Understanding ecological relationships among fishes and their environments are important for informing management policies. We conducted a statewide assessment of cisco (Coregonus artedi) in inland lakes of Wisconsin to better understand the status of this pelagic, coldwater forage fish. We then used long-term (2005–2014), standardized walleye (Sander vitreus) survey data from the Ceded Territory of Wisconsin (CTWI) to test for the influence of cisco (present, extirpated, or never present) and several abiotic factors on walleye growth trajectories described using sex-specific asymptotic lengths (L∞), Brody growth coefficients (K), and time in years required to attain common length limits used to manage harvest of walleye in the recreational fishery (381 and 457 mm). Despite being top predators in many north-temperate waters, walleye growth was highly variable among lakes, suggesting that forage base and abiotic factors may be important drivers. Growth characteristics of 160 CTWI walleye populations revealed that females reached greatest L∞ in lakes with cisco compared to those where cisco were never present or those lakes where cisco have been extirpated; however, differences were not statistically significant. Male walleye L∞ did not differ based on cisco presence. Brody growth coefficients (K) for female walleye were positively correlated with growing degree days and Secchi depth; K for males was positively correlated with Secchi depth. Average time to attain 381 and 457 mm were lowest in lakes where cisco have been extirpated. Our results suggest that cooler water temperatures and lower water clarity may be more important drivers of walleye maximum growth potential in northern Wisconsin lakes than the presence of cisco.
","language":"English","publisher":"Schweizerbart Science Publishers","doi":"10.1127/adv_limnol/2021/0061","usgsCitation":"Noring, A.M., Sass, G., Midway, S., VanDeHey, J.A., Raabe, J., Isermann, D.A., Kampa, J., Parks, T., Lyons, J., and Jennings, M.J., 2021, Pelagic forage versus abiotic factors as drivers of walleye growth in northern Wisconsin lakes: Advances in Limnology, v. 66, p. 207-223, https://doi.org/10.1127/adv_limnol/2021/0061.","productDescription":"17 p.","startPage":"207","endPage":"223","ipdsId":"IP-093425","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":400062,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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University","active":true,"usgs":false}],"preferred":false,"id":804009,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"VanDeHey, Justin A.","contributorId":244468,"corporation":false,"usgs":false,"family":"VanDeHey","given":"Justin","email":"","middleInitial":"A.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":804010,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Raabe, Joshua K.","contributorId":244469,"corporation":false,"usgs":false,"family":"Raabe","given":"Joshua K.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":804011,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":804006,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kampa, Jeffrey M.","contributorId":244470,"corporation":false,"usgs":false,"family":"Kampa","given":"Jeffrey M.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":804012,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Parks, Timothy P.","contributorId":244471,"corporation":false,"usgs":false,"family":"Parks","given":"Timothy P.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":804013,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lyons, John","contributorId":244472,"corporation":false,"usgs":false,"family":"Lyons","given":"John","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":804014,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jennings, Martin J.","contributorId":244473,"corporation":false,"usgs":false,"family":"Jennings","given":"Martin","email":"","middleInitial":"J.","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":804015,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70227001,"text":"70227001 - 2021 - Historic coregonine habitat use and assessment of larval nursery locations in Lake Erie","interactions":[],"lastModifiedDate":"2021-12-27T14:40:08.87022","indexId":"70227001","displayToPublicDate":"2021-12-15T08:38:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":656,"text":"Advances in Limnology","active":true,"publicationSubtype":{"id":10}},"title":"Historic coregonine habitat use and assessment of larval nursery locations in Lake Erie","docAbstract":"Coregonine fishes (Coregonus spp.) are important components of Great Lake food webs and support lucrative commercial and recreational fisheries. Due to a combination of several factors including habitat loss, over-exploitation, and introduction of exotic species, the distribution and abundance of coregonines have been reduced. Examples of these declines are evident in Lake Erie where cisco (C. artedi) have been nearly extirpated, and lake whitefish (C. clupeaformis), while still abundant, are declining. To identify key habitat locations of coregonines in Lake Erie, we conducted a literature review of historical spawning, nursery, and adult habitat sites where coregonines have been observed. We used these sites as a reference for larval sampling at six locations across the southern shore of Lake Erie during spring 2017. Paired bongo samplers were used to collect larvae, and average densities were calculated for comparison across sites. Larval coregonine (46 visually identified as lake whitefish; 8 classified as coregonines) densities were highest at Huron, OH (0.880/1,000 m3 ± 1.61), followed by Sandusky, OH (0.426/1,000 m3 ± 1.05), Dunkirk, NY (0.208/1,000 m3 ± 0.703), Fairport, OH (0.185/1,000 m3 ± 0.680), Erie, PA (0.120/1,000 m3 ± 0.532), and Conneaut, OH (0.1196/1,000 m3 ± 0.528). Using contemporary sampling data coupled with historical spawning locations, we identified sites that are currently being used as nursery locations by lake whitefish. By validating the contemporary use of historic spawning and nursery sites, this study identifies locations where habitat protection and restoration or future stocking of coregonids could be conducted in Lake Erie in efforts to recover populations and improve fishery production.
","language":"English","publisher":"Schweizerbart Science","doi":"10.1127/adv_limnol/2021/0072","usgsCitation":"Schaefer, H.M., Roseman, E., DeBruyne, R.L., Vandergoot, C., and Diana, J.S., 2021, Historic coregonine habitat use and assessment of larval nursery locations in Lake Erie: Advances in Limnology, v. 66, p. 245-259, https://doi.org/10.1127/adv_limnol/2021/0072.","productDescription":"15 p.","startPage":"245","endPage":"259","ipdsId":"IP-094017","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":393414,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.82568359375,\n 41.50857729743935\n ],\n [\n -82.41943359375,\n 41.1290213474951\n ],\n [\n -80.9912109375,\n 41.45919537950706\n ],\n [\n -78.57421875,\n 42.633958722673135\n ],\n [\n -78.72802734375,\n 43.068887774169625\n ],\n [\n -80.31005859375,\n 43.18114705939968\n ],\n [\n -81.93603515625,\n 42.69858589169842\n ],\n [\n -83.27636718749999,\n 42.4234565179383\n ],\n [\n -83.7158203125,\n 41.902277040963696\n ],\n [\n -83.82568359375,\n 41.50857729743935\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"66","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schaefer, Hannah M","contributorId":216810,"corporation":false,"usgs":false,"family":"Schaefer","given":"Hannah","email":"","middleInitial":"M","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":829138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":829139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeBruyne, Robin L. 0000-0002-9232-7937 rdebruyne@usgs.gov","orcid":"https://orcid.org/0000-0002-9232-7937","contributorId":4936,"corporation":false,"usgs":true,"family":"DeBruyne","given":"Robin","email":"rdebruyne@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":829140,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vandergoot, Christopher 0000-0003-4128-3329 cvandergoot@usgs.gov","orcid":"https://orcid.org/0000-0003-4128-3329","contributorId":178356,"corporation":false,"usgs":true,"family":"Vandergoot","given":"Christopher","email":"cvandergoot@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":829141,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Diana, James S.","contributorId":216547,"corporation":false,"usgs":false,"family":"Diana","given":"James","email":"","middleInitial":"S.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":829142,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226871,"text":"70226871 - 2021 - Effects of low pH on the coral reef cryptic invertebrate communities near CO2 vents in Papua New Guinea","interactions":[],"lastModifiedDate":"2021-12-17T14:43:26.31306","indexId":"70226871","displayToPublicDate":"2021-12-15T08:27:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Effects of low pH on the coral reef cryptic invertebrate communities near CO2 vents in Papua New Guinea","title":"Effects of low pH on the coral reef cryptic invertebrate communities near CO2 vents in Papua New Guinea","docAbstract":"Small cryptic invertebrates (the cryptofauna) are extremely abundant, ecologically important, and species rich on coral reefs. Ongoing ocean acidification is likely to have both direct effects on the biology of these organisms, as well as indirect effects through cascading impacts on their habitats and trophic relationships. Naturally acidified habitats have been important model systems for studying these complex interactions because entire communities that are adapted to these environmental conditions can be analyzed. However, few studies have examined the cryptofauna because they are difficult to census quantitatively in topographically complex habitats and are challenging to identify. We addressed these challenges by using Autonomous Reef Monitoring Structures (ARMS) for sampling reef-dwelling invertebrates >2 mm in size and by using DNA barcoding for taxonomic identifications. The study took place in Papua New Guinea at two reef localities, each with three sites at varying distances from carbon dioxide seeps, thereby sampling across a natural gradient in acidification. We observed sharp overall declines in both the abundance (34–56%) and diversity (42–45%) of organisms in ARMS under the lowest pH conditions sampled (7.64–7.75). However, the overall abundance of gastropods increased slightly in lower pH conditions, and crustacean and gastropod families exhibited varying patterns. There was also variability in response between the two localities, despite their close proximity, as one control pH site displayed unusually low diversity and abundances for all invertebrate groups. The data illustrate the complexity of responses of the reef fauna to pH conditions, and the role of additional factors that influence the diversity and abundance of cryptic reef invertebrates.