The U.S. Geological Survey (USGS) is the primary U.S. Government agency for water data collection and dissemination. In this role, the USGS has recently created and deployed a National Water Census Data Portal (NWC-DP) which provides access to streamflow, evapotransporation, precipitation, aquatic biology and other data at the national level. Recognizing the value of these data sets for hydrologic science education, this paper presents an effort to bridge the gap between pencil–and-paper-based hydrology curriculum and the USGS NWC-DP resource. Specifically, we have developed an R package, National Water Census Education (NWCEd), and five associated laboratory exercises that integrate R- and web-services-based access to the NWC-DP data sets. Using custom functions built into the NWCEd, students are able to access unprecedented amounts of hydrologic data from the NWC-DP, which can be applied to current hydrology curriculum and analyzed using NWCEd and a number of other open-source R tools.
","language":"English","publisher":"BYU","usgsCitation":"Nelson, J., Ames, D.P., and Blodgett, D.L., 2018, Open hydrology courseware using the United States Geological Survey’s National Water Census Data Portal: Open Water Journal, v. 5, no. 1, p. 1-14.","productDescription":"Article 1; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-086854","costCenters":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"links":[{"id":353730,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":353724,"type":{"id":15,"text":"Index Page"},"url":"https://scholarsarchive.byu.edu/openwater/vol5/iss1/1/"}],"volume":"5","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6cfe4b0da30c1bfbe42","contributors":{"authors":[{"text":"Nelson, Jake","contributorId":204467,"corporation":false,"usgs":false,"family":"Nelson","given":"Jake","email":"","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":734059,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ames, Daniel P.","contributorId":204468,"corporation":false,"usgs":false,"family":"Ames","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":734060,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blodgett, David L. 0000-0001-9489-1710 dblodgett@usgs.gov","orcid":"https://orcid.org/0000-0001-9489-1710","contributorId":3868,"corporation":false,"usgs":true,"family":"Blodgett","given":"David","email":"dblodgett@usgs.gov","middleInitial":"L.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":734058,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196718,"text":"70196718 - 2018 - Clusters of community exposure to coastal flooding hazards based on storm and sea level rise scenarios—implications for adaptation networks in the San Francisco Bay region","interactions":[],"lastModifiedDate":"2018-05-21T13:07:34","indexId":"70196718","displayToPublicDate":"2018-04-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3242,"text":"Regional Environmental Change","active":true,"publicationSubtype":{"id":10}},"title":"Clusters of community exposure to coastal flooding hazards based on storm and sea level rise scenarios—implications for adaptation networks in the San Francisco Bay region","docAbstract":"Sea level is projected to rise over the coming decades, further increasing the extent of flooding hazards in coastal communities. Efforts to address potential impacts from climate-driven coastal hazards have called for collaboration among communities to strengthen the application of best practices. However, communities currently lack practical tools for identifying potential partner communities based on similar hazard exposure characteristics. This study uses statistical cluster analysis to identify similarities in community exposure to flooding hazards for a suite of sea level rise and storm scenarios. We demonstrate this approach using 63 jurisdictions in the San Francisco Bay region of California (USA) and compare 21 distinct exposure variables related to residents, employees, and structures for six hazard scenario combinations of sea level rise and storms. Results indicate that cluster analysis can provide an effective mechanism for identifying community groupings. Cluster compositions changed based on the selected societal variables and sea level rise scenarios, suggesting that a community could participate in multiple networks to target specific issues or policy interventions. The proposed clustering approach can serve as a data-driven foundation to help communities identify other communities with similar adaptation challenges and to enhance regional efforts that aim to facilitate adaptation planning and investment prioritization.
","language":"English","publisher":"Springer","doi":"10.1007/s10113-017-1267-5","usgsCitation":"Hummel, M., Wood, N.J., Schweikert, A., Stacey, M., Jones, J., Barnard, P.L., and Erikson, L., 2018, Clusters of community exposure to coastal flooding hazards based on storm and sea level rise scenarios—implications for adaptation networks in the San Francisco Bay region: Regional Environmental Change, v. 18, no. 5, p. 1343-1355, https://doi.org/10.1007/s10113-017-1267-5.","productDescription":"13 p.","startPage":"1343","endPage":"1355","ipdsId":"IP-084260","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":353750,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.61016845703124,\n 37.385435182627226\n ],\n [\n -121.58569335937501,\n 37.385435182627226\n ],\n [\n -121.58569335937501,\n 38.25974980039479\n ],\n [\n -122.61016845703124,\n 38.25974980039479\n ],\n [\n -122.61016845703124,\n 37.385435182627226\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-07","publicationStatus":"PW","scienceBaseUri":"5afee6cde4b0da30c1bfbe34","contributors":{"authors":[{"text":"Hummel, Michelle","contributorId":204476,"corporation":false,"usgs":false,"family":"Hummel","given":"Michelle","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":734104,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":734105,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schweikert, Amy","contributorId":204479,"corporation":false,"usgs":false,"family":"Schweikert","given":"Amy","email":"","affiliations":[],"preferred":false,"id":734106,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stacey, Mark T.","contributorId":13367,"corporation":false,"usgs":true,"family":"Stacey","given":"Mark T.","affiliations":[],"preferred":false,"id":734107,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, Jeanne","contributorId":50444,"corporation":false,"usgs":true,"family":"Jones","given":"Jeanne","affiliations":[],"preferred":false,"id":734108,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":2880,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":734109,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":147149,"corporation":false,"usgs":true,"family":"Erikson","given":"Li H.","email":"lerikson@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":734110,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70195642,"text":"sir20185010 - 2018 - Characterization of water quality in Bushy Park Reservoir, South Carolina, 2013–15","interactions":[],"lastModifiedDate":"2018-04-25T16:19:05","indexId":"sir20185010","displayToPublicDate":"2018-04-25T15:15:00","publicationYear":"2018","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":"2018-5010","title":"Characterization of water quality in Bushy Park Reservoir, South Carolina, 2013–15","docAbstract":"The Bushy Park Reservoir is the principal water supply for 400,000 people in the greater Charleston, South Carolina, area, which includes homes as well as businesses and industries in the Bushy Park Industrial Complex. Charleston Water System and the U.S. Geological Survey conducted a cooperative study during 2013–15 to assess the circulation of Bushy Park Reservoir and its effects on water-quality conditions, specifically, recurring taste-and-odor episodes. This report describes the water-quality data collected for the study that included a combination of discrete water-column sampling at seven locations in the reservoir and longitudinal water-quality profiling surveys of the reservoir and tributaries to characterize the temporal and spatial water-quality dynamics of Bushy Park Reservoir. Water-quality profiling surveys were conducted with an autonomous underwater vehicle equipped with a multiparameter water-quality-sonde bulkhead. Data collected by the autonomous underwater vehicle included water temperature, dissolved oxygen, pH, specific conductance, turbidity, total chlorophyll as fluorescence (estimate of algal biomass), and phycocyanin as fluorescence (estimate of cyanobacteria biomass) data.
Characterization of the water-quality conditions in the reservoir included comparison to established State nutrient guidelines, identification of any spatial and seasonal variation in water-quality conditions and phytoplankton community structures, and assessment of the degree of influence of water-quality conditions related to Foster Creek and Durham Canal inflows, especially during periods of elevated taste-and-odor concentrations. Depth-profile and autonomous underwater vehicle survey data were used to identify areas within the reservoir where greater phytoplankton and cyanobacteria densities were most likely occurring.
Water-quality survey results indicated that Bushy Park Reservoir tended to stratify thermally at a depth of about 20 feet from June to early October. The stratification was limited to the deeper portions of the reservoir near the dam and often dissipated within the reservoir near the CWS intake less than a mile upstream from the dam. Where thermally stratified, a corresponding depletion of dissolved oxygen also occurred at about the same depth and resulted in an anoxic hypolimnion below the 25-foot depth and an increase in specific conductance, likely due to re-mobilized metals and phosphorus under reducing conditions. In general, chlorophyll estimated from fluorescence exhibited some spatial variation, but no strong consistent pattern or “hot spot” was observed. Phycocyanin, estimated from relative fluorescence unit output as blue-green algae cell density, periodically seemed to be greater in the upper portion of the reservoir, but those differences may be attributed to increased turbidity and the potential change in phytoplankton community structure that affects fluorescence. Increased phycocyanin was observed at about the 10-foot depth during the summer months.
A constant production of 2-methylisoborneol (MIB) near the dam and geosmin in the middle and upper portions of the reservoir appears to be occurring during the summer and early fall in the reservoir, but concentrations of these compounds tend to be between 10 and 15 nanograms per liter, which is at the Charleston Water System treatment threshold. At the Bushy Park Reservoir intake, the dominant taste-and-odor compound tended to be MIB, measured at a 2- or 3-to-1 ratio with geosmin during the summer and fall. During springtime episodes, however, when taste-and-odor compound concentrations typically are elevated above the Charleston Water System treatment threshold, the spatial distribution of geosmin concentrations greater than 15 nanograms per liter (28 to 38 nanograms per liter) was best explained by in situ production in the lower portion of the Bushy Park Reservoir near the dam rather than transport from Foster Creek. This pattern seems to indicate a possible shift in phytoplankton communities (or, at least, cyanobacteria communities) from MIB producers to geosmin producers.
The spatial and seasonal assessment of water-quality conditions in Bushy Park Reservoir identified seasonal differences in water chemistry and spatial differences between the upper and lower portions of the reservoir that correspond to the location of elevated geosmin concentrations. On the basis of the spatial and seasonal assessment of actinomycetes concentrations compared to taste-and-odor compound concentrations, cyanobacteria production likely was the dominant source of the taste-and-odor episodes rather than actinomycetes. The lack of spatial and seasonal patterns in actinomycetes concentrations did not correspond to the springtime geosmin concentrations that were elevated above the Charleston Water System treatment threshold in the lower portion of the reservoir. Additionally, actinomycetes concentrations, although ubiquitous, had a median of about 9 and maximum of about 20 colonies per milliliter, which can be considered low for elevated taste-and-odor compound production. Nonetheless, the potential exists for actinomycetes to be a secondary source of taste-and-odor production and could explain some of the ubiquitous occurrence of low-level taste-and-odor production, such as MIB concentrations, observed throughout the summer and early fall months.
When evaluated by biovolume, cyanobacteria were not the dominant phytoplankton group in Bushy Park Reservoir during the study period. Dolichospermum planctonicum (previously Anabaena planktonica ) was the dominant genera of the cyanobacteria group during spring periods. The geosmin-producing genera that were identified in the 2014 and 2015 spring communities in Bushy Park Reservoir were not observed in the 1999 and 2000 algal taxonomic data.
A more robust examination of phytoplankton species was conducted by using a multivariate analysis that identified seasonal changes in phytoplankton community structure. These seasonal phytoplankton communities appeared to be explained by seasonal changes in water chemistry and may be responsible for episodes of taste-and-odor occurrence, especially geosmin. The most probable source of geosmin identified during the study was D. planctonicum.
