or at a station outside the immediate effect of effluent discharge (in ponds). Continuous water temperature and pH data were collected from April or May 2018 through November or December 2018. Continuous pH data indicated that the pond stations and Assabet River stations had large diel variations in pH during the growing season. Continuous pH data were used together with discrete DOC and total hardness data to evaluate the potential effect of diel variations in pH on calculated site-dependent aluminum criteria values. For the 11 stations, diel variations in pH were determined to correspond to differences in the 10th percentile of CMC values by a median of 160 μg/L, ranging from 0 to 610 μg/L, and differences in the 10th percentile of CCC values by a median of 40 μg/L, ranging from 15 to 210 μg/L. The low monthly instantaneous CMC and CCC values that have the greatest effect on the minimum, 5th percentile, and 10th percentile aluminum values tend to result during the nongrowing season (October or November through March or April) when the range of diel variations in pH is small, thus minimizing the effect of diel variations in pH on the lowest CMC and CCC values.
Historical water-quality data on organic carbon in Massachusetts streams were investigated using data retrieved from the USGS National Water Information System database. An assessment of the availability of historical pH, DOC, and hardness data indicated that more data were available for TOC than for DOC. A linear regression equation was developed for the relation between DOC and TOC concentrations to inform the potential use of available data to evaluate water-quality conditions at additional sites across Massachusetts where only pH, hardness, and TOC data are available. DOC and TOC concentrations were well correlated in the 223 samples in which both constituents were analyzed, and the equation had a coefficient of determination (R2) equal to 0.93.
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10 Bearfoot Road
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Biodiversity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well-being. Understanding how biodiversity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying biodiversity data. A major challenge is that biodiversity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has developed the Essential Biodiversity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret biodiversity observation data from diverse sources. Mapping and analyzing EBVs can help to evaluate how aspects of biodiversity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of biodiversity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within-species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic biodiversity monitoring with respect to theory, sampling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: (i) Genetic Diversity; (ii) Genetic Differentiation; (iii) Inbreeding; and (iv) Effective Population Size (Ne). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large-scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for biodiversity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all biodiversity and species' long-term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytical foundations of Genetic EBVs are well developed, and conservation practitioners should anticipate their increasing application as efforts emerge to scale up genetic biodiversity monitoring regionally and globally.
Bathymetric data were collected at 12 water-supply lakes in northwestern Missouri by the U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources and in collaboration with various local agencies, as part of a multiyear effort to establish or update the surface area and capacity tables for the surveyed lakes. Ten of the lakes were surveyed from July to September 2019, one of the original 10 was resurveyed in March 2020, and two lakes of high interest near Maryville were surveyed in June 2020. Six of the lakes had been surveyed by the U.S. Geological Survey before, and the recent surveys were compared to the earlier surveys to document the changes in the bathymetric surface and capacity of the lake and to produce a bathymetric change map.
Bathymetric data were collected using a high-resolution multibeam mapping system mounted on a boat. Supplemental depth data were collected in shallow areas with an acoustic Doppler current profiler on a remote-controlled boat. At Hamilton Reservoir, a Global Navigation Satellite System survey receiver was used to collect additional bathymetric data at several points across four transects and around the perimeter of a substantial shallow area filled with aquatic vegetation upstream from a low-clearance bridge on the northern arm.
Data points from the various sources were exported at a gridded data resolution appropriate to each lake. Data outside the multibeam echosounder survey extent and greater than the surveyed water-surface elevation generally were obtained from data collected using aerial light detection and ranging point cloud data, 1/9 arc-second National Elevation Dataset data based on aerial light detection and ranging data, or both. A linear enforcement technique was used to add points to the dataset in areas of sparse data (the upper ends of coves where the water was shallow or aquatic vegetation precluded data acquisition) based on surrounding multibeam and upland data values. The various point datasets were used to produce a three-dimensional triangulated irregular network surface of the lake-bottom elevations for each lake. A surface area and capacity table was produced from the three-dimensional surface showing surface area and capacity at specified lake water-surface elevations. Various quality-assurance tests were conducted to ensure quality data were collected with the multibeam, including beam angle checks and patch tests. Additional quality-assurance tests were conducted on the gridded bathymetric data from the survey, the bathymetric surface created from the gridded data, and the contours created from the bathymetric survey.
If data from a previous bathymetric survey existed at a given lake, a bathymetric change map was generated from the elevation difference between the previous survey and the 2019 bathymetric survey data points. After applying any vertical elevation changes to the previous survey data to ensure a match to the 2019 survey datum, coincident points between the surveys were found, and a bathymetric change map was generated using the coincident point data.