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0258725","usgsCitation":"Plaisance, L., Matterson, K., Fabricius, K., Drovetski, S.V., Meyer, C.F., and Knowlton, N., 2021, Effects of low pH on the coral reef cryptic invertebrate communities near CO2 vents in Papua New Guinea: PLoS ONE, v. 16, no. 12, e0258725, 19 p., https://doi.org/10.1371/journal.pone.0258725.","productDescription":"e0258725, 19 p.","ipdsId":"IP-125629","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":450019,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0258725","text":"Publisher Index Page"},{"id":393046,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Papua New Guinea","state":"Milne Bay Province","otherGeospatial":"Dobu, Upa Upasina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 150.8474349975586,\n -9.771994523201766\n ],\n [\n 150.8917236328125,\n -9.771994523201766\n ],\n [\n 150.8917236328125,\n -9.733590552033547\n ],\n [\n 150.8474349975586,\n -9.733590552033547\n ],\n [\n 150.8474349975586,\n -9.771994523201766\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 150.77533721923828,\n -9.812593019509318\n ],\n [\n 150.79696655273438,\n -9.812593019509318\n ],\n [\n 150.79696655273438,\n -9.791279427997022\n ],\n [\n 150.77533721923828,\n -9.791279427997022\n ],\n [\n 150.77533721923828,\n -9.812593019509318\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Plaisance, Laetitia","contributorId":270161,"corporation":false,"usgs":false,"family":"Plaisance","given":"Laetitia","email":"","affiliations":[{"id":56101,"text":"Laboratoire Evolution et Diversité Biologique, CNRS/UPS, Toulouse, France","active":true,"usgs":false}],"preferred":false,"id":828550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matterson, Kenan O.","contributorId":203367,"corporation":false,"usgs":false,"family":"Matterson","given":"Kenan O.","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":828551,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fabricius, Katharina","contributorId":270162,"corporation":false,"usgs":false,"family":"Fabricius","given":"Katharina","email":"","affiliations":[{"id":56102,"text":"Australian Institute of Marine Science, Townsville, Queensland, Australia","active":true,"usgs":false}],"preferred":false,"id":828552,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drovetski, Sergei V. 0000-0002-1832-5597","orcid":"https://orcid.org/0000-0002-1832-5597","contributorId":229520,"corporation":false,"usgs":true,"family":"Drovetski","given":"Sergei","middleInitial":"V.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":828553,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meyer, Christoph F. J.","contributorId":245693,"corporation":false,"usgs":false,"family":"Meyer","given":"Christoph","email":"","middleInitial":"F. J.","affiliations":[{"id":49282,"text":"Centre for Ecology, Evolution & Environmental Changes, University of Lisbon, Portugal; National Institute for Amazonian Research & Smithsonian Tropical Research Institute, Manaus, Brazil; University of Salford, UK","active":true,"usgs":false}],"preferred":false,"id":828554,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knowlton, Nancy","contributorId":174345,"corporation":false,"usgs":false,"family":"Knowlton","given":"Nancy","email":"","affiliations":[{"id":27432,"text":"Department of Invertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, DC 20013, USA","active":true,"usgs":false}],"preferred":false,"id":828555,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230949,"text":"70230949 - 2021 - How well do we know Europa’s topography? An evaluation of the variability in digital terrain models of Europa.","interactions":[],"lastModifiedDate":"2022-04-29T12:14:14.148908","indexId":"70230949","displayToPublicDate":"2021-12-15T07:12:12","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"How well do we know Europa’s topography? An evaluation of the variability in digital terrain models of Europa.","docAbstract":"Demographic studies of many bird species are challenging because their nests are cryptic, resulting in few nests being found. To maximize statistical power, methods are needed that minimize disturbance while yielding as much information per nest as possible. One way to meet these objectives is to use miniature thermal data loggers to precisely date nest fates. Our objectives, therefore, were to (1) examine the possible effect of thermal data loggers on nest success through hatching by grass- and shrub-nesting songbirds that differed in their parasite egg-accepting and -rejecting behavior, (2) examine the effect of using daily temperature data versus less frequent nest-visit data on statistical power, bias, and precision when estimating the daily survival rate (DSR) for nests, and (3) compare these two approaches using a simulation study and field data. We monitored the survival of nests located in agricultural landscapes and used a binomial logistic regression with main effects for data-loggers and parasite-accepting or -rejecting status and their interaction. We also compared maximum likelihood–derived DSR for differences in estimated rates, precision, and sample sizes with both data collected in the field and simulated with varying sample sizes and visit frequencies. We found no evidence that thermal data loggers had any effect on hatching rates either for all species or for parasite egg-accepting and -rejecting species, separately. Both our simulation and analysis of real nest data indicated that use of data loggers increased the statistical power from each nest studied by increasing effective sample sizes and precision of DSR estimates compared to in-person visits. We also found a negative bias in DSR estimates with longer visit intervals, which use of data-loggers removed. Both the results of simulated- and field-data analyses suggest that future studies of nest survival can be improved by automated nest monitoring by removing a source of bias and providing more time to find additional nests.
","language":"English","publisher":"Wiley","doi":"10.1111/jofo.12389","usgsCitation":"Stephenson, M., Klaver, R.W., Schulte, L., and Niemi, J., 2021, Miniature temperature data loggers increase precision and reduce bias when estimating the daily survival rate for bird nests: Journal of Field Ornithology, v. 92, no. 4, p. 492-505, https://doi.org/10.1111/jofo.12389.","productDescription":"14 p.","startPage":"492","endPage":"505","ipdsId":"IP-110086","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481099,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jofo.12389","text":"Publisher Index Page"},{"id":480767,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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A.","contributorId":349258,"corporation":false,"usgs":false,"family":"Schulte","given":"Lisa A.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":924177,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Niemi, Jarad","contributorId":349259,"corporation":false,"usgs":false,"family":"Niemi","given":"Jarad","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":924178,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70226829,"text":"ofr20211094 - 2021 - Geochronologic, isotopic, and geochemical data from pre-Cretaceous plutonic rocks in the Lane Mountain area, San Bernardino County, California","interactions":[],"lastModifiedDate":"2023-03-29T17:44:09.016282","indexId":"ofr20211094","displayToPublicDate":"2021-12-14T12:02:52","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1094","displayTitle":"Geochronologic, Isotopic, and Geochemical Data from Pre- Cretaceous Plutonic Rocks in the Lane Mountain Area, San Bernardino County, California","title":"Geochronologic, isotopic, and geochemical data from pre-Cretaceous plutonic rocks in the Lane Mountain area, San Bernardino County, California","docAbstract":"Pre-Cretaceous, predominantly dioritic plutonic rocks in the Lane Mountain area, California, intrude metasedimentary and metavolcanic rocks considered part of the El Paso terrane. New geochronologic (uranium-lead zircon), geochemical, and isotopic data provide a reliable basis for dividing these pre-Cretaceous plutonic rocks into two mappable suites of Permian–Triassic and Late Jurassic ages. The 26 Permian–Triassic samples included in this report have a mean age of ~248 mega-annum (Ma), range in composition from monzodiorite to quartz monzonite and granodiorite, and have a mean initial 87Sr/86Sr ratio (Sri) of ~0.7045. The 22 Late Jurassic samples have a mean age of ~149 Ma, range in composition from gabbro to granite, and have a mean Sri of ~0.7055. Accurate mapping of these two plutonic suites and their detailed field relations with the associated metamorphic rocks is essential for resolving the geologic history and regional tectonic significance of the Lane Mountain area.