In a synoptic sampling event during a taste-and-odor episode in April 2015, cyanobacteria, not acinomycetes, also was indicated to be the more prevalent source of the geosmin. Although the Edisto River intake and its associated supply tunnel to the treatment facility had relatively high actinomycetes concentrations (130 and 140 colonies per milliliter, respectively) compared to the Bushy Park intake and tunnel (2 colonies per milliliter), corresponding geosmin concentrations were below 5 nanograms per liter for source water from the Edisto River intake and tunnel. Elevated geosmin concentrations above the Charleston Water System treatment threshold were identified in source waters from the Bushy Park Reservoir. The cyanobacteria community at the sampled sites in April 2015 was statistically similar to the community in the Bushy Park Reservoir in April 2014, when geosmin concentrations also were elevated. The only geosmin-producing genus identified at the Bushy Park intake, however, was D. planctonicum.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185010","collaboration":"Prepared in cooperation with Charleston Water System","usgsCitation":"Conrads, P.A., Journey, C.A., Petkewich, M.D., Lanier, T.H., and Clark, J.M., 2018, Characterization of water quality in Bushy Park Reservoir, South Carolina, 2013–15: U.S. Geological Survey Scientific Investigations Report 2018–5010, 175 p., https://doi.org/10.3133/sir20185010. ","productDescription":"xi, 175 p.","numberOfPages":"192","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087952","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":437934,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z7YOXC","text":"USGS data release","linkHelpText":"Phycocyanin, Velocity, AUV, and Profile Data for William Station Shutdown in Bushy Park Reservoir, South Carolina, 2017"},{"id":353632,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5010/sir20185010.pdf","text":"Report","size":"27.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5010"},{"id":353631,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5010/coverthb.jpg"},{"id":353639,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NG4NVX","text":"USGS data release","description":"USGS data release","linkHelpText":"Water Quality Data for Bushy Park Reservoir, South Carolina 2013–2015"}],"country":"United States","state":"South Carolina","otherGeospatial":"Bushy Park Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.364990234375,\n 32.816132537537115\n ],\n [\n -79.43939208984375,\n 32.816132537537115\n ],\n [\n -79.43939208984375,\n 33.51162942617925\n ],\n [\n -80.364990234375,\n 33.51162942617925\n ],\n [\n -80.364990234375,\n 32.816132537537115\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Altantic Water Science Center
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Conservation and management decision making in natural resources is challenging due to numerous uncertainties and unknowns, especially relating to understanding system dynamics. Adaptive resource management (ARM) is a formal process to making logical and transparent recurrent decisions when there are uncertainties about system dynamics. Despite wide recognition and calls for implementing adaptive natural resource management, applications remain limited. More common is a reactive approach to decision making, which ignores future system dynamics. This contrasts with ARM, which anticipates future dynamics of ecological process and management actions using a model-based framework. Practitioners may be reluctant to adopt ARM because of the dearth of comparative evaluations between ARM and more common approaches to making decisions. We compared the probability of meeting management objectives when managing a population under both types of decision frameworks, specifically in relation to typical uncertainties and unknowns. We use a population of Sandhill Cranes as our case study. We evaluate each decision process under varying levels of monitoring and ecological uncertainty, where the true underlying population dynamics followed a stochastic age-structured population model with environmentally driven vital rate density dependence. We found that the ARM framework outperformed the currently employed reactive decision framework to manage Sandhill Cranes in meeting the population objective across an array of scenarios. This was even the case when the candidate set of population models contained only naïve representations of the true population process. Under the reactive decision framework, we found little improvement in meeting the population objective even if monitoring uncertainty was eliminated. In contrast, if the population was monitored without error within the ARM framework, the population objective was always maintained, regardless of the population models considered. Contrary to expectation, we found that age-specific optimal harvest decisions are not always necessary to meet a population objective when population dynamics are age structured. Population managers can decrease risks and gain transparency and flexibility in management by adopting an ARM framework. If population monitoring data has high sampling variation and/or limited empirical knowledge is available for constructing mechanistic population models, ARM model sets should consider a range of mechanistic, descriptive, and predictive model types.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1734","usgsCitation":"Gerber, B.D., and Kendall, W.L., 2018, Adaptive management of animal populations with significant unknowns and uncertainties: A case study: Ecological Applications, v. 28, no. 5, p. 1325-1341, https://doi.org/10.1002/eap.1734.","productDescription":"17 p.","startPage":"1325","endPage":"1341","ipdsId":"IP-073120","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489030,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/nrs_facpubs/84","text":"External Repository"},{"id":394876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gerber, Brian D.","contributorId":187620,"corporation":false,"usgs":false,"family":"Gerber","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":831785,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":831704,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196508,"text":"ofr20181060 - 2018 - USA National Phenology Network observational data documentation","interactions":[],"lastModifiedDate":"2018-04-25T16:12:08","indexId":"ofr20181060","displayToPublicDate":"2018-04-25T00:00:00","publicationYear":"2018","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":"2018-1060","title":"USA National Phenology Network observational data documentation","docAbstract":"The goals of the USA National Phenology Network (USA-NPN, www.usanpn.org) are to advance science, inform decisions, and communicate and connect with the public regarding phenology and species’ responses to environmental variation and climate change. The USA-NPN seeks to advance the science of phenology and facilitate ecosystem stewardship by providing phenological information freely and openly. To accomplish these goals, the USA-NPN National Coordinating Office (NCO) delivers observational data on plant and animal phenology in several formats, including minimally processed status and intensity datasets and derived phenometrics for individual plants, sites, and regions. This document describes the suite of observational data products delivered by the USA National Phenology Network, covering the period 2009–present for the United States and accessible via the Phenology Observation Portal (http://dx.doi.org/10.5066/F78S4N1V) and via an Application Programming Interface. The data described here have been used in diverse research and management applications, including over 30 publications in fields such as remote sensing, plant evolution, and resource management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181060","usgsCitation":"Rosemartin, A., Denny, E.G., Gerst, K.L., Marsh, R.L., Posthumus, E.E., Crimmins, T.M., and Weltzin, J.F., 2018, USA National Phenology Network observational data documentation: U.S. Geological Survey Open-File Report 2018–1060, 24 p., https://doi.org/10.3133/ofr20181060.","productDescription":"Report: vi, 24 p.; Appendix tables","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-085279","costCenters":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"links":[{"id":353663,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1060/coverthb.jpg"},{"id":353665,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1060/ofr20181060_appendix1tables.zip","text":"Appendix 1 Tables","size":"67 KB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2018-1080 Appendix 1"},{"id":353664,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1060/ofr20181060.pdf","text":"Report","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1080"}],"contact":"Director,
Ecosystems Mission Area
U.S. Geological Survey
12201 Sunrise Valley Dr., MS 300
Reston, VA 20192
Data from a long-term capture-recapture program were used to assess the status and dynamics of populations of two long-lived, federally endangered catostomids in Upper Klamath Lake, Oregon. Lost River suckers (LRS; Deltistes luxatus) and shortnose suckers (SNS; Chasmistes brevirostris) have been captured and tagged with passive integrated transponder (PIT) tags during their spawning migrations in each year since 1995. In addition, beginning in 2005, individuals that had been previously PIT-tagged were re-encountered on remote underwater antennas deployed throughout sucker spawning areas. Captures and remote encounters during the spawning season in spring 2016 were incorporated into capture-recapture analyses of population dynamics.
Cormack-Jolly-Seber (CJS) open population capture-recapture models were used to estimate annual survival probabilities, and a reverse-time analog of the CJS model was used to estimate recruitment of new individuals into the spawning populations. In addition, data on the size composition of captured fish were examined to provide corroborating evidence of recruitment. Model estimates of survival and recruitment were used to derive estimates of changes in population size over time and to determine the status of the populations through 2015. Separate analyses were done for each species and also for each subpopulation of LRS. Shortnose suckers and one subpopulation of LRS migrate into tributary rivers to spawn, whereas the other LRS subpopulation spawns at groundwater upwelling areas along the eastern shoreline of the lake.
Capture-recapture analyses indicated that with a few exceptions, the survival of males and females in both Lost River sucker subpopulations was high (greater than 0.88) from 1999 to 2015. Survival was notably lower for males from the river in 2000, 2006, and 2012, and for the shoreline areas in 2002. From 2001 to 2015, the abundance of males in the lakeshore spawning subpopulation decreased by at least 64 percent and the abundance of females decreased by at least 56 percent. Capture-recapture models suggested that the abundance of both sexes in the river spawning subpopulation of LRS had increased substantially since 2006; increases were mostly due to large estimated recruitment events in 2006 and 2008. We know that the estimates in 2006 are substantially biased in favor of recruitment because of a sampling issue. We are skeptical of the magnitude of recruitment indicated by the 2008 estimates as well because (1) few small individuals that would indicate the presence of new recruits were captured in that year, and (2) recapture probabilities in recruitment models based on just physical recaptures of fish were lower than desired for robust inferences from capture-recapture models. If we assume instead that little or no recruitment occurred for this subpopulation, the abundance of both sexes in the river spawning subpopulation likely has decreased at rates similar to the rates for the lakeshore spawning subpopulation from 2002 to 2015.
Shortnose suckers experienced lower and more variable annual survival than either LRS subpopulation. Annual survival of both sexes was relatively low in 2003, 2004, 2010, and 2012. In addition, female survival was low in 1999 and 2000 while male survival was low in 2002. Survival estimate precision in early years of the study; however, are poor. Capture-recapture models and size composition data indicate that recruitment of new individuals into the SNS spawning population was trivial from 2001 to 2005. Models indicate that more than 10 percent of the population was new recruits in a number of more recent years. As a result, capture-recapture modeling suggests that the abundance of adult spawning SNS was relatively stable from 2006 to 2010. We are skeptical of the estimated recruitment in 2006 because of the known sampling issue. We also are skeptical of the estimated recruitment in other recent years because few small individuals that would indicate the presence of new recruits were captured in any of those years, and recapture probabilities in recruitment models were low. The best-case scenario for SNS, based on capture-recapture recruitment modeling, indicates that the abundance of males in the spawning population decreased by 78 percent and the abundance of females decreased by 77 percent from 2001 to 2015. Decreases in abundance for both sexes are likely greater than these estimates indicate.
Despite relatively high survival in most years, we conclude that both species have experienced substantial decreases in the abundance of spawning adults because losses from mortality have not been balanced by recruitment of new individuals. Although capture-recapture data indicate substantial recruitment of new individuals into the spawning populations for SNS and river spawning LRS in some years, size data do not corroborate these estimates. As a result, the status of the endangered sucker populations in Upper Klamath Lake remains distressed, especially for SNS. Our monitoring program provides a robust platform for estimating vital population parameters, evaluating the status of the populations, and assessing the effectiveness of conservation and recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181064","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hewitt, D.A., Janney, E.C., Hayes, B.S., and Harris, A.C., 2018, Status and trends of adult Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) sucker populations in Upper Klamath Lake, Oregon, 2017: U.S. Geological Survey Open-File Report 2018-1064, 31 p., https://doi.org/10.3133/ofr20181064.","productDescription":"iv, 31 p.","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-096959","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":437938,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97K04NO","text":"USGS data release","linkHelpText":"Status and trends of adult Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) sucker populations in Upper Klamath Lake, Oregon, 2023"},{"id":353417,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1064/coverthb.jpg"},{"id":353418,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1064/ofr20181064.pdf","text":"Report","size":"905 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1064"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.10067749023438,\n 42.21936476344714\n ],\n [\n -121.79992675781249,\n 42.21936476344714\n ],\n [\n -121.79992675781249,\n 42.61981257367216\n ],\n [\n -122.10067749023438,\n 42.61981257367216\n ],\n [\n -122.10067749023438,\n 42.21936476344714\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
The Oregon Department of Transportation (ODOT) and other state departments of transportation need quantitative information about the percentages of different land cover categories above any given stream crossing in the state to assess and address roadway contributions to water-quality impairments and resulting total maximum daily loads. The U.S. Geological Survey, in cooperation with ODOT and the FHWA, added roadway and land cover information to the online StreamStats application to facilitate analysis of stormwater runoff contributions from different land covers. Analysis of 25 delineated basins with drainage areas of about 100 mi2 indicates the diversity of land covers in the Willamette Valley, Oregon. On average, agricultural, developed, and undeveloped land covers comprise 15%, 2.3%, and 82% of these basin areas. On average, these basins contained about 10 mi of state highways and 222 mi of non-state roads. The Stochastic Empirical Loading and Dilution Model was used with available water-quality data to simulate long-term yields of total phosphorus from highways, non-highway roadways, and agricultural, developed, and undeveloped areas. These yields were applied to land cover areas obtained from StreamStats for the Willamette River above Wilsonville, Oregon. This analysis indicated that highway yields were larger than yields from other land covers because highway runoff concentrations were higher than other land covers and the highway is fully impervious. However, the total highway area was a fraction of the other land covers. Accordingly, highway runoff mitigation measures can be effective for managing water quality locally, they may have limited effect on achieving basin-wide stormwater reduction goals.