A decrease in capacity was observed at all the lakes for which a previous survey existed. The decrease in capacity at the primary spillway or intake elevation ranged from 0.8 percent at Lake Viking to 21.4 percent at Middle Fork Grand River Reservoir. The mean bathymetric change ranged from 0.33 foot at Willow Brook Lake to 1.18 feet at Middle Fork Grand River Reservoir. The computed sedimentation rate generally ranged from 0.54 to 4.19 acre-feet per year at Maysville Lake and Middle Fork Grand River Reservoir, respectively; however, Lake Viking had the largest sedimentation rate of 14.9 acre-feet per year, despite having the smallest decrease in capacity at the spillway elevation of only 0.8 percent and a mean bathymetric change of only 0.4 foot. Evidence of dredging was observed in the bathymetric surface for Lake Viking. Some changes observed in some bathymetric change maps are hypothesized to result from the difference in data collection equipment and techniques between the previous and present bathymetric surveys. Certain erosional features around the perimeter of certain lakes may be the result of wave action during low-water years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3486","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Huizinga, R.J., Oyler, L.D., and Rivers, B.C., 2022, Bathymetric contour maps, surface area and capacity tables, and bathymetric change maps for selected water-supply lakes in northwestern Missouri, 2019 and 2020: U.S. Geological Survey Scientific Investigations Map 3486, 12 sheets, includes 21-p. pamphlet, https://doi.org/10.3133/sim3486.","productDescription":"Pamphlet: vi, 21 p.; 13 Sheets: 44.00 x 34.00 inches or smaller; Data Release","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-127919","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501902,"rank":31,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112865.htm","text":"Maryville Reservoir"},{"id":501901,"rank":30,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112864.htm","text":"Mozingo Lake","linkFileType":{"id":5,"text":"html"}},{"id":501900,"rank":29,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112863.htm","text":"Maysville Reservoir","linkFileType":{"id":5,"text":"html"}},{"id":501899,"rank":28,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112862.htm","text":"King City Lake","linkFileType":{"id":5,"text":"html"}},{"id":501898,"rank":27,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112861.htm","text":"Harrison County Lake","linkFileType":{"id":5,"text":"html"}},{"id":501897,"rank":26,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112860.htm","text":"Lake Viking","linkFileType":{"id":5,"text":"html"}},{"id":501894,"rank":23,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112854.htm","text":"Bethany near City Lake","linkFileType":{"id":5,"text":"html"}},{"id":501893,"rank":22,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112853.htm","text":"Willow Brook Lake","linkFileType":{"id":5,"text":"html"}},{"id":501892,"rank":21,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112852.htm","text":"King City South Lake","linkFileType":{"id":5,"text":"html"}},{"id":398203,"rank":16,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet11.1.pdf","text":"Sheet 11.1","size":"1.81 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Mozingo Lake near Maryville, Missouri, 2020"},{"id":398201,"rank":15,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet10.pdf","text":"Sheet 10","size":"1.87 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, surface area and capacity table, and bathymetric change map for Maysville Reservoir near Maysville, Missouri, 2019"},{"id":398196,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet06.pdf","text":"Sheet 6","size":"1.77 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, surface area and capacity table, and bathymetric change map for Middle Fork Grand River Reservoir near Stanberry, Missouri, 2019"},{"id":398195,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet05.pdf","text":"Sheet 5","size":"1.05 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Old Bethany City Lake near Bethany, Missouri, 2019"},{"id":398194,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet04.pdf","text":"Sheet 4","size":"1.71 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Bethany New City Lake near Bethany, Missouri, 2020"},{"id":398193,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet03.pdf","text":"Sheet 3","size":"1.73 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, surface area and capacity table, and bathymetric change map for Willow Brook Lake near Maysville, Missouri, 2019"},{"id":398200,"rank":14,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet09.pdf","text":"Sheet 9","size":"1.23 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for King City Reservoir system near King City, Missouri, 2019"},{"id":398199,"rank":13,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet08.pdf","text":"Sheet 8","size":"1.86 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Harrison County Lake near Bethany, Missouri, 2019"},{"id":398198,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet07.pdf","text":"Sheet 7","size":"2.36 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, surface area and capacity table, and bathymetric change map for Lake Viking near Gallatin, Missouri, 2019"},{"id":501896,"rank":25,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112856.htm","text":"Middle Fork Grand River Reservoir","linkFileType":{"id":5,"text":"html"}},{"id":398192,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet02.pdf","text":"Sheet 2","size":"1.50 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, surface area and capacity table, and bathymetric change map for King City South Lake near King City, Missouri, 2019"},{"id":398214,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet01.pdf","text":"Sheet 1","size":"1.38 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, surface area and capacity table, and bathymetric change map for Hamilton Reservoir near Hamilton, Missouri, 2019"},{"id":501895,"rank":24,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112855.htm","text":"Old