The sub-0.706 Sri values of both plutonic suites at Lane Mountain are consistent with previous suggestions that the El Paso terrane is allochthonous and did not develop on Precambrian continental lithosphere. Both suites are considered parts of northwest-trending magmatic arcs interpreted to have formed above east-dipping subduction zones along the evolving North American continental margin, and both arcs are interpreted to cross a major east-west-trending boundary between the El Paso terrane and rocks considered part of ancestral North America in the San Bernardino Mountains area to the south. The El Paso terrane thus appears to have been attached to the San Bernardino Mountains area at least since Permian–Triassic time, although the boundary probably has been modified by Cenozoic faulting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211094","usgsCitation":"Stone, P., Brown, H.J., Cecil, M.R., Fleck, R.J., Vazquez, J.A., and Fitzpatrick, J.A., 2021, Geochronologic, isotopic, and geochemical data from pre-Cretaceous plutonic rocks in the Lane Mountain area, San Bernardino County, California: U.S. Geological Survey Open-File Report 2021–1094, 74 p., https://doi.org/10.3133/ofr20211094.","productDescription":"viii, 74 p.","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-121822","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":436088,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KRD45S","text":"USGS data release","linkHelpText":"Tabular geochronologic, geochemical, and isotopic data from igneous rocks in the Lane Mountain area, San Bernardino County, 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Bernardino\",\"state\":\"CA\"}}]}","contact":"Contact Information,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035
Lava lake surfaces display the tops of active magma columns and respond to eruption variables such as magmatic pressure, convection, degassing, and cooling, as well as interactions with the craters that contain them. However, they are challenging to study owing to the numerous hazards that accompany these eruptions, and they are typically difficult to observe because the emitted gas plumes obscure the lava lake surfaces. The 2008–2018 Overlook crater and lava lake at Kīlauea Volcano, Hawaiʻi, provided a remarkable opportunity to study several high-resolution data streams of eruption variables that impacted the lava lake. To investigate how the crater and associated lava lake responded to changes in these eruption variables, we acquired terrestrial light detection and ranging (lidar) surveys of the Overlook crater and lava lake surface from February 2012 through December 2013, supplemented with several earlier terrestrial and airborne lidar datasets, to quantitatively track changes in the shape of the lava lake surface and the crater walls. Lidar captures high-resolution data even when the lake is completely obscured by thick gas plumes. We used a novel “unrolling technique” to map volumetric changes in crater shape, because standard elevation differencing fails to capture all topographic changes on the nearly vertical, and sometimes overhanging, crater walls. We measured crater perimeter growth rates of approximately 52 meters per year from 2009 to 2013, with the greatest growth occurring along a line linking areas of persistent upwelling and downwelling. We suggest that the development of an oblong crater with a perimeter that grows linearly is best explained by a model where degradation is favored at the sites of persistent upwelling and downwelling and where growth is controlled by a lithology that varies little with respect to rock strength. We also found that most of the Overlook crater growth occurred during a relatively small number of significant rockfall events (~16) over this period. Additional lidar datasets revealed that the lava lake surface has a measurable slope from the areas of persistent upwelling to downwelling, although rockfalls from the crater walls temporarily changed the direction of crustal plate movement along with the magnitude and direction of the lava lake surface slope. Our study demonstrates that lidar is an effective tool for tracking the topography of an active volcanic crater when heavy outgassing renders other tools, such as structure from motion, ineffective.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1867C","usgsCitation":"LeWinter, A.L., Anderson, S.W., Finnegan, D.C., Patrick, M.R., and Orr, T.R., 2021, Crater growth and lava-lake dynamics revealed through multitemporal terrestrial lidar scanning at Kīlauea Volcano, Hawaiʻi, chap. C of Patrick, M., Orr, T., Swanson, D., and Houghton, B., eds., The 2008–2018 summit lava lake at Kīlauea Volcano, Hawaiʻi: U.S. Geological Survey Professional Paper 1867, 26 p., https://doi.org/10.3133/pp1867C.","productDescription":"viii, 26 p.","numberOfPages":"26","onlineOnly":"N","ipdsId":"IP-121567","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":392860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1867/c/covrthb.jpg"},{"id":392861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1867/c/pp1867c.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.3192138671875,\n 19.37593175537523\n ],\n [\n -155.21896362304685,\n 19.37593175537523\n ],\n [\n -155.21896362304685,\n 19.460118162137714\n ],\n [\n -155.3192138671875,\n 19.460118162137714\n ],\n [\n -155.3192138671875,\n 19.37593175537523\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact HVO
Hawaiian Volcano Observatory
U.S. Geological Survey
1266 Kamehameha Avenue
Suite A-8
Hilo, HI 96720
The collapse of Diporeia spp. and invasions of dreissenid mussels (zebra, Dreissena polymorpha; quagga, D. bugensis) and round goby (Neogobius melanostomus) have been associated with declines in abundance of native benthic fishes in the Great Lakes, including historically abundant slimy sculpin (Cottus cognatus). We hypothesized that as round goby colonized deeper habitat, slimy sculpin avoided habitat competition, predation, and aggression from round goby by shifting to deeper habitat. Accordingly, we predicted increased depth overlap of slimy sculpin with both round goby and deepwater sculpin (Myoxocephalus thompsonii) that resulted in habitat squeeze by both species. We used long-term bottom trawl data from Lakes Michigan, Huron, and Ontario to evaluate shifts in slimy sculpin depth and their depth overlap with round goby and deepwater sculpin. Lake Huron most supported our hypotheses as slimy sculpin shifted to deeper habitat coincident with the round goby invasion, and depth overlap between slimy sculpin and both species recently increased. Slimy sculpin depth trends in Lakes Michigan and Ontario suggest other ecological and environmental factors better predicted sculpin depth in these lakes.
Context: Camera trapping is increasingly used to collect information on wildlife occurrence and behaviour remotely. Not only does the technique provide insights into habitat use by species of interest, it also gathers information on non-target species.
Aims: We implemented ground-based camera trapping to investigate the behaviours of ground-dwelling birds, a technique that has largely been unutilised for studying birds, especially in wind-energy facilities.
Methods: We used camera traps to monitor activities of Agassiz’s desert tortoises (Gopherus agassizii) at their self-constructed burrows in a wind-energy facility near Palm Springs, California, USA. While doing so, we collected data on numerous burrow commensals, including birds.
Key results: Monitoring from late spring to mid-autumn in one year showed regular use of tortoise burrows and the immediate area by 12 species of birds, especially passerines. The most abundant species, as indicated by the number of photographs, but not necessarily individuals, was the rock wren (Salpinctes obsoletus), with a total of 1499 events. Birds appeared to use the interior or proximate vicinity of burrows for gathering nesting material, displaying, feeding, dust bathing and other activities. Of the bird species observed, 10 are known to be occasional casualties of turbine-blade strikes. The minimum known-age of a burrow had a positive relationship with bird counts.
Conclusions: Using camera traps focused at ground level can be a useful tool in avian conservation efforts because it is an effective technique for measuring bird presence, activity and behaviour in altered habitats such as wind farms, especially for those species that are low flyers or ground dwellers.
Implications: Acquiring data over the long term by using ground-based monitoring with camera traps could add to our understanding of avian behaviour and habitat use in relation to wind-energy infrastructure and operations, and help determine the vulnerability of avifauna that utilise the area.