The Gunnison Basin Selenium Management Program implemented a water-quality monitoring network in 2011 in the lower Gunnison River Basin in Colorado. Selenium is a trace element that bioaccumulates in aquatic food chains and can cause reproductive failure, deformities, and other harmful effects. This report presents the percentile values of selenium because regulatory agencies in Colorado make decisions based on the U.S. Environmental Protection Agency (EPA) Clean Water Act Section 303(d) that uses percentile values of concentration. Also presented are dissolved-selenium loads at 18 sites in the lower Gunnison River Basin for water years (WYs) 2011–2016 (October 1, 2010, through September 30, 2016). Annual dissolved-selenium loads were calculated for five sites with continuous U.S. Geological Survey (USGS) streamflow-gaging stations. Annual dissolved-selenium loads for WY 2011 through WY 2016 ranged from 179 and 391 pounds (lb) at Uncompahgre River at Colona to 11,100 and 17,300 lb at Gunnison River near Grand Junction (herein called Whitewater), respectively.
Instantaneous loads were calculated for five sites with continuous U.S. Geological Survey (USGS) streamflow-gaging stations and 13 ancillary sites where discrete water-quality sampling also took place, using discrete water-quality samples and the associated discharge measurements collected during the period. Median instantaneous loads ranged from 0.01 pound per day (lb/d) at Smith Fork near Lazear to 33.0 lb/d at Whitewater. Mean instantaneous loads ranged from 0.06 lb/d at Smith Fork near Lazear to 36.2 lb/d at Whitewater. Most tributary sites in the basin had a median instantaneous dissolved-selenium load of less than 20.0 lb/day. In general, dissolved-selenium loads at Gunnison River main-stem sites showed an increase from upstream to downstream.
The State of Colorado water-quality standard for dissolved selenium of 4.6 micrograms per liter (µg/L) was compared to the 85th percentiles for dissolved selenium at selected sites. Annual 85th percentiles for dissolved selenium were calculated for the five core sites having USGS streamflow-gaging stations using estimated dissolved-selenium concentrations from linear regression models. The 85th-percentile concentrations for WYs 2011–2016 based on this method ranged from 0.62 µg/L and 1.1µg/L at Uncompahgre River at Colona to 12.1 µg/L and 18.7 µg/L at Uncompahgre River at Delta.
The 85th percentiles for dissolved selenium also were calculated for sites with sufficient data using water-quality samples collected during WYs 2011–2016. The annual 85th-percentile concentrations based on the discrete samples ranged from 0.16 µg/L and 0.17 µg/L at Gunnison River below Gunnison Tunnel to 62.2 µg/L and 170 µg/L at Loutzenhizer Arroyo at North River Road.
A trend analysis was completed for Whitewater to determine if dissolved-selenium loads are increasing or decreasing. The trend analysis indicates a decrease of 9,100 lb from WY 1986 to WY 2016, a 40.8 percent reduction during the time period. The trend analysis for the annual dissolved-selenium load for WY 1994 to WY 2016 indicates a decrease of 6,300 lb per year, or 33.3 percent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185001","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Henneberg, M.F., 2018, Assessment of dissolved-selenium concentrations and loads in the lower Gunnison River Basin, Colorado, as part of the Selenium Management Program, from 2011 to 2016: U.S. Geological Survey Scientific Investigations Report 2018–5001, 23 p., https://doi.org/10.3133/ofr20185001.","productDescription":"v, 23 p.","numberOfPages":"33","onlineOnly":"Y","ipdsId":"IP-090932","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":353466,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5001/coverthb.jpg"},{"id":353467,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5001/sir20185001.pdf","text":"Report","size":"2.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5001"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.5,\n 38\n ],\n [\n -107.25,\n 38\n ],\n [\n -107.25,\n 39.25\n ],\n [\n -108.5,\n 39.25\n ],\n [\n -108.5,\n 38\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
The city of Goma and its surrounding villages (Democratic Republic of the Congo, DRC) are among the world’s most densely populated regions strongly affected by volcanic hazards. In 2002, Nyiragongo volcano erupted destroying 10–15% of Goma and forced a mass evacuation of the population. Hence, the ~ 1.5 million inhabitants of Goma and Gisenyi (Rwanda) continue to live with the threat of new lava flows and other eruptive hazards from this volcano. The current network of fractures extends from Nyiragongo summit to Goma and continues beneath Lake Kivu, which gives rise to the fear that an eruption could even produce an active vent within the center of Goma or within the lake. A sub-lacustrine volcanic eruption with vents in the floor of the main basin and/or Kabuno Bay of Lake Kivu could potentially release about 300 km3 of carbon dioxide (CO2) and 60 km3 of methane (CH4) dissolved in its deep waters that would be catastrophic to populations (~ 2.5 million people) along the lake shores. For the time being, ongoing hazards related to Nyiragongo and Nyamulagira volcanoes silently kill people and animals, slowly destroy the environment, and seriously harm the health of the population. They include mazuku (CO2-rich locations where people often die of asphyxiation), the highly fluoridated surface and ground waters, and other locally neglected hazards. The volcanic gas plume causes poor air quality and acid rain, which is commonly used for drinking water. Given the large number of people at risk and the continued movement of people to Goma and the surrounding villages, there is an urgent need for a thorough natural hazards assessment in the region. This paper presents a general view of natural hazards in the region around Goma based on field investigations, CO2 measurements in mazuku, and chemistry data for Lake Kivu, rivers and rainwater. The field investigations and the datasets are used in conjunction with extremely rich-historical (1897–2000) and recently published information about Nyiragongo and Nyamulagira volcanoes and Lake Kivu. We also present maps of mazuku and fractures in Goma, describe the volcanic eruption history with hazard assessment and mitigation implications, and consider social realities useful for an integrated risk management strategy.
","language":"English","publisher":"Springer","doi":"10.1007/s11069-018-3288-x","usgsCitation":"Balagizi, C.M., Kies, A., Kasereka, M.M., Tedesco, D., Yalire, M.M., and McCausland, W.A., 2018, Natural hazards in Goma and the surrounding villages, East African Rift System: Natural Hazards, v. 93, no. 1, p. 31-66, https://doi.org/10.1007/s11069-018-3288-x.","productDescription":"36 p.","startPage":"31","endPage":"66","ipdsId":"IP-061455","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":353654,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Democratic Republic of the Congo","city":"Goma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 20.1708984375,\n -5.353521355337321\n ],\n [\n 31.3330078125,\n -5.353521355337321\n ],\n [\n 31.3330078125,\n 6.18424616128059\n ],\n [\n 20.1708984375,\n 6.18424616128059\n ],\n [\n 20.1708984375,\n -5.353521355337321\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"93","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-31","publicationStatus":"PW","scienceBaseUri":"5afee6d3e4b0da30c1bfbe74","contributors":{"authors":[{"text":"Balagizi, Charles M.","contributorId":204381,"corporation":false,"usgs":false,"family":"Balagizi","given":"Charles","email":"","middleInitial":"M.","affiliations":[{"id":36925,"text":"Goma Volcano Observatory","active":true,"usgs":false}],"preferred":false,"id":733837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kies, Antoine","contributorId":204383,"corporation":false,"usgs":false,"family":"Kies","given":"Antoine","email":"","affiliations":[{"id":36926,"text":"University of Luxembourg","active":true,"usgs":false}],"preferred":false,"id":733839,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kasereka, Marcellin M.","contributorId":204382,"corporation":false,"usgs":false,"family":"Kasereka","given":"Marcellin","email":"","middleInitial":"M.","affiliations":[{"id":36925,"text":"Goma Volcano Observatory","active":true,"usgs":false}],"preferred":false,"id":733838,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tedesco, Dario","contributorId":204384,"corporation":false,"usgs":false,"family":"Tedesco","given":"Dario","email":"","affiliations":[{"id":36927,"text":"Second University of Naples","active":true,"usgs":false}],"preferred":false,"id":733840,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yalire, Mathieu M.","contributorId":204385,"corporation":false,"usgs":false,"family":"Yalire","given":"Mathieu","email":"","middleInitial":"M.","affiliations":[{"id":36925,"text":"Goma Volcano Observatory","active":true,"usgs":false}],"preferred":false,"id":733841,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCausland, Wendy A. 0000-0002-8683-1440","orcid":"https://orcid.org/0000-0002-8683-1440","contributorId":204380,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","email":"","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":733836,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196637,"text":"ofr20181037 - 2018 - Leveraging geodetic data to reduce losses from earthquakes","interactions":[],"lastModifiedDate":"2018-04-24T13:34:26","indexId":"ofr20181037","displayToPublicDate":"2018-04-23T00:00:00","publicationYear":"2018","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":"2018-1037","title":"Leveraging geodetic data to reduce losses from earthquakes","docAbstract":"Seismic hazard assessments that are based on a variety of data and the best available science, coupled with rapid synthesis of real-time information from continuous monitoring networks to guide post-earthquake response, form a solid foundation for effective earthquake loss reduction. With this in mind, the Earthquake Hazards Program (EHP) of the U.S. Geological Survey (USGS) Natural Hazards Mission Area (NHMA) engages in a variety of undertakings, both established and emergent, in order to provide high quality products that enable stakeholders to take action in advance of and in response to earthquakes. Examples include the National Seismic Hazard Model (NSHM), development of tools for improved situational awareness such as earthquake early warning (EEW) and operational earthquake forecasting (OEF), research about induced seismicity, and new efforts to advance comprehensive subduction zone science and monitoring. Geodetic observations provide unique and complementary information directly relevant to advancing many aspects of these efforts (fig. 1). EHP scientists have long leveraged geodetic data for a range of influential studies, and they continue to develop innovative observation and analysis methods that push the boundaries of the field of geodesy as applied to natural hazards research. Given the ongoing, rapid improvement in availability, variety, and precision of geodetic measurements, considering ways to fully utilize this observational resource for earthquake loss reduction is timely and essential. This report presents strategies, and the underlying scientific rationale, by which the EHP could achieve the following outcomes:
A geodesy program that encompasses a balanced mix of activities to sustain missioncritical capabilities, grows new competencies through the continuum of fundamental to applied research, and ensures sufficient resources for these endeavors provides a foundation by which the EHP can be a leader in the application of geodesy to earthquake science. With this in mind the following objectives provide a framework to guide EHP efforts:
Maintaining a vibrant internal research program provides the foundation by which the EHP can remain an effective and trusted source for earthquake science. Exploiting abundant new data sources, evaluating and assimilating the latest science, and pursuing novel avenues of investigation are means to fulfilling the EHP’s core responsibilities and realizing the important scientific advances envisioned by its scientists. Central to the success of such a research program is engaging personnel with a breadth of competencies and a willingness and ability to adapt these to the program’s evolving priorities, enabling current staff to expand their skills and responsibilities, and planning holistically to meet shared workforce needs.
In parallel, collaboration with external partners to support scientific investigations that complement ongoing internal research enables the EHP to strengthen earthquake information products by incorporating alternative perspectives and approaches and to study topics and geographic regions that cannot be adequately covered internally.
With commensurate support from technical staff who possess diverse skills, including engineering, information technology, and proficiency in quantitative analysis combined with basic geophysical knowledge, the EHP can achieve the geodetic outcomes identified in this document.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181037","usgsCitation":"Murray, J.R., Roeloffs, E.A., Brooks, B.A., Langbein, J., Leith, W., Minson, S.E., Svarc, J., and Thatcher, W., 2018, Leveraging geodetic data to reduce losses from earthquakes: U.S. Geological Survey Open-File Report 2018–1037, 34 p., https://doi.org/10.3133/ofr20181037.","productDescription":"vi, 34 p.","numberOfPages":"43","onlineOnly":"Y","ipdsId":"IP-089859","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":353651,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1037/coverthb.jpg"},{"id":353652,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1037/ofr20181037.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1037"}],"contact":"Contact Information, Menlo Park, Calif.
Office—Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
https://earthquake.usgs.gov/
In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within the 3-nautical-mile limit of California’s State Waters. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data, acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow subsurface geology.