Bethany City lake","linkFileType":{"id":5,"text":"html"}},{"id":501891,"rank":20,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112851.htm","text":"Hamilton Reservoir","linkFileType":{"id":5,"text":"html"}},{"id":398831,"rank":19,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sim3486/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"}},{"id":398206,"rank":18,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet12.pdf","text":"Sheet 12","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Maryville Reservoir near Maryville, Missouri, 2020"},{"id":398204,"rank":17,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3486/sim3486_sheet11.2.pdf","text":"Sheet 11.2","size":"2.12 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"—Bathymetric contour map, and surface area and capacity table for Mozingo Lake near Maryville, Missouri, 2020"},{"id":398189,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3486/images"},{"id":398188,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3486/sim3486.XML"},{"id":398186,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3486/coverthb.jpg"},{"id":398190,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92M53NJ","text":"USGS data release","linkHelpText":"Bathymetric and supporting data for various water supply lakes in northwestern Missouri, 2019 and 2020 (ver. 1.1, September 2021)"},{"id":398187,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3486/sim3486.pdf","text":"Pamphlet","size":"1.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3486"}],"country":"United States","state":"Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.03173828125,\n 39.67337039176558\n ],\n [\n -93.84521484375,\n 39.67337039176558\n ],\n [\n -93.84521484375,\n 40.59727063442024\n ],\n [\n -95.03173828125,\n 40.59727063442024\n ],\n [\n -95.03173828125,\n 39.67337039176558\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Rd
Rolla, MO 65401
As the seismological community embraces fiber optic distributed acoustic sensing (DAS), DAS arrays are becoming a logical, scalable option to obtain strain and ground‐motion data for which the installation of seismometers is not easy or cheap, such as in dense offshore arrays. The potential of strain data in earthquake early warning (EEW) applications has been recently demonstrated using records from borehole strainmeters (BSMs). However, current BSM networks are sparse, installing more BSMs is expensive and often impractical, and BSMs have the same limitations in offshore environments as other traditional seismic instruments. Here, we aim to provide a road map about how DAS data could be used in existing EEW applications, using the ShakeAlert EEW System for the West Coast of the United States as an example. We review the data requirements for EEW systems, examine ways in which strain‐derived ground‐motion data can be incorporated into such systems without significant modifications, and determine what is still needed for full utilization of DAS data in these applications. Importantly, EEW algorithms require ground‐motion amplitude information for rapid earthquake source characterization; thus, accurate strain amplitude observations, not only phase information, are necessary for deriving these ground‐motion metrics from DAS data. To obtain high‐quality ground‐motion observations, EEW‐compatible DAS arrays need to be multicomponent, well coupled, and low noise. We suggest ways to achieve such data requirements using existing DAS technology and discuss areas in which further research is needed to optimize DAS array performance for EEW.
Groundwater-level declines and limited quantitative knowledge of the groundwater-flow system in the Harney Basin prompted a cooperative study between the U.S. Geological Survey and the Oregon Water Resources Department to evaluate the groundwater-flow system and budget. This report provides a hydrologic budget of the Harney Basin groundwater system that includes separate groundwater budgets for upland and lowland areas to avoid double counting water that recharges in the uplands, discharges to streams and springs in the uplands, flows downstream to the lowlands, and recharges the lowland groundwater system. Lowlands generally represent the conterminous valleys within the center of the basin, including floodplains of the major streams and uplands represent all other areas in the basin.
The upland groundwater budget is minimally affected by groundwater development and generally represents the budget of the natural system. In upland areas during 1982–2016, mean-annual recharge totaled 288,000 acre-feet (acre-ft) and mean-annual discharge totaled 239,000 acre-ft, resulting in a net recharge of 49,000 acre-ft. Upland groundwater recharge occurs as infiltration of precipitation and snowmelt and was estimated using the USGS Soil-Water-Balance model calibrated to estimates of runoff, evapotranspiration (ET), base flow, and snow-water equivalent. Groundwater discharge to streams is the predominant discharge mechanism in upland areas and was estimated as 225,000 acre-feet per year (acre-ft/yr) during 1982–2016 using hydrograph separation and summer low-flow estimates in streamgaged watersheds and a linear relation between estimated streamflow and base flow in ungaged watersheds. The remaining upland discharge occurs through springs (14,000 acre-ft/yr) that either emerge downgradient of locations where groundwater discharge to streams was estimated or are routed to irrigated areas. Spring discharge was estimated as a compilation of current and historical measurements. The net upland recharge, which is 17 percent of total upland recharge, ultimately recharges lowland areas as groundwater flow from uplands to lowlands.
The lowland groundwater budget for the Harney Basin represents a combination of natural conditions and human activity as more than 99 percent of groundwater development has occurred either inside or within 2 miles of the lowland boundary. In lowland areas during 1982–2016, mean annual groundwater recharge totaled 173,000 acre-ft and groundwater discharge totaled 283,000 acre-ft, indicating discharge exceeded recharge by more than 60 percent.