","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/WR21071","usgsCitation":"Puffer, S., Tennant, L.A., Lovich, J.E., Agha, M., Smith, A.L., Delaney, D., Arundel, T.R., Fleckenstein, L.J., Briggs, J., Walde, A., and Ennen, J., 2021, Birds not in flight: Using camera traps to observe ground use of birds at a wind-energy facility: Wildlife Research, v. 49, p. 283-294, https://doi.org/10.1071/WR21071.","productDescription":"12 p.","startPage":"283","endPage":"294","ipdsId":"IP-116087","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":393506,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Palm Springs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 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Mickey","contributorId":22235,"corporation":false,"usgs":false,"family":"Agha","given":"Mickey","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false},{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":829410,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Amanda L. amandasmith@usgs.gov","contributorId":193098,"corporation":false,"usgs":true,"family":"Smith","given":"Amanda","email":"amandasmith@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":829411,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Delaney, David","contributorId":75444,"corporation":false,"usgs":true,"family":"Delaney","given":"David","affiliations":[],"preferred":false,"id":829412,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Arundel, Terence R. 0000-0003-0324-4249 tarundel@usgs.gov","orcid":"https://orcid.org/0000-0003-0324-4249","contributorId":139242,"corporation":false,"usgs":true,"family":"Arundel","given":"Terence","email":"tarundel@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829413,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fleckenstein, Leo J.","contributorId":196259,"corporation":false,"usgs":false,"family":"Fleckenstein","given":"Leo","email":"","middleInitial":"J.","affiliations":[{"id":13019,"text":"Department of Forestry, University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":829414,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Briggs, Jessica","contributorId":22691,"corporation":false,"usgs":true,"family":"Briggs","given":"Jessica","affiliations":[],"preferred":false,"id":829415,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Walde, Andrew","contributorId":212741,"corporation":false,"usgs":false,"family":"Walde","given":"Andrew","affiliations":[],"preferred":false,"id":829416,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Ennen, Joshua","contributorId":72691,"corporation":false,"usgs":true,"family":"Ennen","given":"Joshua","affiliations":[],"preferred":false,"id":829417,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70236523,"text":"70236523 - 2021 - Influence of antecedent geology on the Holocene formation and evolution of Horn Island, Mississippi, USA","interactions":[],"lastModifiedDate":"2022-09-09T12:28:43.889046","indexId":"70236523","displayToPublicDate":"2021-12-11T07:25:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Influence of antecedent geology on the Holocene formation and evolution of Horn Island, Mississippi, USA","docAbstract":"Horn Island, one of the two most stable barriers along the Mississippi-Alabama chain (Cat, East and West Ship, Horn, West Petit Bois, Petit Bois, and Dauphin), provides critical habitat, helps regulate estuarine conditions in the Mississippi Sound, and reduces wave energy and storm surge before they reach the mainland shore. However, important details of the formation and evolution of the island in response to sea-level rise, storms, and antecedent geology remain unclear. This study integrates 2200 km of high-resolution geophysical data, 35 sediment cores, and 18 radiocarbon ages to better understand the geologic history of the island. Incised valleys of the Biloxi and Pascagoula Rivers underlie Horn Island and played a profound role in the evolution of the system. Within the incised valleys, sandy paleochannel deposits represent potential sediment sources during island development. Scour associated with wave and tidal ravinement processes liberated sand from the paleochannels and along with numerous other sizable sand sources on the shelf contributed to the formation and continued maintenance of Horn Island. Based on radiocarbon ages, transgressive ephemeral islands/shoals with no preserved shoreface existed at least 8000 cal yr BP and were frequently overwashed when sea-level rise rates were ~ 4–5 mm/yr. Approximately 5000 cal yr BP, coinciding with a deceleration in sea-level rise to about 1.4 mm/yr and attendant increased sand supply, radiocarbon ages associated with Horn Island's barrier complex and lower shoreface indicate a period of island stabilization. Seismic and sediment core data show a long history of westward lateral migration by longshore currents through tidal ravinement and inlet fill. Subsurface sand packages associated with tidal inlet fill and paleochannels are available for ravinement and may be important sand sources for Horn Island to maintain subaerial exposure with the expected accelerated future sea-level rise.
Using diagnostic data and contemporary sampling efforts, we conducted surveillance for a diversity of pathogens, toxicants, and diseases of muskrats (Ondatra zibethicus). Between 1977 and 2019, 26 diagnostic cases were examined from Kansas and throughout the Southeast and Mid-Atlantic, USA. We identified multiple causes of mortality in muskrats, but trauma (8/26), Tyzzer’s disease (5/6), and cysticercosis (5/26) were the most common. We also conducted necropsies, during November 2018—January 2019 Pennsylvania muskrat trapping season, on 380 trapper-harvested muskrat carcasses after the pelt was removed. Tissue samples and exudate were tested for presence of or exposure to a suite of pathogens and contaminants. Gastrointestinal tracts were examined for helminths. Intestinal helminths were present in 39.2% of necropsied muskrats, with Hymenolepis spp. (62%) and echinostome spp. (44%) being the most common Molecular testing identified a low prevalence of infection with Clostridium piliforme in the feces and Sarcocystis spp. in the heart. We detected a low seroprevalence to Toxoplasma gondii (1/380). No muskrats were positive for Francisella tularensis or Babesia spp. Cysticercosis was detected in 20% (5/26) of diagnostic cases and 15% (57/380) of our trapper-harvested muskrats. Toxic concentrations of arsenic, cadmium, lead, or mercury were not detected in tested liver samples. Copper, molybdenum, and zinc concentrations were detected at acceptable levels comparative to previous studies. Parasite intensity and abundance were typical of historic reports; however, younger muskrats had higher intensity of infection than older muskrats which is contradictory to what has been previously reported. A diversity of pathogens and contaminants have been reported from muskrats, but the associated disease impacts are poorly understood. Our data are consistent with historic reports and highlight the wide range of parasites, pathogens and contaminants harbored by muskrats in Pennsylvania. The data collected are a critical component in assessing overall muskrat health and serve as a basis for understanding the impacts of disease on recent muskrat population declines.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0260987","usgsCitation":"Ganoe, L., Brown, J., Lovallo, M., Yabsley, M., Garrett, K., Thompson, A., Poppenga, R., Ruder, M., and Walter, W., 2021, Surveillance for diseases, pathogens, and toxicants of muskrat (Ondatra zibethicus) in Pennsylvania and surrounding regions: PLoS ONE, v. 16, no. 12, e0260987, 21 p., https://doi.org/10.1371/journal.pone.0260987.","productDescription":"e0260987, 21 p.","ipdsId":"IP-132458","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467218,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0260987","text":"Publisher Index Page"},{"id":466438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.85632156110431,\n 42.2858130455985\n ],\n [\n -80.85632156110431,\n 39.57662331387951\n ],\n [\n -74.27817658513136,\n 39.57662331387951\n ],\n [\n -74.27817658513136,\n 42.2858130455985\n ],\n [\n -80.85632156110431,\n 42.2858130455985\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-12-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Ganoe, Laken S.","contributorId":348374,"corporation":false,"usgs":false,"family":"Ganoe","given":"Laken S.","affiliations":[{"id":83355,"text":"Pennsylvania Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":923410,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Justin D.","contributorId":348375,"corporation":false,"usgs":false,"family":"Brown","given":"Justin D.","affiliations":[{"id":83356,"text":"Department of Veterinary and Biomedical Sciences","active":true,"usgs":false}],"preferred":false,"id":923411,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lovallo, Matthew J.","contributorId":348376,"corporation":false,"usgs":false,"family":"Lovallo","given":"Matthew J.","affiliations":[{"id":83357,"text":"Bureau of Wildlife Management","active":true,"usgs":false}],"preferred":false,"id":923412,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yabsley, Michael J.","contributorId":348377,"corporation":false,"usgs":false,"family":"Yabsley","given":"Michael J.","affiliations":[{"id":39308,"text":"Southeastern Cooperative Wildlife Disease Study","active":true,"usgs":false}],"preferred":false,"id":923413,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garrett, Kayla B.","contributorId":348378,"corporation":false,"usgs":false,"family":"Garrett","given":"Kayla B.","affiliations":[{"id":81749,"text":"Warnell School of Forestry and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923414,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thompson, Alec T.","contributorId":348379,"corporation":false,"usgs":false,"family":"Thompson","given":"Alec T.","affiliations":[{"id":39308,"text":"Southeastern Cooperative Wildlife Disease Study","active":true,"usgs":false}],"preferred":false,"id":923415,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Poppenga, Robert H.","contributorId":348380,"corporation":false,"usgs":false,"family":"Poppenga","given":"Robert H.","affiliations":[{"id":36526,"text":"California Animal Health and Food Safety Laboratory","active":true,"usgs":false}],"preferred":false,"id":923416,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ruder, Mark G.","contributorId":348381,"corporation":false,"usgs":false,"family":"Ruder","given":"Mark G.","affiliations":[{"id":39308,"text":"Southeastern Cooperative Wildlife Disease Study","active":true,"usgs":false}],"preferred":false,"id":923417,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923409,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70236286,"text":"70236286 - 2021 - Variable effects of wind-energy development on seasonal habitat selection of pronghorn","interactions":[],"lastModifiedDate":"2022-08-31T12:01:28.295502","indexId":"70236286","displayToPublicDate":"2021-12-09T06:59:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Variable effects of wind-energy development on seasonal habitat selection of pronghorn","docAbstract":"In the face of climate change, wind energy represents an important alternative to oil and gas extraction to meet increasing energy demands, but it has the potential to disrupt wildlife populations. Because behavioral adjustments, such as altered habitat selection, are a primary way that long-lived species respond to novel disturbances, we evaluated effects of wind energy development on pronghorn (Antilocapra americana) space use and habitat selection. Using data from GPS-collared female pronghorn in the Shirley Basin of south-central Wyoming, USA, we tested four potential effects of wind turbines on pronghorn space use during the summer and winter: (1) displacement away from wind turbines, (2) increase in size of home ranges, (3) short-term avoidance behavior within home ranges, and (4) changes in avoidance behavior within home ranges over time. We monitored 166 individuals over five summers (2010, 2011, 2018, 2019, and 2020) and 142 individuals over five winters (2009/2010, 2010/2011, 2011/2012, 2018/2019, and 2019/2020) and used resource selection functions to evaluate selection relative to turbines after controlling for other habitat factors, such as snow depth. Although a lack of consistent negative effects of wind turbines on pronghorn across years suggested that wind energy development may have less severe and more intermittent effects on pronghorn than oil and gas development has had on other ungulates, there was a trend toward increased displacement during the study and behavioral avoidance was apparent for individuals in close proximity to turbines. However, pronghorn were highly variable in their fine-scale habitat selection, across both individuals and years, which could make effects of wind energy development difficult to detect. Nevertheless, some individuals, particularly those close to wind-energy facilities, did avoid turbines, which could translate to population-level behavioral or demographic changes over time and affect the resilience and stability of the population. Over time, the accumulation of development, including wind turbines, roads, and fences, can both limit movement and fragment habitat, potentially reaching a critical threshold beyond which populations are negatively impacted.