The map area is in the southern part of the Western Transverse Ranges geologic province, which is north of the California Continental Borderland. Significant clockwise rotation—at least 90°—since the early Miocene has been proposed for the Western Transverse Ranges province, and the region is presently undergoing north-south shortening. The offshore part of the map area lies south of the steep south flank of the Santa Ynez Mountains. The crest of the range, which has a maximum elevation of about 760 m in the map area, lies about 4 km north of the shoreline.
Gaviota is an unincorporated community that has a sparse population (less than 100), and the coastal zone is largely open space that is locally used for cattle grazing. The Union Pacific railroad tracks extend westward along the coast through the entire map area, within a few hundred meters of the shoreline. Highway 101 crosses the eastern part of the map area, also along the coast, then turns north (inland) and travels through Cañada de la Gaviota and Gaviota Pass en route to Buellton. Gaviota State Park lies at the mouth of Cañada de la Gaviota. West of Gaviota, the onland coastal zone is occupied by the Hollister Ranch, a privately owned, gated community that has no public access.
The map area has a long history of petroleum exploration and development. Several offshore gas fields were discovered and were developed by onshore directional drilling in the 1950s and 1960s. Three offshore petroleum platforms were installed in adjacent federal waters in 1976 (platform “Honda”) and 1989 (platforms “Heritage” and “Harmony”). Local offshore and onshore operations were serviced for more than a century by the Gaviota marine terminal, which is currently being decommissioned and will be abandoned in an intended transition to public open space.
The Offshore of Gaviota map area lies within the western Santa Barbara Channel region of the Southern California Bight, and it is somewhat protected from large Pacific swells from the north and northwest by Point Conception and from south and southwest swells by offshore islands and banks. Much of the shoreline in the map area is characterized by narrow beaches that have thin sediment cover, backed by low (10- to 20-m-high) cliffs that are capped by a narrow coastal terrace. Beaches are subject to wave erosion during winter storms, followed by gradual sediment recovery or accretion in the late spring, summer, and fall months during the gentler wave climate.
The map area lies in the western-central part of the Santa Barbara littoral cell, which is characterized by west-to-east transport of sediment from Point Arguello on the northwest to Hueneme and Mugu Canyons on the southeast. Sediment supply to the western and central part of the littoral cell is mainly from relatively small coastal watersheds. In the map area, sediment sources include Cañada de la Gaviota (52 km2), as well as Cañada de la Llegua, Arroyo el Bulito, Cañada de Santa Anita, Cañada de Alegria, Cañada del Agua Caliente, Cañada del Barro, Cañada del Leon, Cañada San Onofre, and many others. Coastal-watershed discharge and sediment load are highly variable, characterized by brief large events during major winter storms and long periods of low (or no) flow and minimal sediment load between storms. In recent (recorded) history, the majority of high-discharge, high-sediment-flux events have been associated with El Niño phases of the El Niño–Southern Oscillation climatic pattern.
Shelf width in the Offshore of Gaviota map area ranges from about 4.3 to 4.7 km, and shelf slopes average about 1.0° to 1.2° but are highly variable because of the presence of the large Gaviota sediment bar. This bar extends southwestward for about 9 km from the mouth of Cañada de la Gaviota to the shelf break, is as wide as 2 km, and is by far the largest shore-attached sediment bar in the Santa Barbara Channel. The shelf is underlain by bedrock and variable amounts (0 to as much as 36 m in the Gaviota bar) of upper Quaternary sediments deposited as sea level fluctuated in the late Pleistocene. The trend of the shelf break changes from about 276° to 236° azimuth over a distance of about 12 km, and it ranges in depth from about 91 m to as shallow as 62 to 73 m where significant shelf-break and upper-slope failure and landsliding has apparently occurred. The shelf break in the western part of the map area is notably embayed by the heads of three large (150- to 300-m-wide) channels that have been referred to as “the Gaviota Canyons” or as “Drake Canyon,” “Sacate Canyon,” and “Alegria Canyon.”
Seafloor habitats in the broad Santa Barbara Channel region consist of significant amounts of soft, unconsolidated sediment interspersed with isolated areas of rocky habitat that support kelp-forest communities in the nearshore and rocky-reef communities in deeper water. The potential marine benthic habitat types mapped in the Offshore of Gaviota map area are directly related to its Quaternary geologic history, geomorphology, and active sedimentary processes. These potential habitats lie primarily within the Shelf (continental shelf) but also partly within the Flank (basin flank or continental slope) megahabitats. The fairly homogeneous seafloor of sediment and low-relief bedrock provides characteristic habitat for rockfish, groundfish, crabs, shrimp, and other marine benthic organisms. Several areas of smooth sediment form nearshore terraces that have relatively steep, smooth fronts, which may be attractive to groundfish. Below the steep shelf break, soft, unconsolidated sediment is interrupted by the heads of several submarine canyons and rills, some bedrock exposures, and small carbonate mounds associated with asphalt mounds and pockmarks, also good potential habitat for rockfish. The map area includes the relatively small (5.2 km2) Kashtayit State Marine Conservation Area, which largely occupies the inner part of the Gaviota sediment bar.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181023","usgsCitation":"Johnson, S.Y., Dartnell, P., Cochrane, G.R., Hartwell, S.R., Golden, N.E., Kvitek, R.G., and Davenport, C.W. (S.Y. Johnson and S.A. Cochran, eds.), 2018, California State Waters Map Series— Offshore of Gaviota, California: U.S. Geological Survey Open-File Report 2018–1023, pamphlet 41 p., 9 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20181023.","productDescription":"Pamphlet: iv, 41 p.; 9 Sheets: 52.0 x 36.0 inches or smaller; Dataset; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-082722","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":437941,"rank":23,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TH8JWJ","text":"USGS data release","linkHelpText":"California State Waters Map Series Data Catalog--Offshore of Gaviota, California"},{"id":399119,"rank":22,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_107161.htm"},{"id":353556,"rank":21,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2018/1023/ofr20181023_metadata.html","text":"Metadata","description":"OFR 2018-1023 Metadata"},{"id":353516,"rank":20,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1023/ofr20181023_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1023 Pamphlet"},{"id":353515,"rank":19,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7TH8JWJ","text":"Data Catalog","linkFileType":{"id":5,"text":"html"},"description":"OFR 2018-1023 Data Catalog","linkHelpText":"The GIS data layers for this map are accessible from “California State Waters Map Series—Offshore of Gaviota, California” which is part of California State Waters Map Series Data Catalog. Each GIS data file is listed with a brief description, a small image, and links to the metadata files and the downloadable data files."},{"id":353514,"rank":18,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/sim/3225/","text":"Scientific Investigations Map 3225","description":"Scientific Investigations Map 3225","linkHelpText":"California State Waters Map Series—Hueneme Canyon and Vicinity, California, by Sam Y. Johnson and others."},{"id":353513,"rank":17,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/sim/3254/","text":"Scientific Investigations Map 3254","description":"Scientific Investigations Map 3254","linkHelpText":"California State Waters Map Series—Offshore of Ventura, California, by Sam Y. 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Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
http://walrus.wr.usgs.gov/
In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within the 3-nautical-mile limit of California’s State Waters. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data, acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow subsurface geology.
The Offshore of Point Conception map area is in the westernmost part of the Western Transverse Ranges geologic province, which is north of the California Continental Borderland. Significant clockwise rotation—at least 90°—since the early Miocene has been proposed for the Western Transverse Ranges province, and this region is presently undergoing north-south shortening. The offshore part of the map area lies south of the steep south and west flanks of the Santa Ynez Mountains. The crest of the range, which has a maximum elevation of about 340 m in the map area, lies about 5 km north and east of the arcuate shoreline.
The onland part of the coastal zone is remote and sparsely populated. The road to Jalama Beach County Park provides the only public coastal access in the entire map area. North of this county park, the coastal zone is part of Vandenberg Air Force Base. South of Jalama Beach County Park, most of the coastal zone is part of the Cojo-Jalama Ranch, purchased by the Nature Conservancy in December 2017. A relatively small part of the coastal zone in the eastern part of the map area lies within the privately owned Hollister Ranch. The nearest significant commercial centers are Lompoc (population, about 42,000), about 10 km north of the map area, and Goleta (population, about 30,000), about 50 km east of the map area. The Union Pacific railroad tracks run west and northwest along the coast through the entire map area, within a few hundred meters of the shoreline. The map area has a long history of petroleum exploration, and the seafloor notably includes large asphalt mounds and pockmarks that result from petroleum seepage. Several offshore gas and oil fields were discovered, and some were developed, in and on the margin of California’s State Waters.
Much of the shoreline in the Offshore of Point Conception map area is characterized by narrow beaches that have thin sediment cover above bedrock platforms, backed by low (10- to 20-m-high) cliffs that are capped by a coastal terrace. Beaches are subject to wave erosion during winter storms, followed by gradual sediment recovery or accretion in the late spring, summer, and fall months during the gentler wave climate. The map area lies in the west-central part of the Santa Barbara littoral cell, which is characterized by west-to-east transport of sediment from Point Arguello on the northwest to Hueneme and Mugu Canyons on the southeast. Sediment supply to the map area is mainly from relatively small coastal watersheds, including the Jalama Creek–Espada Creek drainage basin (about 63 km2), as well as Cañada del Jolloru, Black Canyon, Wood Canyon, Cañada del Cojo, and Barranca Honda. Coastal-watershed discharge and sediment load are highly variable, characterized by brief large events during major winter storms and long periods of low (or no) flow and minimal sediment load between storms. In recent (recorded) history, the majority of high-discharge, high-sediment-flux events have been associated with El Niño phases of the El Niño–Southern Oscillation climatic pattern.
Following the coastline, the shelf bends to the north and northwest around Point Conception, and the trend of the shelf break changes from about 298° to 241° azimuth. Shelf width ranges from about 5 km south of Point Conception to about 11 km northwest of it; the slope ranges from about 1.0° to 1.2° to about 0.7° south and northwest of Point Conception, respectively. Southwest of Point Conception, the shelf break and upper slope are incised by a 600-m-wide, 20- to 30-m-deep, south-facing trough, one of five heads of the informally named Arguello submarine canyon.
The map area is located at a major biogeographic transition zone between the east-west-trending Santa Barbara Channel region of the Southern California Bight and the northwest-trending central California coast. North of Point Conception, the coast is subjected to high wave exposure from the north, west, and south, as well as consistently strong upwelling that brings cold, nutrient-rich waters to the surface. Southeast of Point Conception, the Santa Barbara Channel is largely protected from strong north swells by Point Conception and from south swells by the Channel Islands; surface waters are warmer, and upwelling is weak and seasonal.
Seafloor habitats in the broad Santa Barbara Channel region consist of significant amounts of soft, unconsolidated sediment interspersed with isolated areas of rocky habitat that support kelp-forest communities in the nearshore and rocky-reef communities in deeper water. The potential marine benthic habitat types mapped in the Offshore of Point Conception map area are directly related to its Quaternary geologic history, geomorphology, and active sedimentary processes. These potential habitats lie primarily within the Shelf (continental shelf) but also partly within the Flank (basin flank or continental slope) megahabitats. The fairly homogeneous seafloor of sediment and low-relief bedrock provides characteristic habitat for rockfish, groundfish, crabs, shrimp, and other marine benthic organisms. Several areas of smooth sediment form nearshore terraces that have relatively steep, smooth fronts, which are attractive to groundfish. Below the steep shelf break, soft, unconsolidated sediment is interrupted by the heads of several submarine canyons, gullies, and rills, also good potential habitat for rockfish. The map area includes the large (58.3 km2) Point Conception State Marine Reserve.