Excluding groundwater pumping, the lowland groundwater budget is more in balance with a mean annual recharge of 165,000 acre-ft and a mean annual discharge of 131,000 acre-ft during 1982–2016. The 23-percent difference between non-pumping recharge and discharge mostly represents the cumulative uncertainty in the estimates of the various groundwater budget components but also likely includes a small reduction in natural groundwater discharge captured by pumping. Lowland groundwater is predominantly recharged by infiltration of surface water (116,000 acre-ft/yr) through streams, floodwater, and irrigation, with a lesser amount as groundwater inflow from uplands and minimal recharge beneath Malheur and Harney Lakes. Recharge from streams and floodwater (natural and irrigation) was estimated using a balance of measured and estimated surface-water inflow to and outflow from lowland areas including streamflow, springflow, and ET where a portion of surface-water inflow to lowland areas is comprised of upland discharge to streams and springs. Groundwater ET (119,000 acre-ft/yr) is the predominant natural discharge mechanism in lowland areas and was estimated as the mean from two remote-sensing based approaches incorporating groundwater ET measurements from other similar basins and 23 years (1987–2015) of Landsat imagery. Discharge of lowland groundwater into Malheur and Harney Lakes is about 700 acre-ft/yr and is represented in groundwater ET estimates. The remaining natural groundwater discharge from lowland areas issues from Sodhouse Spring (8,900 acre-ft/yr) and as groundwater flow to the Malheur River Basin through Virginia Valley (3,100 acre-ft/yr). The relatively large amount of groundwater discharged to springs in Warm Springs Valley (25,000 acre-ft/yr) is accounted for in groundwater ET estimates. Natural groundwater discharge in lowland areas of the Harney Basin has remained relatively constant during the last 80 years based on comparisons with estimates north of Malheur Lake and west of Harney Lake published in the 1930s.
Annual net amount of groundwater pumped (pumpage) from the Harney Basin during 2017–18 averaged 144,000 acre-ft. The net value is the difference between pumpage (about 152,000 acre-ft/yr) and reinfiltration of groundwater pumped for irrigation and non-irrigation purposes (about 8,000 acre-ft/yr). Net pumpage was estimated in concurrent studies that compiled groundwater-use data and coupled reported groundwater pumpage data from wells with remote-sensing-based ET estimates from groundwater-irrigated fields. Total pumpage for irrigation has increased from about 54,000 acre-ft/yr during 1991–92 to 145,000 acre-ft/yr during 2017–18. Presently, pumpage is greatest in the lowland region north of Malheur Lake (81,000 acre-ft/yr), with lesser amounts to the north and northwest of Harney Lake (41,000 acre-ft/yr) and to the south and east of Malheur Lake (22,000 acre-ft/yr).
During this study, mean annual lowland groundwater discharge (including pumpage) exceeded mean annual recharge, indicating that the lowland hydrologic budget is out of balance. Net groundwater pumpage during 2017–18 is similar to groundwater discharge from all other sources in the lowlands and is four times the imbalance between non-pumping lowland recharge and discharge (34,000 acre-ft/yr). Declining groundwater levels at depth across many parts of the Harney Basin lowlands indicate that pumpage is depleting aquifer storage and is likely capturing a small amount of natural groundwater discharge to springs and ET in some lowland areas. If pumping continues, aquifer storage depletion will continue until the capture rate of natural discharge to springs and ET is equal to the pumping rate. If groundwater development occurs in upland areas and reduces either the streamflow or groundwater inflow to lowland areas, the deficit in the lowland water budget will increase.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215128","collaboration":"Prepared in cooperation with the Oregon Water Resources Department","usgsCitation":"Garcia, C.A., Corson-Dosch, N.T., Beamer, J.P., Gingerich, S.B., Grondin, G.H., Overstreet, B.T., Haynes, J.V., and Hoskinson, M.D., 2021, Hydrologic budget of the Harney Basin groundwater system, southeastern Oregon (ver. 1.1, November 2022): U.S. Geological Survey Scientific Investigations Report 2021–5128, 144 p., https://doi.org/10.3133/sir20215128.","productDescription":"Report: xiii, 144 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-119839","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":502128,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112849.htm","linkFileType":{"id":5,"text":"html"}},{"id":398083,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QABFML","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supplemental data–Hydrologic budget of the Harney Basin groundwater system, Oregon"},{"id":398082,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94NH4D8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil- Water-Balance (SWB) model archive used to simulate mean annual upland recharge from infiltration of precipitation and snowmelt in Harney Basin, Oregon, 1982–2016"},{"id":409214,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5128/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2021-5128 Version History"},{"id":398080,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5128/coverthb2.jpg"},{"id":398081,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5128/sir20215128.pdf","text":"Report","size":"21.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5128"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.08056640625,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 42.35854391749705\n ],\n [\n -117.7734375,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 44.24519901522129\n ],\n [\n -120.08056640625,\n 42.35854391749705\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: April 2022; Version 1.1: November 2022","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