The U.S. Geological Survey, in cooperation with the Delaware Department of Natural Resources and Environmental Control and the Delaware Geological Survey, conducted a groundwater-quality investigation to (1) describe the occurrence and distribution of PFAS, and (2) document any changes in groundwater quality in the Columbia aquifer public water-supply wells in the Delaware Coastal Plain between 2000 and 2008 and between 2008 and 2018. Thirty public water-supply wells located throughout the Columbia aquifer of the Delaware Coastal Plain were sampled from August through November 2018. Groundwater collected from the wells was analyzed for the occurrence and distribution of 18 per- and polyfluorinated alkyl substances (PFAS) as well as groundwater age. Descriptive statistical analyses were performed to assess PFAS analytical results within the well network and the combined perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) concentrations were compared to the U.S. Environmental Protection Agency’s (EPA) health advisory level (HAL) for informational purposes only and not for evidence of compliance or noncompliance with Federal regulations. The EPA’s HAL is a health-based reference level for public drinking water as supplied to customers and is not applied to source (raw) water. Groundwater-age data were compared for sites sampled in 2000, 2008, and 2018 to document any changes.
All samples were analyzed for 18 PFAS using EPA Method 537 (modified). Forty-four percent of the analyzed PFAS were detected in the study well network. Sixteen of the sampled wells have one or more PFAS detections, and as many as eight different PFAS were found in a single sample. Wells with a higher number of PFAS detected (five or more) were in New Castle and Sussex Counties. The PFAS most frequently detected were PFOA, with 47 percent detection; perfluorohexanoic acid (PFHxA), with 33 percent detection; and PFOS and perfluorohexane sulfonate (PFHxS), with 27 percent detection each. PFAS concentrations were below 1,000 parts per trillion (ppt). Two wells exceeded the EPA’s lifetime-drinking water health advisory level of 70 ppt for combined concentrations of PFOA and PFOS.
The average age of groundwater entering the screens of the supply wells sampled in 2018 ranged from 8.2 to 45.8 years, with a median groundwater age of 25.7 years. Groundwater age was positively correlated with well depth and negatively correlated with dissolved oxygen. Groundwater age and PFAS concentrations were negatively correlated in the Columbia aquifer. Data from the 23 resampled wells indicate a significant positive difference in the average modeled groundwater-sample-age results. The average groundwater age from samples collected in 2018 was generally 5 years older than the average groundwater age from samples collected in 2008. The same pattern was found during cycle two (2008) of this study, where the 2008 groundwater age was on average 7 years older than the samples collected in 2000. The distribution of groundwater sample ages among the 17 trend wells and during the three study cycles (2000, 2008, and 2018) indicates that sample-age medians were statistically different from zero; well-water sample-age data show a slight increase in groundwater sample age.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211109","collaboration":"Prepared in cooperation with the Delaware Geological Survey and Delaware Department of Natural Resources and Environmental Control","usgsCitation":"Reyes, B., 2021, Occurrence and distribution of PFAS in sampled source water of public drinking-water supplies in the surficial aquifer in Delaware, 2018; PFAS and groundwater age-dating results: U.S. Geological Survey Open-File Report 2021–1109, 27 p., https://doi.org/10.3133/ofr20211109.","productDescription":"Report: vii, 27 p.; Data Release; Database","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122437","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":392630,"rank":7,"type":{"id":39,"text":"HTML 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Rangelands have immense inherent spatial and temporal variability, yet land condition and trends are often assessed at a limited number of spatially “representative” points. Spatially comprehensive, and quantitative, Ecological Potential (EP) data provide a baseline for comparison to current rangeland vegetation conditions and trends. Here, we define EP as potential fractional cover (bare ground, herbaceous, litter, shrub, and sagebrush) represented in the least disturbed areas and most productive years of the Landsat satellite archive (1985-present) for each 30-m pixel. We produce EP maps across rangelands in the western United States by training regression tree models using Rangeland Condition Monitoring Assessment and Projection (RCMAP) time-series fractional cover maps in ecologically intact sites (with limited annual herbaceous cover, no recent disturbance or vegetation treatment, and less bare ground cover than expected). As independent predictor variables in these models, we use digital soils and topography data and six bimonthly composites of the 90th percentile of Normalized Difference Vegetation Index (NDVI) and associated spectral bands from the 1985–2020 Landsat archive. EP predictions were successful in capturing biophysical gradients present in the independent variables and depicting potential cover in the absence of disturbance; we found no influence of fires or land treatments in the data. Next, we compared EP to contemporary (2018) cover, to create departure maps that can be used as a screening tool indicating degradation and providing an early warning of vegetation state change. Finally, we used a dichotomous key to convert the 1985 and 2018 RCMAP cover and EP cover into vegetation states important to land management decisions (invaded sagebrush steppe, annual grasslands, etc.). We found that in 1985, 21.2% of the study area had a different vegetation state than EP, and this percentage increased to 24.2% by 2018. More than 50% of the EP native sagebrush steppe was converted to an annual grassland, perennial grassland, or non-sagebrush shrub by 2018, and an additional 7% was classified as invaded sagebrush steppe, at risk of transition to another state. EP products provide a spatio-temporal reference of vegetation conditions from the last three decades across rangelands in the western United States. Use of the EP reference can improve adaptive management practice by providing monitoring and control data, which are often lacking, and assist in differentiating treatment effect from confounding factors.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.108447","usgsCitation":"Rigge, M.B., Meyer, D., and Bunde, B., 2021, Ecological potential fractional component cover based on Long-Term satellite observations across the western United States: Ecological Indicators, v. 133, 108447, 14 p., https://doi.org/10.1016/j.ecolind.2021.108447.","productDescription":"108447, 14 p.","ipdsId":"IP-129599","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":450064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.108447","text":"Publisher Index Page"},{"id":396994,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.755859375,\n 27.839076094777816\n ],\n [\n -103.271484375,\n 33.65120829920497\n ],\n [\n -98.96484375,\n 36.87962060502676\n ],\n [\n -104.853515625,\n 40.17887331434696\n ],\n [\n -105.1171875,\n 41.902277040963696\n ],\n [\n -102.216796875,\n 43.70759350405294\n ],\n [\n -102.3046875,\n 49.03786794532644\n ],\n [\n -122.6953125,\n 49.210420445650286\n ],\n [\n -123.57421875,\n 48.16608541901253\n ],\n [\n -125.595703125,\n 48.22467264956519\n ],\n [\n -124.365234375,\n 44.213709909702054\n ],\n [\n -124.8046875,\n 39.90973623453719\n ],\n [\n -120.58593749999999,\n 34.016241889667015\n ],\n [\n -118.21289062499999,\n 32.84267363195431\n ],\n [\n -116.71874999999999,\n 32.84267363195431\n ],\n [\n -115.09277343749999,\n 32.713355353177555\n ],\n [\n -114.67529296874999,\n 32.76880048488168\n ],\n [\n -114.85107421875,\n 32.52828936482526\n ],\n [\n -111.02783203125,\n 31.297327991404266\n ],\n [\n -108.17138671875,\n 31.353636941500987\n ],\n [\n -108.17138671875,\n 31.765537409484374\n ],\n [\n -106.3916015625,\n 31.74685416292141\n ],\n [\n -104.67773437499999,\n 30.183121842195515\n ],\n [\n -103.11767578124999,\n 28.94086176940557\n ],\n [\n -102.45849609375,\n 29.76437737516313\n ],\n [\n -101.53564453124999,\n 29.76437737516313\n ],\n [\n -99.73388671874999,\n 27.72243591897343\n ],\n [\n -99.755859375,\n 27.839076094777816\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"133","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":837781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, Deb 0000-0002-8841-697X","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":288363,"corporation":false,"usgs":false,"family":"Meyer","given":"Deb","affiliations":[{"id":61730,"text":"Retired, KBR","active":true,"usgs":false}],"preferred":false,"id":837782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bunde, Brett 0000-0003-0228-779X","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":288364,"corporation":false,"usgs":false,"family":"Bunde","given":"Brett","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":837783,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226730,"text":"tm4B6 - 2021 - Historical and paleoflood analyses for probabilistic flood-hazard assessments—Approaches and review guidelines","interactions":[],"lastModifiedDate":"2021-12-08T11:59:24.716062","indexId":"tm4B6","displayToPublicDate":"2021-12-07T14:15:55","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"4-B6","displayTitle":"Historical and Paleoflood Analyses for Probabilistic Flood-Hazard Assessments—Approaches and Review Guidelines","title":"Historical and paleoflood analyses for probabilistic flood-hazard assessments—Approaches and review guidelines","docAbstract":"Paleoflood studies are an effective means of providing specific information on the recurrence and magnitude of rare and large floods. Such information can be combined with systematic flood measurements to better assess the frequency of large floods. Paleoflood data also provide valuable information about the linkages among climate, land use, flood-hazard assessments, and channel morphology. This document summarizes methods and techniques for the preparation, gathering, evaluation, and interpretation of paleoflood information, including uncertainties, especially with respect to new statistical approaches available to efficiently use such data. We summarize best practices and strategies for assessing and mitigating uncertainties and provide guidelines on appropriate technical review of paleoflood analyses based on project goals and requirements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4B6","collaboration":"Prepared in cooperation with the Nuclear Regulatory Commission","usgsCitation":"Harden, T.M., Ryberg, K.R., O’Connor, J.E., Friedman, J.M., and Kiang, J.E., 2021, Historical and paleoflood analyses for probabilistic flood-hazard assessments—Approaches and review guidelines: U.S. Geological Survey Techniques and Methods, book 4, chap. B6, 91 p., https://doi.org/10.3133/tm4B6.","productDescription":"vii, 91 p.","onlineOnly":"Y","ipdsId":"IP-123028","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":392605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/b06/tm4b6.pdf","text":"Report","size":"18.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-B6"},{"id":392604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/b06/coverthb.jpg"}],"contact":"Director, Oregon Water Science Center
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Variation in temperature is known to influence mortality patterns in ectotherms. Even though a few experimental studies on model organisms have reported a positive relationship between temperature and actuarial senescence (i.e., the increase in mortality risk with age), how variation in climate influences the senescence rate across the range of a species is still poorly understood in free-ranging animals. We filled this knowledge gap by investigating the relationships linking senescence rate, adult lifespan, and climatic conditions using long-term capture–recapture data from multiple amphibian populations. We considered two pairs of related anuran species from the Ranidae (Rana luteiventris and Rana temporaria) and Bufonidae (Anaxyrus boreas and Bufo bufo) families, which diverged more than 100 Mya and are broadly distributed in North America and Europe. Senescence rates were positively associated with mean annual temperature in all species. In addition, lifespan was negatively correlated with mean annual temperature in all species except A. boreas. In both R. luteiventris and A. boreas, mean annual precipitation and human environmental footprint both had negligible effects on senescence rates or lifespans. Overall, our findings demonstrate the critical influence of thermal conditions on mortality patterns across anuran species from temperate regions. In the current context of further global temperature increases predicted by Intergovernmental Panel on Climate Change scenarios, a widespread acceleration of aging in amphibians is expected to occur in the decades to come, which might threaten even more seriously the viability of populations and exacerbate global decline.
Hydrologic records used to create previously published maps depicting mean annual runoff are biased to a relatively dry period in Oklahoma history that was dominated by droughts. Therefore, the U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, developed an updated mean annual runoff and annual runoff variability map for Oklahoma and parts of adjacent States. The updated map, which is based on mean-annual-streamflow regression equations developed from available streamgage data through 2007, is assumed to be representative of the long-term mean annual runoff conditions. The map covers all 69 8-digit hydrologic units with at least 1 square mile of area in Oklahoma; those 8-digit hydrologic units contain 2,870 12-digit hydrologic units that provided the geographic framework for the analysis described in this report. Although parts of adjacent States are included in the study area, this report is primarily focused on providing a map of mean annual runoff and annual runoff variability for Oklahoma.
The mean annual runoff increased from less than 0.25 inch per year in the Panhandle of northwestern Oklahoma to more than 30 inches per year in the mountainous terrain of southeastern Oklahoma. The orientation and pattern of mean annual runoff contours in this report were comparable to those of previously published map reports. The annual runoff variability, or the difference between the 80-percent and 20-percent streamflow-duration statistics, increased from less than 0.25 inch per year in the Panhandle of northwestern Oklahoma to more than 40 inches per year in the mountainous terrain of southeastern Oklahoma. The annual runoff variability data were similar in orientation and pattern to the mean annual runoff contours; annual runoff variability generally increased proportionally with increasing mean annual runoff. The annual runoff variability was also greatest, therefore, in the mountainous terrain of southeastern Oklahoma.
The mean annual runoff and annual runoff variability were calculated at sampled points representing the outlets of 12-digit hydrologic units, so the map in this report is most representative of runoff conditions in rural, unregulated drainage basins at the 12-digit hydrologic-unit scale. The map was developed by using regression equations formulated on streamgage data for the entire period of record through 2007, but those equations are biased to the period 1940–2007 when streamgages became more numerous and distributed across Oklahoma. Therefore, the map is likely most representative of runoff conditions during the period 1940–2007. Because runoff is a function of climate variables that can change over time, caution is warranted when using the information in this report to project mean annual runoff and annual runoff variability conditions beyond 2007.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3482","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Smith, S.J., and Sherrod, E.M., 2021, Mean annual runoff and annual runoff variability map for Oklahoma, 1940–2007: U.S. Geological Survey Scientific Investigations Map 3482, 1 sheet, scale 1:100,000, 10-p. pamphlet, https://doi.org/10.3133/sim3482.","productDescription":"Pamphlet: vi, 10 p.; Sheet: 34.00 x 24.00 inches; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-127939","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":392378,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SG5ZDO","text":"USGS data release","linkHelpText":"Data release for mean annual runoff and annual runoff variability map for Oklahoma, 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\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Estuaries, at the nexus of rivers and the ocean, are depositional areas that respond to changes in streamflow, tides, sea level, and inputs of sediment from marine and watershed sources. Understanding changes in bed elevations, deposited and eroded sediment, and water depth throughout estuaries is relevant for understanding their present-day status and long-term evolution, identifying potential hazards to human communities, and informing estuarine conservation. In response to observations of sedimentation in the Nehalem Bay, northwestern Oregon, by the Port of Nehalem, the magnitudes and patterns of bathymetric change in the Bay were documented and described by two approaches. The first approach compared changes in bed elevation with estimated volumes of erosion and deposition from overlapping survey data acquired in 1957 and 2019 for the area of the Nehalem Bay from upstream of the Highway 101 bridge to downstream of Fishery Point. The second approach examined changes in water depth for seven zones from the confluence of the North Fork and Nehalem Rivers to the mouth of the Nehalem River using nautical charts (1891, 1947, 1970, 1990, and 2004). These two approaches were used because the bathymetric surveys from 1957 and 2019 could be tied to a common vertical datum, allowing for a direct comparison of changes in bed elevations, whereas the nautical charts could not be tied to a common vertical datum, which limited the analyses to a comparison of changes in water depths over a broader time frame.