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Cochran, eds.), 2018, California State Waters Map Series— Offshore of Point Conception, California: U.S. Geological Survey Open-File Report 2018–1024, pamphlet 36 p., 9 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20181024.","productDescription":"Pamphlet: iv, 36 p.; 9 Sheets: 55.0 x 36.0 inches or smaller; Dataset; Metadata","additionalOnlineFiles":"Y","ipdsId":"IP-082855","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":437940,"rank":23,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QN64XQ","text":"USGS data release","linkHelpText":"California State Waters Map Series Data Catalog--Offshore of Point Conception, California"},{"id":353525,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2018/1024/ofr20181024_sheet8.pdf","text":"Sheet 8","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1024 Sheet 8","linkHelpText":"Local (Offshore of Point Conception Map Area) and Regional (Offshore from Point Conception to Hueneme Canyon) Shallow-Subsurface Geology and Structure, Santa Barbara Channel, California By Samuel Y. 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Kvitek"},{"id":353517,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1024/coverthb.jpg"}],"scale":"24000","country":"United States","state":"California","otherGeospatial":"Point Conception","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.5636,\n 34.3917\n ],\n [\n -120.3717,\n 34.3917\n ],\n [\n -120.3717,\n 34.5422\n ],\n [\n -120.5636,\n 34.5422\n ],\n [\n -120.5636,\n 34.3917\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
http://walrus.wr.usgs.gov/
The bedrock geologic map of the Miles Pond and Concord quadrangles covers an area of approximately 107 square miles (276 square kilometers) in east-central Vermont and adjacent New Hampshire, north of and along the Connecticut River. This map was created as part of a larger effort to produce a new bedrock geologic map of Vermont through the collection of field data at a scale of 1:24,000. The majority of the map area consists of the Bronson Hill anticlinorium, a post-Early Devonian structure that is cored by metamorphosed Cambrian to Silurian sedimentary, volcanic, and plutonic rocks. A major feature on the map is the Monroe fault, interpreted to be a west-directed, steeply dipping Late Devonian (Acadian) thrust fault. To the west of the Monroe fault, rocks of the Connecticut Valley-Gaspé trough dominate and consist primarily of metamorphosed Silurian and Devonian sedimentary rocks. To the north, the Victory pluton intrudes the Bronson Hill anticlinorium. The Bronson Hill anticlinorium consists of the metamorphosed Albee Formation, the Ammonoosuc Volcanics, the Comerford Intrusive Complex, the Highlandcroft Granodiorite, and the Joselin Turn tonalite. The Albee Formation is an interlayered, feldspathic metasandstone and pelite that is locally sulfidic. Much of the deformed metasandstone is tectonically pinstriped. In places, one can see compositional layering that was transposed by a steeply southeast-dipping foliation. The Ammonoosuc Volcanics are lithologically complex and predominantly include interlayered and interfingered rhyolitic to basaltic volcanic and volcaniclastic rocks, as well as lesser amounts of siltstone, phyllite, graywacke, and grit. The Comerford Intrusive Complex crops out east of the Monroe fault and consists of metamorphosed gabbro, diorite, tonalite, aplitic tonalite, and crosscutting diabase dikes. Abundant mafic dikes from the Comerford Intrusive Complex intruded the Albee Formation and Ammonoosuc Volcanics east of the Monroe fault. The Highlandcroft Granodiorite and Joslin Turn tonalite plutons intruded during the Middle to Late Ordovician.
West of the Monroe fault, the Connecticut Valley-Gaspé trough consists of the Silurian and Devonian Waits River and Gile Mountain Formations. The Waits River Formation is a carbonaceous muscovite-biotite-quartz (±garnet) phyllite containing abundant beds of micaceous quartz-rich limestone. The Gile Mountain Formation consists of interlayered metasandstone and graphitic (and commonly sulfidic) slate, along with minor calcareous metasandstone and ironstone. Graded bedding is common in the Gile Mountain Formation. Rocks of the Devonian New Hampshire Plutonic Suite intruded as plutons, dikes, and sills. The largest of these is the Victory pluton, which consists of weakly foliated, biotite granite and granodiorite. The Victory pluton also intruded a large part of the Albee Formation to the north.
This report consists of a geologic map and an online geographic information systems database that includes contacts of bedrock geologic units, faults, outcrops, and structural geologic information. The geologic map is intended to serve as a foundation for applying geologic information to problems involving land use decisions, groundwater availability and quality, earth resources such as natural aggregate for construction, assessment of natural hazards, and engineering and environmental studies for waste disposal sites and construction projects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181025","collaboration":"Prepared in cooperation with the State of Vermont, Vermont Agency of Natural Resources, Vermont Geological Survey, and the State of New Hampshire, Department of Environmental Services, New Hampshire Geological Survey","usgsCitation":"Rankin, D.W., 2018, Bedrock geologic map of the Miles Pond and Concord quadrangles, Essex and Caledonia Counties, Vermont, and Grafton County, New Hampshire: U.S. Geological Survey Open-File Report 2018–1025, 1 sheet, scale 1:24,000, https://doi.org/10.3133/ofr20181025.","productDescription":"1 Sheet: 34.47 x 40.58 inches; Databases; Metadata; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081110","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":353546,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2018/1025/metadata/ofr20181025_milespond-concordnh-geologicmap.mxd","text":"Geologic Map (ArcGIS 10.5)","linkHelpText":"- Miles Pond and Concord, Vermont, and New Hampshire, Geologic Map"},{"id":353538,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2018/1025/ofr20181025_concord-miles-pond-geologicmap10.pdf","text":"Geologic Map","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1025"},{"id":399116,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_107158.htm"},{"id":353547,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2018/1025/metadata/ofr20181025_milespond-concordnh-metadata.zip","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Miles Pond and Concord, Vermont, and New Hampshire, Metadata"},{"id":353545,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2018/1025/metadata/ofr20181025_milespond-concordnh.gdb.zip","text":"Database","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Miles Pond and Concord, Vermont, and New Hampshire, Geodatabase"},{"id":353544,"rank":4,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2018/1025/metadata/ofr20181025_milespond-concordnh-basemap.zip","text":"Base Map","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Miles Pond and Concord, Vermont, and New Hampshire, Base Map"},{"id":353537,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1025/coverthb3.jpg"}],"scale":"24000","country":"United States","state":"New Hampshire, Vermont","county":"Caledonia County, Essex County, Grafton County","otherGeospatial":"Miles Pond and Concord quadrangles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72,\n 44.375\n ],\n [\n -71.75,\n 44.375\n ],\n [\n -71.75,\n 44.5\n ],\n [\n -72,\n 44.5\n ],\n [\n -72,\n 44.375\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Eastern Geology and Paleoclimate
Science Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
The bedrock geologic map of the Lisbon quadrangle, and parts of the Sugar Hill and East Haverhill quadrangles, Grafton County, New Hampshire, covers an area of approximately 73 square miles (189 square kilometers) in west-central New Hampshire. This map was created as part of a larger effort to produce a new bedrock geologic map of Vermont through the collection of field data at a scale of 1:24,000. A large part of the map area consists of the Bronson Hill anticlinorium, a post-Early Devonian structure that is cored by metamorphosed Cambrian to Devonian sedimentary, volcanic, and plutonic rocks.
The Bronson Hill anticlinorium is the apex of the Middle Ordovician to earliest-Silurian Bronson Hill magmatic arc that contains the Ammonoosuc Volcanics, Partridge Formation, and Oliverian Plutonic Suite, and extends from Maine, through western New Hampshire (down the eastern side of the Connecticut River), through southern New England to Long Island Sound. The deformed and partially eroded arc is locally overlain by a relatively thin Silurian section of metasedimentary rocks (Clough Quartzite and Fitch Formation) that thickens to the east. The Silurian section near Littleton is disconformably overlain by a thicker, Lower Devonian section that includes mostly metasedimentary and minor metavolcanic rocks of the Littleton Formation. The Bronson Hill anticlinorium is bisected by a series of northeast-southwest trending Mesozoic normal faults. Primarily among them is the steeply northwest-dipping Ammonoosuc fault that divides older and younger units (lower and upper sections) of the Ammonoosuc Volcanics. The Ammonoosuc Volcanics are lithologically complex and predominantly include interlayered and interfingered rhyolitic to basaltic volcanic and volcaniclastic rocks, as well as lesser amounts of slate, phyllite, ironstone, chert, sandstone, and pelite. The Albee Formation underlies the Ammonoosuc Volcanics and is predominantly composed of interbedded metamorphosed sandstone, siltstone, and phyllite.
During the Late Ordovician, a series of arc-related plutons intruded the Ammonoosuc Volcanics including the Moody Ledge pluton and the Scrag granite of Billings (1937). Subsequent plutonism related to the Acadian orogeny occurred after volcanism and deposition resulted in the Littleton Formation during the Late Devonian, including the intrusion of the Haverhill pluton and French Pond Granite found in the southern part of the map.
This report consists of a geologic map and an online geographic information systems database that includes contacts of bedrock geologic units, faults, outcrops, and structural geologic information. The geologic map is intended to serve as a foundation for applying geologic information to problems involving land use decisions, groundwater availability and quality, earth resources such as natural aggregate for construction, assessment of natural hazards, and engineering and environmental studies for waste disposal sites and construction projects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181016","collaboration":"Prepared in cooperation with the State of Vermont, Vermont Agency of Natural Resources, Vermont Geological Survey, and the State of New Hampshire, Department of Environmental Services, New Hampshire Geological Survey","usgsCitation":"Rankin, D.W., 2018, Bedrock geologic map of the Lisbon quadrangle, and parts of the Sugar Hill and East Haverhill quadrangles, Grafton County, New Hampshire: U.S. Geological Survey Open-File Report 2018–1016, 1 sheet, scale 1:24,000, https://doi.org/10.3133/ofr20181016.","productDescription":"1 Sheet: 34.66 x 37.08 inches; Databases; Metadata; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-082431","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":399117,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_107159.htm"},{"id":353552,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2018/1016/metadata/ofr20181016_lisbonnh-metadata.zip","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Lisbon, New Hampshire, Metadata"},{"id":353550,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2018/1016/metadata/ofr20181016_lisbonnh.gdb.zip","text":"Database","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Lisbon, New Hampshire, Geodatabase"},{"id":353549,"rank":4,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/of/2018/1016/metadata/ofr20181016_lisbonnh-basemap.zip","text":"Base Map","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Lisbon, New Hampshire, Base Map"},{"id":353551,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2018/1016/metadata/ofr20181016_lisbonnh-geologicmap.mxd","text":"Geologic Map (ArcGIS 10.5)","linkHelpText":"- Lisbon, New Hampshire, Geologic Map"},{"id":353540,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2018/1016/ofr20181016_lisbon-geologic-map.pdf","text":"Geologic Map","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1016"},{"id":353539,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1016/coverthb2.jpg"}],"scale":"24000","country":"United States","state":"New Hampshire","county":"Grafton County","otherGeospatial":"Lisbon quadrangle, Sugar Hill and East Haverhill quadrangles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72,\n 44\n ],\n [\n -71.75,\n 44\n ],\n [\n -71.75,\n 44.25\n ],\n [\n -72,\n 44.25\n ],\n [\n -72,\n 44\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Eastern Geology and Paleoclimate
Science Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
The collection of high-frequency (in other words, “continuous”) water data has been made easier over the years because of advances in technologies to measure, transmit, store, and query large, temporally dense datasets. Commercially available, in-situ sensors and data-collection platforms—together with new techniques for data analysis—provide an opportunity to monitor water quantity and quality at time scales during which meaningful changes occur. The U.S. Geological Survey (USGS) Continuous Monitoring Workshop was held to build stronger collaboration within the Water Mission Area on the collection, interpretation, and application of continuous monitoring data; share technical approaches for the collection and management of continuous data that improves consistency and efficiency across the USGS; and explore techniques and tools for the interpretation of continuous monitoring data, which increases the value to cooperators and the public. The workshop was organized into three major themes: Collecting Continuous Data, Understanding and Using Continuous Data, and Observing and Delivering Continuous Data in the Future. Presentations each day covered a variety of related topics, with a special session at the end of each day designed to bring discussion and problem solving to the forefront.