System-scale restoration efforts within the Upper Mississippi River National Wildlife and Fish Refuge have included annual monitoring of submersed aquatic vegetation (SAV) since 1998 in four representative reaches spanning ∼ 440 river kilometers. We developed predictive models relating monitoring data (site-scale SAV abundance indices) to diver-harvested SAV biomass, used the models to back-estimate annual standing stock biomass between 1998 and 2018, and compared biomass estimates with previous abundance measures. We modeled two morphologically distinct groups of SAV with differing sampling efficiencies and estimated each separately: the first category included only wild celery Vallisneria americana, which has long, unbranched leaves and dominates lotic environments, while the second category included 17 branched morphology species (e.g., hornwort Ceratophyllum demersum and Canadian water weed Elodea canadensis) and dominates lentic environments. Wild celery accounted for approximately half of total estimated total biomass in the four reaches, combined branched species accounted for half, and invasive species (Eurasian watermilfoil Myriophyllum spicatum and curly-leaf pondweed Potamogeton crispus), a fraction of the branched species, accounted for < 1.5%. Site-scale SAV estimates ranged from 0 to 535 g·m−2 (dry mass). We observed increases in biomass in most areas between 1998 and 2009 and substantial increases (e.g., from < 10 g·m−2 to ∼ 125 g·m−2) in wild celery in extensive impounded areas between 2002 and 2007. Analyses also indicate a transitional period in 2007–2010 during which changes in biomass trajectories were evident in all reaches and included the start of a 9-y, ∼ 70% decrease in wild celery biomass in the southernmost impounded area. Biomass estimates provided new insights and illustrated scales of change that were not previously apparent using traditional metrics. The ability to estimate biomass from Long Term Resource Monitoring data improves conservation efforts through better understanding of changes in habitat and food resources for biota, improved goal setting for restoration projects and improved quantification of SAV-mediated structural effects such as anchoring of sediments and feedbacks with water quality.
Santa Barbara County, California, USA.
To analyze a wide array of newly collected chemical, isotopic, dissolved gas, and age dating tracers in conjunction with historical data from groundwater and oil wells to determine if water and/or thermogenic gas from oil-bearing formations have mixed with groundwater in the Orcutt Oil Field and surrounding area.
Three of 15 groundwater samples had compositions indicating potential mixing with water and/or thermogenic gas from oil-bearing formations. Relevant indicators included salinity tracers (TDS, Cl, Br), NH3, DOC, enriched δ13C-DIC, δ2H-CH4, δ13C-CH4, and δ13C-C2H6 values, and trace amounts of C3-C5 gas. The potential sources/pathways for oil-bearing formation water and/or thermogenic gas in groundwater overlying and adjacent to the Orcutt Oil Field include: (1) upward movement from formations developed for oil production due to: (a) natural migration; or (b) anthropogenic activity such as injection and/or movement along wellbores; and (2) oil and gas shows in overlying non-producing oil-bearing formations. Groundwater age tracers, elevated 4He concentrations, and isotopic compositions of noble gases indicated legacy produced water ponds were not a source. This phase of the study relied on samples and data from existing infrastructure. Additional data on potential end-member compositions from new and existing wells and assessments of potential vertical head gradients and pathways between oil and groundwater zones may yield additional insight.
Understanding the relative strengths of intrinsic and extrinsic factors regulating populations is a long-standing focus of ecology and critical to advancing conservation programs for imperiled species. Conservation could benefit from an increased understanding of factors influencing vital rates (somatic growth, recruitment, survival) in small, translocated populations, which is lacking owing to difficulties in long-term monitoring of rare species. Translocations, here defined as the transfer of wild-captured individuals from source populations to new habitats, are widely used for species conservation, but outcomes are often minimally monitored, and translocations that are monitored often fail. To improve our understanding of how translocated populations respond to environmental variation, we developed and tested hypotheses related to intrinsic (density dependent) and extrinsic (introduced rainbow trout Oncorhynchus mykiss, stream flow and temperature regime) causes of vital rate variation in endangered humpback chub (Gila cypha) populations translocated to Colorado River tributaries in the Grand Canyon (GC), USA. Using biannual recapture data from translocated populations over 10 years, we tested hypotheses related to seasonal somatic growth, and recruitment and population growth rates with linear mixed-effects models and temporal symmetry mark–recapture models. We combined data from recaptures and resights of dispersed fish (both physical captures and continuously recorded antenna detections) from throughout GC to test survival hypotheses, while accounting for site fidelity, using joint live-recapture/live-resight models. While recruitment only occurred in one site, which also drove population growth (relative to survival), evidence supported hypotheses related to density dependence in growth, survival, and recruitment, and somatic growth and recruitment were further limited by introduced trout. Mixed-effects models explained between 67% and 86% of the variation in somatic growth, which showed increased growth rates with greater flood-pulse frequency during monsoon season. Monthly survival was 0.56–0.99 and 0.80–0.99 in the two populations, with lower survival during periods of higher intraspecific abundance and low flood frequency. Our results suggest translocations can contribute toward the recovery of large-river fishes, but continued suppression of invasive fishes to enhance recruitment may be required to ensure population resilience. Furthermore, we demonstrate the importance of flooding to population demographics in food-depauperate, dynamic, invaded systems.