Bed elevation changes from 1957 to 2019 were assessed from upstream of the Highway 101 bridge to downstream of Fishery Point where the two surveys overlapped (2 square kilometers) using thalweg longitudinal profiles, channel cross sections, and digital elevation models (DEMs) showing the elevation differences between the two surveys (or DEMs of difference). The most prominent change between 1957 and 2019 was the migration of the thalweg (or deepest part of the channel) between the downstream end of Lazarus Island and downstream of Fishery Point; this migration resulted in sediment deposition in the former thalweg and sediment erosion in formerly shallow areas to form the new thalweg. Bed elevation changes in the thalweg also varied longitudinally between 1957 and 2019. The bed elevation of the thalweg in both surveys, however, was generally less than 1 meter (m). The thalweg in the area of overlapping surveys shortened from about 7.0 to 6.7 kilometers in length over that same period. The bed elevation changes between the DEMs showed that maximum erosion and deposition was 4.3 and 4.5 m, respectively. In this same time period, the net change in sediment volume was 230,000 cubic meters (m3), indicating net deposition. However, the error estimated for the 95 percent confidence interval analyses is ±315,000 m3, and therefore does not preclude the possibility that net erosion may have occurred.
Historical changes in water depth from soundings depicted on nautical charts from 1891, 1947, 1970, 1990, and 2004 were evaluated by assessing spatial and temporal changes for seven zones of the Nehalem Bay. Across all years and zones, water depths ranged from about 0.2 to 9.4 m, whereas median water depths ranged from 0.3 to 6.4 m. Median depths and the range of water depths did not systematically increase or decrease throughout all zones during the same periods. In all nautical charts, the zone at the mouth of the Nehalem River consistently had the deepest soundings (7.9 to 9.4 m) and the greatest range of water depths (7.3 to 8.8 m). Qualitative evaluation of the nautical charts showed minimal changes in the overall shape of the Nehalem Bay. The exception to this observation was at the mouth of the Bay, where two historical outlets to the Pacific Ocean depicted in the 1891 nautical chart were reduced to one outlet following the construction of jetties (1916 and 1918).
The results of this study emphasize that bed elevations and water depths within the Nehalem Bay have varied between 1891 and 2019, as illustrated by the lateral and vertical changes in the thalweg and changes in water depths over time. Changes in thalweg position and related patterns of sediment erosion and deposition are expected in the future as the Nehalem Bay continues to respond to changes in tides, sea level, streamflow, and sediment inputs from watershed and marine sources. The results of this study and the surveys from 1957 and 2019 provide a foundation for documenting and evaluating future changes in the Nehalem Bay and prioritizing actions to manage and protect natural resources and recreational access to the Nehalem Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215108","collaboration":"Prepared in cooperation with the Port of Nehalem","usgsCitation":"Keith, M.K., Jones, K.L., and Gordon, G.W., 2021, Historical changes in bed elevation and water depth within the Nehalem Bay, Oregon, 1891–2019: U.S. Geological Survey Scientific Investigations Report 2021–5108, 48 p., https://doi.org/10.3133/sir20215108.","productDescription":"Report: x, ; Data Release","onlineOnly":"Y","ipdsId":"IP-115603","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":392536,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VJOGM1","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Digital elevation model of the Nehalem Bay near Wheeler, Oregon 2019"},{"id":392535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5108/sir20215108.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5108"},{"id":392534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5108/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Nehalem Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.95324707031249,\n 45.62172169252446\n ],\n [\n -123.77746582031249,\n 45.62172169252446\n ],\n [\n -123.77746582031249,\n 45.761774855141226\n ],\n [\n -123.95324707031249,\n 45.761774855141226\n ],\n [\n -123.95324707031249,\n 45.62172169252446\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Lake Eucha is a source of water for public supply and recreation for the residents of Tulsa and other municipalities in northeastern Oklahoma. Beaty Creek and Spavinaw Creek flow into Lake Eucha and drain about 388 square miles of agricultural and forested land in northeastern Oklahoma and northwestern Arkansas. Beginning in the 1990s, eutrophication of Lake Eucha characterized by excessive algal blooms resulted in taste and odor problems associated with lake water when it is used for public supply. The predominant sources of phosphorus in the Eucha-Spavinaw drainage area were identified by previous investigators as runoff from fertilized agricultural areas (nonpoint sources) and treated effluent from a wastewater-treatment plant (point source). To further evaluate the transport of nitrogen, phosphorus, and suspended sediment in the Eucha-Spavinaw drainage area, the U.S. Geological Survey (USGS), in collaboration with the City of Tulsa, estimated the loads and computed temporal trends of these constituents from water-quality and streamflow data collected at five USGS streamgages in the Beaty Creek and Spavinaw Creek subbasins.
Estimates and comparisons of total nitrogen, total phosphorus, and suspended-sediment loads from the Beaty Creek and Spavinaw Creek subbasins to Lake Eucha during 2011–18 were made by using different types of regression equations. The first type of regression equation is referred to as “daily mean load regression equations” and was developed from water-quality data obtained from periodic water-quality samples and daily mean streamflow data collected at five USGS streamgages. The second type of regression equation is referred to as “instantaneous continuous load regression equations.” In addition to water-quality data obtained from periodic water-quality samples, continuous real-time (every 15 minutes) measurements of physicochemical properties (specific conductance, water temperature, and turbidity), and continuous streamflow data were used to estimate instantaneous continuous loads of total nitrogen, total phosphorus, and suspended sediment at two of the same five streamgages where daily mean loads were estimated. The use of these two types of regression equations was documented by previous investigators who estimated loads of total nitrogen, total phosphorus, and suspended sediment in the study area by using data collected during 2002–10.
The regression equations used to estimate constituent loads that were based on water-quality data obtained from periodic water-quality samples and continuous water-quality and streamflow data (instantaneous continuous load regression equations) better described the temporal variance in constituent loads compared to the regression equations based only on periodic water-quality data and daily mean streamflows (daily mean load regression equations). Estimates computed using instantaneous continuous load regression equations showed that mean annual loads of 1,844,000 pounds of total nitrogen, 150,300 pounds of total phosphorus, and 78,735,000 pounds of suspended sediment were transported into Lake Eucha from the Beaty Creek and Spavinaw Creek subbasins. Most of the estimated mean annual loads from the Beaty Creek and Spavinaw Creek subbasins entered Lake Eucha during runoff conditions, including about 80 percent of total nitrogen, 95 percent of total phosphorus, and 98 percent of suspended sediment.
Daily, annual, and mean annual load estimates varied substantially, depending on streamflow conditions and the independent variables used to develop the regression equations. Daily and annual loads estimated from instantaneous continuous load regression equations that included specific conductance, water temperature, turbidity, and streamflow described the variability in the field data better than did loads estimated from daily mean load regression equations that included streamflow, seasonality, and time. Loads estimated from the instantaneous continuous load regression equations generally were greater than those estimated from the daily mean load regression equations.