The workshop brought together more than 70 USGS scientists and managers from across the Water Mission Area and Water Science Centers. Tools to manage, assure, control quality, and explore large streams of continuous water data are being developed by the USGS and other organizations and will be critical to making full use of these high-frequency data for research and monitoring. Disseminating continuous monitoring data and findings relevant to critical cooperator and societal issues is central to advancing the USGS networks and mission. Several important outcomes emerged from the presentations and breakout sessions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181059","usgsCitation":"Sullivan, D.J., Joiner, J.K., Caslow, K.A., Landers, M.N., Pellerin, B.A., Rasmussen, P.P., and Sheets, R.A., 2018, U.S. Geological Survey continuous monitoring workshop—Workshop summary report: U.S. Geological Survey Open-File Report 2018–1059, 29 p., https://doi.org/10.3133/ofr20181059.","productDescription":"iv, 29 p.","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092143","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":353586,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1059/coverthb.jpg"},{"id":353587,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1059/ofr20181059.pdf","text":"Report","size":"1.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1059"}],"contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
The largest populations of federally endangered Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) exist in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California. Upper Klamath Lake populations are decreasing because adult mortality, which is relatively low, is not being balanced by recruitment of young adult suckers into known spawning aggregations. Most Upper Klamath Lake juvenile sucker mortality appears to occur within the first year of life. Annual production of juvenile suckers in Clear Lake Reservoir appears to be highly variable and may not occur at all in very dry years. However, juvenile sucker survival is much higher in Clear Lake, with non-trivial numbers of suckers surviving to join spawning aggregations. Long-term monitoring of juvenile sucker populations is needed to (1) determine if there are annual and species-specific differences in production, survival, and growth, (2) to identify the season (summer or winter) in which most mortality occurs, and (3) to help identify potential causes of high juvenile sucker mortality, particularly in Upper Klamath Lake.
We initiated an annual juvenile sucker monitoring program in 2015 to track cohorts in 3 months (June, August, and September) annually in Upper Klamath Lake and Clear Lake Reservoir. We tracked annual variability in age-0 sucker apparent production, juvenile sucker apparent survival, and apparent growth. Using genetic markers, we were able to classify suckers as one of three taxa: shortnose or Klamath largescale suckers, Lost River, or suckers with genetic markers of both species (Intermediate Prob[LRS]). Using catch data, we generated taxa-specific indices of year class strength, August–September apparent survival, and overwinter apparent survival. We also examined prevalence and severity of afflictions such as parasites, wounds, and deformities.
Indices of year class strength in Upper Klamath Lake were similar for shortnose suckers in 2015 and 2016, but about twice as high for Lost River suckers and suckers having intermediate Prob[LRS] in 2016 than in 2015. Indices of apparent August–September survival were lower in 2016 (0.41) than in 2015 (1.07) for shortnose suckers and suckers identified as having intermediate Prob [LRS] (0.14 in 2016 and 1.69 in 2015). Indices of apparent August—September survival were similar in 2016 (0.16) and 2015 (0.07) for Lost River suckers. Indices of apparent survival were lower for age-0 Lost River suckers than age-0 shortnose suckers in both years. Although samples sizes are small, a declining trend in the ratio of Lost River to shortnose suckers from 28/23 (1.22) as age-0 fish in September of 2015 to 1/9 (0.11) as age-1 fish in June of 2016 is consistent with higher over winter apparent mortality for Lost River suckers than shortnose suckers in Upper Klamath Lake.
Shortnose sucker year class strength was greater in years with high Willow Creek inflows and Clear Lake surface elevation during the spawning season, indicating that access to spawning habitat was an important contributing factor. In previous sampling, age-0 sucker catch per unit effort (CPUE) was relatively high in 2011 and 2012, moderately high in 2013, and zero in 2014 and 2015. The 2011 and 2012 year classes continued to be detected, but the 2013 year class went undetected for the first time in 2016. The 2014 year class continued to be undetected in 2016. Three suckers with one annulus each on fin rays were captured in Clear Lake in 2016. Although these fish are potential representatives of the 2015 year class, they were small for their age, indicating they may have hatched in 2016. Age-0 shortnose and Lost River suckers were captured in Clear Lake in 2016, indicating new cohorts of both taxa were produced. Moderate to abundant year classes were produced in 2011, 2012, and 2016 when lake surface elevation greater than 1,378.9 m (4,524 ft) during the February–June spawning season. Also in 2011 and 2016, rapid increases in lake-surface elevation indicated potentially high Willow Creek inflows. A somewhat less abundant year class produced in 2012 than in 2011 and 2016 was associated with lower spawning season inflows. The apparently smaller 2013 year class was formed when Willow Creek inflows were apparently low and lake surface never exceeded 1,379.2 m (4,524.9 ft). In 2014 and 2015, when year-classes were small or not detected, the Clear Lake surface elevations were at or below 1,378.2 m (4,522 ft), and there was very little spring time Willow Creek inflow.
Age-0 shortnose sucker CPUE in Clear Lake was correlated with seasonal decreases in water volumes in 2016 and could not be used to create indices of August–September survival. Age-0 shortnose sucker catch rates in Clear Lake Reservoir were about seven times less in August than in September. Meanwhile, the water volume in Clear Lake Reservoir declined by about 36 percent between these two sampling periods. Higher September catch rates may have resulted from additional age-0 suckers entering the lake from the river, a concentrating effect of declining water volumes, or both.
Differences in August standard length, apparent growth rates, and the prevalence of abnormalities were consistent with healthier age-0 suckers in Clear Lake Reservoir than in Upper Klamath Lake. Age-0 suckers were larger in August in Clear Lake Reservoir than in Upper Klamath Lake, which may be due to an earlier hatch date, faster growth, or both in Clear Lake Reservoir. Sample sizes were only large enough to compare growth rates of age-0 shortnose suckers from Upper Klamath Lake in 2015 to Clear Lake Reservoir in 2016. Age-0 shortnose suckers grew more between August and September in Clear Lake Reservoir in 2016 than in Upper Klamath Lake in 2015. Petechial hemorrhages of the skin on age-0 suckers were more prevalent in Upper Klamath Lake than in Clear Lake Reservoir in 2016. Deformed opercula, black-spot forming parasites, and infections presumed to be Columnaris sp. were observed on less than 12 percent of suckers from Upper Klamath Lake but were not observed on suckers from Clear Lake Reservoir in 2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181066","usgsCitation":"Burdick, S.M., Ostberg, C.O., and Hoy, M.S., 2018, Juvenile Lost River and shortnose sucker year class strength, survival, and growth in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2016 Monitoring Report: U.S. Geological Survey Open-File Report 2018–1066, 43 p., https://doi.org/10.3133/ofr20181066.","productDescription":"vi, 43 p.","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-094193","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":353625,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1066/coverthb.jpg"},{"id":353626,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1066/ofr20181066.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1066"}],"country":"United States","state":"California, Oregon","county":"Klamath County, Modoc County","otherGeospatial":"Clear Lake Reservoir, Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.11509704589845,\n 42.222924262739824\n ],\n [\n -121.75186157226561,\n 42.222924262739824\n ],\n [\n -121.75186157226561,\n 42.61829672418602\n ],\n [\n -122.11509704589845,\n 42.61829672418602\n ],\n [\n -122.11509704589845,\n 42.222924262739824\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.23687744140624,\n 41.781552998900345\n ],\n [\n -121.04736328125,\n 41.781552998900345\n ],\n [\n -121.04736328125,\n 41.94365947797709\n ],\n [\n -121.23687744140624,\n 41.94365947797709\n ],\n [\n -121.23687744140624,\n 41.781552998900345\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
We surveyed for Least Bell’s Vireos (LBVI) (Vireo bellii pusillus) and Southwestern Willow Flycatchers (SWFL) (Empidonax traillii extimus) along the San Luis Rey River, between College Boulevard in Oceanside and Interstate 15 in Fallbrook, California (middle San Luis Rey River), in 2017. Surveys were conducted from April 13 to July 11 (LBVI) and from May 16 to July 28 (SWFL). We found 146 LBVI territories, at least 107 of which were occupied by pairs. Five additional transient LBVIs were detected. LBVIs used five different habitat types in the survey area: mixed willow, willow-cottonwood, willow-sycamore, riparian scrub, and upland scrub. Forty-four percent of the LBVIs occurred in habitat characterized as mixed willow and 89 percent of the LBVI territories occurred in areas with greater than 50 percent native plant cover. Of 16 banded LBVIs detected in the survey area, 8 had been given full color-band combinations prior to 2017. Four other LBVIs with single (natal) federal bands were recaptured and banded in 2017. Three LBVIs with single dark blue federal bands indicating that they were banded as nestlings on the lower San Luis Rey River and one LBVI with a single gold federal band indicating that it was banded as a nestling on Marine Corps Base Camp Pendleton (MCBCP) could not be recaptured for identification. One banded LBVI emigrated from the middle San Luis Rey River to the lower San Luis Rey River in 2017.
One resident SWFL territory and one transient Willow Flycatcher of unknown subspecies (WIFL) were observed in the survey area in 2017. The resident SWFL territory, which was comprised of mixed willow habitat (5–50 percent native plant cover), was occupied by a single male from May 22 to June 21, 2017. No evidence of pairing or nesting activity was observed. The SWFL male was banded with a full color-combination indicating that he was originally banded as a nestling on the middle San Luis Rey River in 2014 and successfully bred in the survey area in 2016. The male SWFL left the middle San Luis Rey River after June 21, 2017 and subsequently was detected on the San Dieguito River on June 26, 2017, by USGS biologists. The transient WIFL was detected on May 30, 2017, in mixed willow habitat comprised of 50–95 percent of native plant cover.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1082","usgsCitation":"Allen, L.D., Howell, S.L., and Kus, B.E., 2018, Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) on the Middle San Luis Rey River, San Diego County, southern California—2017 data summary: U.S. Geological Survey Data Series 1082, 12 p., https://doi.org/10.3133/ds1082.","productDescription":"iv, 12 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-094720","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":353635,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1082/ds1082.pdf","text":"Report","size":"7.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1082"},{"id":353634,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1082/coverthb.jpg"}],"country":"United States","state":"California","county":"San Diego County","otherGeospatial":"Middle San Luis Rey River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.30016708374022,\n 33.24859375258743\n ],\n [\n -117.15854644775389,\n 33.24859375258743\n ],\n [\n -117.15854644775389,\n 33.32421729380816\n ],\n [\n -117.30016708374022,\n 33.32421729380816\n ],\n [\n -117.30016708374022,\n 33.24859375258743\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Preserving remaining nonhybridized populations Cutthroat Trout Oncorhynchus clarkii is a conservation priority often requiring management action. Although proactive programs for Rainbow Trout O. mykiss and hybrid suppression offer a flexible tool, particularly in large interconnected river basins, this management approach is used less frequently than alternatives such as barriers and piscicides. We describe the results of a targeted Rainbow Trout hybrid suppression program spanning 15 years in the upper Snake River, Wyoming, a core stronghold for Yellowstone Cutthroat Trout O. clarkii bouvieri. Initially, Rainbow Trout hybrids were relatively common in the Gros Ventre River, a major tributary to the Snake River. Between 2002 and 2016, 926 individuals of Rainbow Trout ancestry were removed from the Gros Ventre River. Relative abundance of Rainbow Trout hybrids decreased over this time, while the Yellowstone Cutthroat Trout population increased. Temporal genetic data collected in 2007–2008 and again in 2014 demonstrate that the overall proportion Rainbow Trout admixture and the proportion of hybrids in a sample both significantly decreased in the Gros Ventre River and did not increase elsewhere in the Snake River basin. In conclusion, proactive Rainbow Trout suppression appears to have reduced the threat of Rainbow Trout hybridization in this river basin and helped protect an interconnected metapopulation that has a highly diverse life history and genetic variation important for long-term persistence.