Over the past ~30 years, hyperspectral remote sensing of chemical variations in white mica have proven to be useful for ore deposit studies in a range of deposit types. To better understand mineral deposits and to guide spectrometer design, this contribution reviews relevant papers from the fields of remote sensing, spectroscopy, and geology that have utilized spectral changes caused by chemical variation in white micas. This contribution reviews spectral studies conducted at the following types of mineral deposits: base metal sulfide, epithermal, porphyry, sedimentary rock hosted gold deposits, orogenic gold, iron oxide copper gold, and unconformity-related uranium. The structure, chemical composition, and spectral features of white micas, in this contribution defined as muscovite, paragonite, celadonite, phengite, illite, and sericite, are given. Reviewed laboratory spectral studies determined that shifts in the position of the white mica 2200 nm combination feature of 1 nm correspond to a change in Aloct content of approximately ±1.05%. Many of the reviewed spectral studies indicated that a shift in the position of the white mica 2200 nm combination feature of 1 nm was geologically significant.
A sensitivity analysis of spectrometer characteristics; bandpass, sampling interval, and channel position, is conducted using spectra of 19 white micas with deep absorption features to determine minimum characteristics required to accurately measure a shift in the position of the white mica 2200 nm combination feature. It was determined that a sampling interval < 16.3 nm and bandpass <17.5 nm are needed to achieve a root mean square error (RMSE) of 2 nm, whereas a sampling interval < 8.8 nm and bandpass <9.8 nm are needed to achieve a RMSE of 1 nm. For comparison, commonly used imaging spectrometers HyMap, AVIRIS-Classic, SpecTIR®'s AisaFENIX 1K, and HySpextm SWIR 384 have 2.1, 1.2, 0.96, and 0.95 nm RMSE in determining the position of the 2200 nm white mica combination feature, respectively.
An additional sensitivity analysis is conducted to determine the effect of signal to noise ratio (SNR) on the RMSE of the position of the white mica 2200 nm combination feature, using spectra of 18 white micas with deep absorption features. For a spectrometer with sampling interval and bandpass of 1 nm, we estimate that RMSEs of 1 and 1.5 nm are achievable with spectra having a minimum SNR of approximately 246 and 64, respectively. For a spectrometer with sampling interval and bandpass of 5 nm, we estimate that RMSEs of 1 and 1.5 nm are attainable with spectra having a minimum SNR of approximately 431 and 84, respectively. When using a spectrometer with a sampling interval 8.8 nm and a bandpass of 9.8 nm, a RMSE of 1 is only achievable with convolved, noiseless reference spectra. For the 8.8_9.8 nm spectrometer, spectra with SNR of 250 and 100 result in RMSE of 1.1 and 1.3, respectively. Therefore, fine spectral resolution characteristics achieve RMSEs better than 1 nm for high SNR spectra while spectrometers with coarse spectral resolution have larger RMSE, perform well with noisy data, and are useful for white mica studies if RMSE of 1.1 to 1.5 nm is acceptable.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113000","usgsCitation":"Meyer, J.M., Holley, E.A., and Kokaly, R.F., 2022, Hyperspectral remote sensing of white mica: A review of imaging and point-based spectrometer studies for mineral resources, with spectrometer design considerations: Remote Sensing of Environment, v. 275, 113000, 18 p., https://doi.org/10.1016/j.rse.2022.113000.","productDescription":"113000, 18 p.","ipdsId":"IP-133226","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":448175,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113000","text":"Publisher Index Page"},{"id":435886,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92VF8HP","text":"USGS data release","linkHelpText":"HySpex by NEO VNIR-1800 and SWIR-384 imaging spectrometer radiance and reflectance data, with associated ASD FieldSpec® NG calibration data, collected at Cripple Creek Victor mine, Cripple Creek, Colorado, 2017"},{"id":407517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"275","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Meyer, John Michael 0000-0003-2810-9414","orcid":"https://orcid.org/0000-0003-2810-9414","contributorId":297062,"corporation":false,"usgs":true,"family":"Meyer","given":"John","email":"","middleInitial":"Michael","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":853194,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holley, Elizabeth A. 0000-0003-2504-4555","orcid":"https://orcid.org/0000-0003-2504-4555","contributorId":265154,"corporation":false,"usgs":false,"family":"Holley","given":"Elizabeth","email":"","middleInitial":"A.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":853195,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":205165,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond","email":"","middleInitial":"F.