Temporal trends in total nitrogen concentrations showed statistically significant (probability value less than or equal to 0.05) downward trends during both base-flow and runoff conditions at all five USGS streamgages except for the streamgage 07191179 Spavinaw Creek near Cherokee City, Ark. Temporal trends in total phosphorus concentrations were not consistent between streamgages over the study period, showing upward and downward trends throughout the Eucha-Spavinaw drainage area. Total phosphorus concentrations during base-flow and runoff conditions showed statistically significant upward trends at USGS streamgages 07191160 Spavinaw Creek near Maysville, Ark., and 07191222 Beaty Creek near Jay, Okla. Total phosphorus concentrations showed a statistically significant downward trend during base-flow conditions at USGS streamgage 071912213 Spavinaw Creek near Colcord, Okla., and in both base-flow and runoff conditions at USGS streamgage 07191179 Spavinaw Creek near Cherokee City, Ark. Temporal trends in suspended-sediment concentrations were not consistent between streamgages over the study period and were similar to temporal trends in total phosphorus concentrations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215105","collaboration":"Prepared in cooperation with the City of Tulsa, Oklahoma","usgsCitation":"Paizis, N., Becker, C., and Lockmiller, K., 2021, Load estimation and trend analysis for nitrogen, phosphorus, and suspended sediment in the Eucha-Spavinaw drainage area, northeastern Oklahoma and northwestern Arkansas, 2011–18: U.S. Geological Survey Scientific Investigations Report 2021–5105, 57 p., https://doi.org/10.3133/sir20215105.","productDescription":"Report: x, 57 p.; Dataset","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-127112","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":392369,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5105/coverthb.jpg"},{"id":392370,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5105/sir20215105.pdf","size":"2.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5105"},{"id":392371,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5105/images"},{"id":392372,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Arkansas, Oklahoma","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-94.076,36.4991],[-93.9071,36.4983],[-93.8977,36.4983],[-93.8697,36.4982],[-93.8698,36.4324],[-93.8698,36.3871],[-93.8695,36.3758],[-93.8693,36.3467],[-93.8695,36.3073],[-93.8697,36.2918],[-93.8697,36.2705],[-93.8697,36.2669],[-93.87,36.2347],[-93.8883,36.2353],[-93.9893,36.2373],[-94.0024,36.238],[-94.0127,36.2382],[-94.0131,36.2305],[-94.013,36.2083],[-94.021,36.2086],[-94.154,36.2108],[-94.174,36.2114],[-94.1785,36.2113],[-94.2504,36.2127],[-94.279,36.2135],[-94.2819,36.2139],[-94.3349,36.2147],[-94.3352,36.1856],[-94.3367,36.1425],[-94.3561,36.1426],[-94.3891,36.1433],[-94.3889,36.0988],[-94.4071,36.0994],[-94.4242,36.0995],[-94.4447,36.0995],[-94.4624,36.1001],[-94.4801,36.1006],[-94.5274,36.1019],[-94.5433,36.102],[-94.5498,36.1027],[-94.5537,36.1258],[-94.56,36.1623],[-94.583,36.1623],[-94.7959,36.1618],[-95.0114,36.1629],[-95.0119,36.2501],[-95.0039,36.2503],[-95.0072,36.5114],[-95.006,36.6003],[-95.0008,36.6001],[-95.001,36.6723],[-94.6187,36.6694],[-94.6185,36.6004],[-94.6182,36.4984],[-94.4355,36.4997],[-94.3816,36.4996],[-94.1651,36.4996],[-94.076,36.4991]]]},\"properties\":{\"name\":\"Benton\",\"state\":\"AR\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Relatively little research has been conducted to systematically quantify the nationwide earthquake risk of gas pipelines in the US; simultaneously, national guidance is limited for operators across the country to consistently evaluate the earthquake risk of their assets. Furthermore, many challenges and uncertainties exist in a comprehensive seismic risk assessment of gas pipelines. As a first stage in a systematic nationwide assessment, we quantify the earthquake risk of gas transmission pipelines in the conterminous US due to strong ground shaking, including the associated uncertainties. Specifically, we integrate the US Geological Survey 2018 National Seismic Hazard Model, a logic tree–based exposure model, three different vulnerability models, and a consequence model. The results enable comparison against other risk assessment efforts, encourage more transparent deliberation regarding alternative approaches, and facilitate decisions on potentially assessing localized risks due to ground failures that require site-specific data. Based on the uncertainties approximated herein, the resulting sensitivity analyses suggest that the vulnerability model is the most influential source of uncertainty. Finally, we highlight research needs such as (1) developing more vulnerability models for regional seismic risk assessment of gas pipelines; (2) identifying, prioritizing, and measuring input pipeline attributes that are important for estimating seismic damage; and (3) better quantifying seismic hazards with their uncertainties at the national scale, for both ground failures and ground shaking.
A reliable chlorophyll–nutrient relationship (CNR) is essential for lake eutrophication management. Although the spatial variability of CNRs has been extensively explored, temporal variations of CNRs at the individual lake scale has rarely been discussed. The paucity of information about temporal dependence in CNRs may in part be due to the lack of a suitable statistical framework that helps guide such investigations. In order to reveal temporal dependence of CNR, this study develop a novel statistical framework. In the framework, we employ quantile regression to generate overall (the entire dataset), annual (subsets for each year), and accumulative (subsets collected before a certain year) CNRs. We aim to 1) show biases of annual relationships by comparing the overall and annual relationships and 2) determine whether or not data accumulation is enough to develop a reliable CNR. We use Lake Champlain and Lake Kasumigaura as case studies to illustrate the necessary steps needed to utilize this novel framework. Results show that large interannual variations exist for CNRs. Accumulative relationships tend to converge to the overall relationship, indicating that overall relationships are reliable for informing lake-specific eutrophication management in the two case study lakes. The novel statistical framework that we propose for a procedure to estimate reliable CNRs is important for informing lake-specific eutrophication control decision-making processes.
The South Loup River in central Nebraska has been impaired by bacteria since at least 2004, which has resulted in the river not meeting its intended use as a recreational waterway. As part of a strategy for reducing the bacterial load in the river, the U.S. Geological Survey, in cooperation with the Lower Loup Natural Resources District, made continuous estimates of Escherichia coli (E. coli) and nutrient concentrations during seasonal monitoring at the South Loup River at Saint Michael, Nebraska, during 2017–18. Continuous turbidity data were collected from mid-April through October in 2017 and 2018 and were paired with 35 co-occurring discrete water samples that were analyzed for E. coli, nutrients, and suspended solids. Surrogate models relating the discrete concentrations to the continuous turbidity data were developed using ordinary-least-squares regression and were evaluated for model performance and uncertainty. Although the model assumptions were met for E. coli, the imprecision of the E. coli model was considerably higher than the other constituents, probably because of measurement imprecision and greater sensitivity to environmental factors. Once the models were developed, the turbidity data were used to predict continuous constituent concentrations and corresponding prediction intervals, which were made available online as part of the U.S. Geological Survey National Water Information System database. It is expected that results from these models will provide stakeholders with an understanding of constituent concentrations during the 2017–18 monitoring period and the results will also provide a good reference point for any future comparisons.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215120","collaboration":"Prepared in cooperation with the Lower Loup Natural Resources District","usgsCitation":"Rus, D.L., and Densmore, B.K., 2021, Continuous turbidity data used to compute constituent concentrations in the South Loup River, Nebraska, 2017–18: U.S. Geological Survey Scientific Investigations Report 2021–5120, 10 p., https://doi.org/10.3133/sir20215120.","productDescription":"Report: vi, 10 p.; 2 Datasets","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-127801","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":392236,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5120/coverthb.jpg"},{"id":392237,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5120/sir20215120.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5120"},{"id":392238,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://www.waterqualitydata.us/","text":"National Water Quality Monitoring Council website and digital data","linkHelpText":"— Water quality portal"},{"id":392239,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Nebraska","otherGeospatial":"South Loup River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.667724609375,\n 40.93841495689795\n ],\n [\n -98.2177734375,\n 40.93841495689795\n ],\n [\n -98.2177734375,\n 42.02481360781777\n ],\n [\n -100.667724609375,\n 42.02481360781777\n ],\n [\n -100.667724609375,\n 40.93841495689795\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512