Environmental DNA (eDNA) detection is a technique used to non-invasively detect cryptic, low density, or logistically difficult-to-study species, such as imperiled manatees. For eDNA measurement, genetic material shed into the environment is concentrated from water samples and analyzed for the presence of target species. Cytochrome bquantitative PCR and droplet digital PCR eDNA assays were developed for the 3 Vulnerable manatee species: African, Amazonian, and both subspecies of the West Indian (Florida and Antillean) manatee. Environmental DNA assays can help to delineate manatee habitat ranges, high use areas, and seasonal population changes. To validate the assay, water was analyzed from Florida’s east coast containing a high-density manatee population and produced 31564 DNA molecules l-1on average and high occurrence (ψ) and detection (p) estimates (ψ = 0.84 [0.40-0.99]; p = 0.99 [0.95-1.00]; limit of detection 3 copies µl-1). Similar occupancy estimates were produced in the Florida Panhandle (ψ = 0.79 [0.54-0.97]) and Cuba (ψ = 0.89 [0.54-1.00]), while occupancy estimates in Cameroon were lower (ψ = 0.49 [0.09-0.95]). The eDNA-derived detection estimates were higher than those generated using aerial survey data on the west coast of Florida and may be effective for population monitoring. Subsequent eDNA studies could be particularly useful in locations where manatees are (1) difficult to identify visually (e.g. the Amazon River and Africa), (2) are present in patchy distributions or are on the verge of extinction (e.g. Jamaica, Haiti), and (3) where repatriation efforts are proposed (e.g. Brazil, Guadeloupe). Extension of these eDNA techniques could be applied to other imperiled marine mammal populations such as African and Asian dugongs.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr00880","usgsCitation":"Hunter, M., Meigs-Friend, G., Ferrante, J.A., Takoukam Kamla, A., Dorazio, R., Keith Diagne, L., Luna, F., Lanyon, J.M., and Reid, J.P., 2018, Surveys of environmental DNA (eDNA): a new approach to estimate occurrence in Vulnerable manatee populations: Endangered Species Research, v. 35, p. 101-111, https://doi.org/10.3354/esr00880.","productDescription":"12 p.","startPage":"101","endPage":"111","ipdsId":"IP-087696","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468820,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr00880","text":"Publisher Index Page"},{"id":353606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6d9e4b0da30c1bfbe8e","contributors":{"authors":[{"text":"Hunter, Margaret 0000-0002-4760-9302 mhunter@usgs.gov","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":140627,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","email":"mhunter@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":733728,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meigs-Friend, Gaia 0000-0001-5181-7510 gmeigs-friend@usgs.gov","orcid":"https://orcid.org/0000-0001-5181-7510","contributorId":4688,"corporation":false,"usgs":true,"family":"Meigs-Friend","given":"Gaia","email":"gmeigs-friend@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":733729,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferrante, Jason A. 0000-0003-3453-4636 jferrante@usgs.gov","orcid":"https://orcid.org/0000-0003-3453-4636","contributorId":201638,"corporation":false,"usgs":true,"family":"Ferrante","given":"Jason","email":"jferrante@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":733730,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Takoukam Kamla, Aristide","contributorId":204221,"corporation":false,"usgs":false,"family":"Takoukam Kamla","given":"Aristide","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":733731,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dorazio, Robert 0000-0003-2663-0468 bob_dorazio@usgs.gov","orcid":"https://orcid.org/0000-0003-2663-0468","contributorId":172151,"corporation":false,"usgs":true,"family":"Dorazio","given":"Robert","email":"bob_dorazio@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":733732,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keith Diagne, Lucy","contributorId":204222,"corporation":false,"usgs":false,"family":"Keith Diagne","given":"Lucy","affiliations":[{"id":36882,"text":"African Aquatic Conservation Fund","active":true,"usgs":false}],"preferred":false,"id":733733,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Luna, Fabia","contributorId":204223,"corporation":false,"usgs":false,"family":"Luna","given":"Fabia","affiliations":[{"id":36883,"text":"The National Center for Research and Conservation of Aquatic Mammals","active":true,"usgs":false}],"preferred":false,"id":733734,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lanyon, Janet M.","contributorId":204224,"corporation":false,"usgs":false,"family":"Lanyon","given":"Janet","email":"","middleInitial":"M.","affiliations":[{"id":13335,"text":"The University of Queensland","active":true,"usgs":false}],"preferred":false,"id":733735,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Reid, James P. 0000-0002-8497-1132 jreid@usgs.gov","orcid":"https://orcid.org/0000-0002-8497-1132","contributorId":3460,"corporation":false,"usgs":true,"family":"Reid","given":"James","email":"jreid@usgs.gov","middleInitial":"P.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":733736,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70196575,"text":"70196575 - 2018 - Opportunistically collected data reveal habitat selection by migrating Whooping Cranes in the U.S. Northern Plains","interactions":[],"lastModifiedDate":"2018-04-19T09:26:58","indexId":"70196575","displayToPublicDate":"2018-04-19T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Opportunistically collected data reveal habitat selection by migrating Whooping Cranes in the U.S. Northern Plains","docAbstract":"The Whooping Crane (Grus americana) is a federally endangered species in the United States and Canada that relies on wetland, grassland, and cropland habitat during its long migration between wintering grounds in coastal Texas, USA, and breeding sites in Alberta and Northwest Territories, Canada. We combined opportunistic Whooping Crane sightings with landscape data to identify correlates of Whooping Crane occurrence along the migration corridor in North Dakota and South Dakota, USA. Whooping Cranes selected landscapes characterized by diverse wetland communities and upland foraging opportunities. Model performance substantially improved when variables related to detection were included, emphasizing the importance of accounting for biases associated with detection and reporting of birds in opportunistic datasets. We created a predictive map showing relative probability of occurrence across the study region by applying our model to GIS data layers; validation using independent, unbiased locations from birds equipped with platform transmitting terminals indicated that our final model adequately predicted habitat use by migrant Whooping Cranes. The probability map demonstrated that existing conservation efforts have protected much top-tier Whooping Crane habitat, especially in the portions of North Dakota and South Dakota that lie east of the Missouri River. Our results can support species recovery by informing prioritization for acquisition and restoration of landscapes that provide safe roosting and foraging habitats. Our results can also guide the siting of structures such as wind towers and electrical transmission and distribution lines, which pose a strike and mortality risk to migrating Whooping Cranes.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-17-80.1","usgsCitation":"Niemuth, N.D., Ryba, A.J., Pearse, A.T., Kvas, S.M., Brandt, D.A., Wangler, B., Austin, J.E., and Carlisle, M.J., 2018, Opportunistically collected data reveal habitat selection by migrating Whooping Cranes in the U.S. Northern Plains: The Condor, v. 120, no. 2, p. 343-356, https://doi.org/10.1650/CONDOR-17-80.1.","productDescription":"14 p.","startPage":"343","endPage":"356","ipdsId":"IP-076151","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468819,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-17-80.1","text":"Publisher Index Page"},{"id":353596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota, South Dakota","volume":"120","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6d9e4b0da30c1bfbe92","contributors":{"authors":[{"text":"Niemuth, Neal D. 0009-0006-9637-5588","orcid":"https://orcid.org/0009-0006-9637-5588","contributorId":204334,"corporation":false,"usgs":false,"family":"Niemuth","given":"Neal","email":"","middleInitial":"D.","affiliations":[{"id":36919,"text":"U.S. Fish and Wildlife Service Habitat and Population Evaluation Team","active":true,"usgs":false}],"preferred":false,"id":733667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ryba, Adam J.","contributorId":204335,"corporation":false,"usgs":false,"family":"Ryba","given":"Adam","email":"","middleInitial":"J.","affiliations":[{"id":36919,"text":"U.S. Fish and Wildlife Service Habitat and Population Evaluation Team","active":true,"usgs":false}],"preferred":false,"id":733668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":733666,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kvas, Susan M.","contributorId":204336,"corporation":false,"usgs":false,"family":"Kvas","given":"Susan","email":"","middleInitial":"M.","affiliations":[{"id":36919,"text":"U.S. Fish and Wildlife Service Habitat and Population Evaluation Team","active":true,"usgs":false}],"preferred":false,"id":733669,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brandt, David A. 0000-0001-9786-307X dbrandt@usgs.gov","orcid":"https://orcid.org/0000-0001-9786-307X","contributorId":149929,"corporation":false,"usgs":true,"family":"Brandt","given":"David","email":"dbrandt@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":733670,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wangler, Brian","contributorId":204337,"corporation":false,"usgs":false,"family":"Wangler","given":"Brian","email":"","affiliations":[{"id":36919,"text":"U.S. Fish and Wildlife Service Habitat and Population Evaluation Team","active":true,"usgs":false}],"preferred":false,"id":733671,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Austin, Jane E. 0000-0001-8775-2210 jaustin@usgs.gov","orcid":"https://orcid.org/0000-0001-8775-2210","contributorId":146411,"corporation":false,"usgs":true,"family":"Austin","given":"Jane","email":"jaustin@usgs.gov","middleInitial":"E.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":733672,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Carlisle, Martha J.","contributorId":204338,"corporation":false,"usgs":false,"family":"Carlisle","given":"Martha","email":"","middleInitial":"J.","affiliations":[{"id":36920,"text":"U.S. Fish and Wildlife Service Ecological Serv, NE field office","active":true,"usgs":false}],"preferred":false,"id":733673,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70195765,"text":"tm3A25 - 2018 - Monitoring stream temperatures—A guide for non-specialists","interactions":[],"lastModifiedDate":"2018-05-01T14:53:13","indexId":"tm3A25","displayToPublicDate":"2018-04-19T00:00:00","publicationYear":"2018","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":"3-A25","title":"Monitoring stream temperatures—A guide for non-specialists","docAbstract":"Water temperature influences most physical and biological processes in streams, and along with streamflows is a major driver of ecosystem processes. Collecting data to measure water temperature is therefore imperative, and relatively straightforward. Several protocols exist for collecting stream temperature data, but these are frequently directed towards specialists. This document was developed to address the need for a protocol intended for non-specialists (non-aquatic) staff. It provides specific step-by-step procedures on (1) how to launch data loggers, (2) check the factory calibration of data loggers prior to field use, (3) how to install data loggers in streams for year-round monitoring, (4) how to download and retrieve data loggers from the field, and (5) how to input project data into organizational databases.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Surface-water techniques in Book 3: Applications of hydraulics","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm3A25","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Heck, M.P., Schultz, L.D., Hockman-Wert, D., Dinger, E.C., and Dunham, J.B., 2018, Monitoring stream temperatures—A guide for non-specialists: U.S. Geological Survey Techniques and Methods, book 3, chap. A25, 76 p., https://doi.org/10.3133/tm3A25.","productDescription":"iv, 76 p.","numberOfPages":"84","onlineOnly":"Y","ipdsId":"IP-090007","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":353592,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/03/a25/coverthb.jpg"},{"id":353593,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/03/a25/tm3a25.pdf","text":"Report","size":"45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 3A25"}],"publicComments":"This report is Chapter 25 of Section A: Surface-water techniques in Book 3: Applications of hydraulics.","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
We have conducted a gravity survey of the Coso geothermal field to continue the time-lapse gravity study of the area initiated in 1991. In this report, we outline a method of processing the gravity data that minimizes the random errors and instrument bias introduced into the data by the Scintrex CG-5 relative gravimeters that were used. After processing, the standard deviation of the data was estimated to be ±13 microGals. These data reveal that the negative gravity anomaly over the Coso geothermal field, centered on gravity station CER1, is continuing to increase in magnitude over time. Preliminary modeling indicates that water-table drawdown at the location of CER1 is between 65 and 326 meters over the last two decades. We note, however, that several assumptions on which the model results depend, such as constant elevation and free-water level over the study period, still require verification.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181053","collaboration":"Prepared in cooperation with the U.S. Department of the Navy Geothermal Program Office","usgsCitation":"Phelps, G., Cronkite-Ratcliff, C., and Blake, K., 2018, A time-lapse gravity survey of the Coso geothermal field, China Lake Naval Air Weapons Station, California: U.S. Geological Survey Open-File Report 2018–1053, 25 p., https://doi.org/10.3133/ofr20181053.","productDescription":"Report: v, 25 p.; Table","numberOfPages":"31","onlineOnly":"Y","ipdsId":"IP-082096","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":353589,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1053/coverthb.jpg"},{"id":353590,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1053/ofr20181053.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1053"},{"id":353607,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2018/1053/ofr20181053_table1.xlsx","text":"Table 1","size":"22 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2018-1053"}],"country":"United States","state":"California","otherGeospatial":"Coso Geothermal Field, China Lake Naval Air Weapons Station","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118,\n 35.88682489453265\n ],\n [\n -117.625,\n 35.88682489453265\n ],\n [\n -117.625,\n 36.25\n ],\n [\n -118,\n 36.25\n ],\n [\n -118,\n 35.88682489453265\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"A study of uranium in groundwater in northeastern Washington was conducted to make a preliminary assessment of naturally occurring uranium in groundwater relying on existing information and limited reconnaissance sampling. Naturally occurring uranium is associated with granitic and metasedimentary rocks, as well as younger sedimentary deposits, that occur in this region. The occurrence and distribution of uranium in groundwater is poorly understood. U.S. Environmental Protection Agency (EPA) regulates uranium in Group A community water systems at a maximum contaminant level (MCL) of 30 μg/L in order to reduce uranium exposure, protect from toxic kidney effects of uranium, and reduce the risk of cancer. However, most existing private wells in the study area, generally for single family use, have not been sampled for uranium. This document presents available uranium concentration data from throughout a multi-county region, identifies data gaps, and suggests further study aimed at understanding the occurrence of uranium in groundwater.