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":853196,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70262380,"text":"70262380 - 2022 - Environmental drivers of biseasonal anthrax outbreak dynamics in two multihost savanna systems","interactions":[],"lastModifiedDate":"2025-01-23T16:47:51.012419","indexId":"70262380","displayToPublicDate":"2022-04-08T10:41:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1459,"text":"Ecological Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Environmental drivers of biseasonal anthrax outbreak dynamics in two multihost savanna systems","docAbstract":"Environmental factors are common forces driving infectious disease dynamics. We compared interannual and seasonal patterns of anthrax infections in two multihost systems in southern Africa: Etosha National Park, Namibia, and Kruger National Park, South Africa. Using several decades of mortality data from each system, we assessed possible transmission mechanisms behind anthrax dynamics, examining (1) within- and between-species temporal case correlations and (2) associations between anthrax mortalities and environmental factors, specifically rainfall and the Normalized Difference Vegetation Index (NDVI), with empirical dynamic modeling. Anthrax cases in Kruger had wide interannual variation in case numbers, and large outbreaks seemed to follow a roughly decadal cycle. In contrast, outbreaks in Etosha were smaller in magnitude and occurred annually. In Etosha, the host species commonly affected remained consistent over several decades, although plains zebra (Equus quagga) became relatively more dominant. In Kruger, turnover of the main host species occurred after the 1990s, where the previously dominant host species, greater kudu (Tragelaphus strepsiceros), was replaced by impala (Aepyceros melampus). In both parks, anthrax infections showed two seasonal peaks, with each species having only one peak in a year. Zebra, springbok (Antidorcas marsupialis), wildebeest (Connochaetes taurinus), and impala cases peaked in wet seasons, while elephant (Loxodonta africana), kudu, and buffalo (Syncerus caffer) cases peaked in dry seasons. For common host species shared between the two parks, anthrax mortalities peaked in the same season in both systems. Among host species with cases peaking in the same season, anthrax mortalities were mostly synchronized, which implies similar transmission mechanisms or shared sources of exposure. Between seasons, outbreaks in one species may contribute to more cases in another species in the following season. Higher vegetation greenness was associated with more zebra and springbok anthrax mortalities in Etosha but fewer elephant cases in Kruger. These results suggest that host behavioral responses to changing environmental conditions may affect anthrax transmission risk, with differences in transmission mechanisms leading to multihost biseasonal outbreaks. This study reveals the dynamics and potential environmental drivers of anthrax in two savanna systems, providing a better understanding of factors driving biseasonal dynamics and outbreak variation among locations.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecm.1526","usgsCitation":"Yen-Hua Huang, Kyrre Kausrud, Ayesha Hassim, Sunday O. Ochai, van Schalkwyk, O., Edgar H. Dekker, Alexander Buyantuev, Claudine C. Cloete, J. Werner Kilian, Mfune, J.K., Kamath, P., van Heerden, H., and Turner, W.C., 2022, Environmental drivers of biseasonal anthrax outbreak dynamics in two multihost savanna systems: Ecological Monographs, v. 92, no. 4, e1526, 24 p., https://doi.org/10.1002/ecm.1526.","productDescription":"e1526, 24 p.","ipdsId":"IP-132491","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481090,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecm.1526","text":"External Repository"},{"id":481006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Namibia, South Africa","otherGeospatial":"Etosha National Park, Kruger National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 13.99697075011565,\n -17.9704803255822\n ],\n [\n 14.042362443293712,\n -19.592355004499595\n ],\n [\n 17.575676967960106,\n -19.575250462094317\n ],\n [\n 17.575676967960106,\n -17.953206628620634\n ],\n [\n 13.99697075011565,\n -17.9704803255822\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 30.65264945923775,\n -22.32856930947861\n ],\n [\n 31.245229473428566,\n -25.55857896070789\n ],\n [\n 32.07283709158796,\n -25.569715149900304\n ],\n [\n 31.896147080990332,\n -23.97674400909702\n ],\n [\n 31.31940421620908,\n -22.35940535120622\n ],\n [\n 30.65264945923775,\n -22.32856930947861\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"92","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-05-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Yen-Hua Huang","contributorId":349084,"corporation":false,"usgs":false,"family":"Yen-Hua Huang","affiliations":[{"id":83418,"text":"Wisconsin Cooperative Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":923989,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kyrre Kausrud","contributorId":349085,"corporation":false,"usgs":false,"family":"Kyrre Kausrud","affiliations":[{"id":61713,"text":"Norwegian Veterinary Institute","active":true,"usgs":false}],"preferred":false,"id":923990,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ayesha Hassim","contributorId":349086,"corporation":false,"usgs":false,"family":"Ayesha Hassim","affiliations":[{"id":83425,"text":"Department of Veterinary Tropical Diseases","active":true,"usgs":false}],"preferred":false,"id":923991,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sunday O. Ochai","contributorId":349088,"corporation":false,"usgs":false,"family":"Sunday O. Ochai","affiliations":[{"id":61713,"text":"Norwegian Veterinary Institute","active":true,"usgs":false}],"preferred":false,"id":923992,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"van Schalkwyk, O. Louis","contributorId":349092,"corporation":false,"usgs":false,"family":"van Schalkwyk","given":"O. Louis","affiliations":[{"id":83426,"text":"Department of Migration","active":true,"usgs":false}],"preferred":false,"id":923993,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edgar H. Dekker","contributorId":349093,"corporation":false,"usgs":false,"family":"Edgar H. 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Werner Kilian","affiliations":[{"id":61496,"text":"Etosha Ecological Institute","active":true,"usgs":false}],"preferred":false,"id":923997,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mfune, John K.E.","contributorId":287158,"corporation":false,"usgs":false,"family":"Mfune","given":"John","email":"","middleInitial":"K.E.","affiliations":[{"id":39588,"text":"University of Namibia","active":true,"usgs":false}],"preferred":false,"id":924921,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kamath, Pauline L.","contributorId":287148,"corporation":false,"usgs":false,"family":"Kamath","given":"Pauline L.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":924922,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"van Heerden, Henriette","contributorId":343077,"corporation":false,"usgs":false,"family":"van Heerden","given":"Henriette","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":924923,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923988,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70230180,"text":"sir20225023 - 2022 - Implementing a rapid deployment bridge scour monitoring system in Colorado, 2019","interactions":[],"lastModifiedDate":"2026-04-09T16:55:33.35947","indexId":"sir20225023","displayToPublicDate":"2022-04-07T13:40:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5023","displayTitle":"Implementing a Rapid Deployment Bridge Scour Monitoring System in Colorado, 2019","title":"Implementing a rapid deployment bridge scour monitoring system in Colorado, 2019","docAbstract":"The U.S. Geological Survey, in cooperation with the Colorado Department of Transportation, installed and operated real-time scour monitoring instrumentation at two bridges in Colorado in 2016 and 2017 to measure streambed elevations in real-time. The instrumentation included acoustic echosounder depth sensors mounted to the bridge substructure units with rigid conduit and fittings. Although functional, the rigid mounting configuration took several days to install at each site, which limits the instrumentation to long-term deployments at previously determined sites. To address this limitation and allow for greater flexibility in bridge selection, a rapid deployment bridge scour monitoring system (RDBSMS) was developed by the U.S. Geological Survey in cooperation with the Colorado Department of Transportation. The RDBSMSs were installed at two other bridges in Colorado in 2019, which were selected by using specific scoring criteria to rank candidate bridges and the potential for high streamflow based on accumulated snowpack. A matrix was developed to rank candidate bridges based on factors including depth, foundation type, average daily traffic, detour route, and scour critical condition. Colorado Department of Transportation bridges F-05-R and P-01-G were selected as the final candidate bridges for installation and testing of the rapid deploy scour monitoring system.
Bridge F-05-R carries Colorado Highway 13 over the Colorado River near the town of Rifle, Colorado. Because of the misalignment of the pier wall with respect to the river, pier number 4 was instrumented on the left side (looking downstream) to monitor scour conditions. Bridge P-01-G carries U.S. Route 160 over the San Juan River near the Four Corners area in Colorado. Because of misalignment of the pier wall with respect to the river, pier number 4 was instrumented on the right side (looking downstream) to monitor scour conditions. The RDSMSs were installed in approximately 3 hours at each bridge.
Scour conditions at both bridges were monitored during the snowmelt runoff period in 2019 using the installed RDBSMSs. No major scour events occurred at either structure, but minor scour and fill was measured at each. Sensor performance at F-05-R was excellent, with no missing or erroneous data. Sensor performance at P-01-G was good for most of the period, with some missing and erroneous data during periods of high turbidity.
Both RDBSMSs were successfully deployed and produced reliable data, demonstrating that both the technology and the installation methods can work in two different riverine environments. Pre-installation of mounting plates would make the installation process faster at flood prone bridges. Having flood prone bridges preconfigured and several RDBSMSs ready to deploy could allow for rapid monitoring during floods such as those which occurred in 2013.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225023","collaboration":"Prepared in cooperation with the Colorado Department of Transportation","usgsCitation":"Henneberg, M.F., and Richards, R.J., 2022, Implementing a rapid deployment bridge scour monitoring system in Colorado, 2019: U.S. Geological Survey Scientific Investigations Report 2022–5023, 18 p., https://doi.org/10.3133/sir20225023.","productDescription":"Report: iv, 18 p.; Database","onlineOnly":"Y","ipdsId":"IP-125349","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":397983,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5023/sir20225023.pdf","text":"Report","size":"8.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5023"},{"id":397982,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5023/coverthb.jpg"},{"id":397986,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5023/sir20225023.xml"},{"id":397984,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System—","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"},{"id":397985,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5023/images"},{"id":502379,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112844.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.984375,\n 37.020098201368114\n ],\n [\n -103.35937499999999,\n 37.020098201368114\n ],\n [\n -103.35937499999999,\n 41.11246878918088\n ],\n [\n -108.984375,\n 41.11246878918088\n ],\n [\n -108.984375,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225