The study encompasses about 13,000 square miles (mi2) in the northeastern part of Washington with a 2010 population of about 563,000. Other than the City of Spokane, most of the study area is rural with small towns interspersed throughout the region. The study area also includes three Indian Reservations with small towns and scattered population. The area has a history of uranium exploration and mining, with two inactive uranium mines on the Spokane Indian Reservation and one smaller inactive mine on the outskirts of Spokane. Historical (1977–2016) uranium in groundwater concentration data were used to describe and illustrate the general occurrence and distribution of uranium in groundwater, as well as to identify data deficiencies. Uranium concentrations were detected at greater than 1 microgram per liter (μg/L) in 60 percent of the 2,382 historical samples (from wells and springs). Uranium concentrations ranged from less than 1 to 88,600 μg/L, and the median concentration of uranium in groundwater for all sites was 1.4 μg/L.
New (2017) uranium in groundwater concentration data were obtained by sampling 13 private domestic wells for uranium in areas without recent (2000s) water-quality data. Uranium was detected in all 13 wells sampled for this study; concentrations ranged from 1.03 to 1,180 μg/L with a median of 22 μg/L. Uranium concentrations of groundwater samples from 6 of the 13 wells exceeded the MCL for uranium. Uranium concentrations in water samples from two wells were 1,130 and 1,180 μg/L, respectively; nearly 40 times the MCL.
Additional data collection and analysis are needed in rural areas where self-supplied groundwater withdrawals are the primary source of water for human consumption. Of the roughly 43,000 existing water wells in the study area, only 1,755 wells, as summarized in this document, have available uranium concentration data, and some of those data are decades old. Furthermore, analysis of area groundwater quality would benefit from a more extensive chemical-analysis suite including general chemistry in order to better understand local geochemical conditions that largely govern the mobility of uranium. Although the focus of the present study is uranium, it also is important to recognize that there are other radionuclides of concern that may be present in area groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3401","usgsCitation":"Kahle, S.C., Welch, W.B., Tecca, A.E., and Eliason, D.M., 2018, Uranium concentrations in groundwater, northeastern Washington: U.S. Geological Survey Scientific Investigations Map 3401, 1 sheet, https://doi.org/10.3133/sim3401.","productDescription":"Map: 44.0 x 34.0 inches; Table; 3 Figures","additionalOnlineFiles":"Y","ipdsId":"IP-091739","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":353579,"rank":5,"type":{"id":13,"text":"Illustration"},"url":"https://pubs.usgs.gov/sim/3401/sim3401_figure05.pdf","text":"Figure 5","size":"4.5 MB layered","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3401 Figure 5","linkHelpText":"Locations of wells with associated uranium concentrations showing generalized geologic material of open interval, Ferry, Pend Oreille, and Stevens Counties, Washington. Wells with groundwater samples with uranium concentrations greater than or equal to 30 micrograms per liter are labeled."},{"id":353574,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3401/coverthb2.jpg"},{"id":353575,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3401/sim3401.pdf","text":"Map","size":"13.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3401"},{"id":353577,"rank":3,"type":{"id":13,"text":"Illustration"},"url":"https://pubs.usgs.gov/sim/3401/sim3401_figure03.pdf","text":"Figure 3","size":"8.3 MB layered","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3401 Figure 3","linkHelpText":"Geology, locations of uranium assay sites or mines, and locations of wells and springs with historical uranium concentrations in groundwater of greater than or equal to 10 micrograms per liter (μg/L), northeastern Washington, 1977–2016."},{"id":353581,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3401/sim3401_table01.xlsx","text":"Table 1","size":"210 KB xlsx","description":"SIM 3401 Table 1"},{"id":353578,"rank":4,"type":{"id":13,"text":"Illustration"},"url":"https://pubs.usgs.gov/sim/3401/sim3401_figure04.pdf","text":"Figure 4","size":"6.1 MB layered","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3401 Figure 4","linkHelpText":"Magnitude and distribution of historical uranium concentrations in groundwater samples, northeastern Washington, 1977–2016."}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120,\n 47.5\n ],\n [\n -117,\n 47.5\n ],\n [\n -117,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -120,\n 47.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
There is a growing interest toward the use of autonomous recording units (ARUs) for acoustic surveying of secretive marsh bird populations. However, there is little information on how ARUs compare to human surveyors or how best to use ARU data that can be collected continuously throughout the day. We used ARUs to conduct 2 acoustic surveys for king (Rallus elegans) and clapper rails (R. crepitans) within a tidal marsh complex along the Pamunkey River, Virginia, USA, during May–July 2015. To determine the effectiveness of an ARU in replacing human personnel, we compared results of callback point‐count surveys with concurrent acoustic recordings and calculated estimates of detection probability for both rail species combined. The success of ARUs at detecting rails that human observers recorded decreased with distance (P ≤ 0.001), such that at <25 m, 90.3% of human‐recorded rails also were detected by the ARU, but at >75 m, only 34.0% of human‐detected rails were detected by the ARU. To determine a subsampling scheme for continuous ARU data that allows for effective surveying of presence and call rates of rails, we used ARUs to conduct 15 continuous 48‐hr passive surveys, generating 720 hr of recordings. We established 5 subsampling periods of 5, 10, 15, 30, and 45 min to evaluate ARU‐based presence and vocalization detections of rails compared with each of the full 60‐min sampling of ARU‐based detection of rails. All subsampling periods resulted in different (P ≤ 0.001) detection rates and unstandardized vocalization rates compared with the hourly sampling period. However, standardized vocalization counts from the 30‐min subsampling period were not different from vocalization counts of the full hourly sampling period. When surveying rail species in estuarine environments, species‐, habitat‐, and ARU‐specific limitations to ARU sampling should be considered when making inferences about abundances and distributions from ARU data.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.860","usgsCitation":"Stiffler, L.L., Anderson, J.T., and Katzner, T., 2018, Evaluating autonomous acoustic surveying techniques for rails in tidal marshes: Wildlife Society Bulletin, v. 42, no. 1, p. 78-83, https://doi.org/10.1002/wsb.860.","productDescription":"6 p.","startPage":"78","endPage":"83","ipdsId":"IP-088311","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":499995,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/3d927e4f83cc4c23b0ab3649c9fefe3e","text":"External Repository"},{"id":353484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-08","publicationStatus":"PW","scienceBaseUri":"5afee6dae4b0da30c1bfbea0","contributors":{"authors":[{"text":"Stiffler, Lydia L.","contributorId":198904,"corporation":false,"usgs":false,"family":"Stiffler","given":"Lydia","email":"","middleInitial":"L.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false},{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":733623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, James T.","contributorId":28071,"corporation":false,"usgs":false,"family":"Anderson","given":"James","email":"","middleInitial":"T.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":733624,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":733622,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195863,"text":"ds1080 - 2018 - Compilation of new and previously published geochemical and modal data for Mesoproterozoic igneous rocks of the St. Francois Mountains, southeast Missouri","interactions":[],"lastModifiedDate":"2018-04-17T10:57:08","indexId":"ds1080","displayToPublicDate":"2018-04-16T13:50:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1080","title":"Compilation of new and previously published geochemical and modal data for Mesoproterozoic igneous rocks of the St. Francois Mountains, southeast Missouri","docAbstract":"The purpose of this report is to present recently acquired as well as previously published geochemical and modal petrographic data for igneous rocks in the St. Francois Mountains, southeast Missouri, as part of an ongoing effort to understand the regional geology and ore deposits of the Mesoproterozoic basement rocks of southeast Missouri, USA. The report includes geochemical data that is (1) newly acquired by the U.S. Geological Survey and (2) compiled from numerous sources published during the last fifty-five years. These data are required for ongoing petrogenetic investigations of these rocks. Voluminous Mesoproterozoic igneous rocks in the St. Francois Mountains of southeast Missouri constitute the basement buried beneath Paleozoic sedimentary rock that is over 600 meters thick in places. The Mesoproterozoic rocks of southeast Missouri represent a significant component of approximately 1.4 billion-year-old (Ga) igneous rocks that crop out extensively in North America along the southeast margin of Laurentia and subsequent researchers suggested that iron oxide-copper deposits in the St. Francois Mountains are genetically associated with ca. 1.4 Ga magmatism in this region. The geochemical and modal data sets described herein were compiled to support investigations concerning the tectonic setting and petrologic processes responsible for the associated magmatism.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1080","usgsCitation":"du Bray, E.A. Day, W.C., and Meighan, C.J., 2018,Compilation of new and previously published geochemical and modal data for Mesoproterozoic igneous rocks of the St. Francois Mountains, southeast Missouri: U.S. Geological Survey Data Series 1080, 10 p., https://doi.org/10.3133/ds1080.","productDescription":"Report: iv, 10 p.; Appendixes; Data Release; Read Me","onlineOnly":"Y","ipdsId":"IP-090393","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":353360,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79W0DSN","text":"USGS data release","linkHelpText":"Data release supporting compilation of new and previously published geochemical and modal data for Mesoproterozoic igneous rocks of the St. Francois Mountains, southeast Missouri"},{"id":353322,"rank":8,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/1080/ds1080_ReadMe.txt","text":"Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"DS 1080 Read Me"},{"id":353316,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1080/ds1080.pdf","text":"Report","size":"48.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1080"},{"id":353317,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1080/ds1080_appendix1_MO15_17_Field_Notes.txt","text":"Appendix 1. Field Notes","size":"16.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"DS 1080 Field Notes, Text File","linkHelpText":"Definition and characterization of data fields for field notes for igneous rocks of the St. Francois Mountains, southeast Missouri collected between 2015 and 2017 (text file)"},{"id":353320,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1080/ds1080_appendix2_SE_MO_ChemData_AlteredMineralized.txt","text":"Appendix 2. Chemical Data, Altered Mineralized","size":"348 kB","linkFileType":{"id":2,"text":"txt"},"description":"DS 1080 Chemical Data, Altered Mineralized","linkHelpText":"Definition and characterization of data fields for geochemical and modal data for igneous rocks in the St. Francois Mountains, southeast Missouri (text file)"},{"id":353315,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1080/coverthb.jpg"},{"id":353321,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1080/ds1080_appendix2_SE_MO_ChemData_FreshUnaltered.txt","text":"Appendix 2. Chemical Data, Fresh Unaltered","size":"256 kB","linkFileType":{"id":2,"text":"txt"},"description":"DS 1080 Chemical Data, Fresh Unaltered","linkHelpText":"Definition and characterization of data fields for geochemical and modal data for igneous rocks in the St. Francois Mountains, southeast Missouri (text file)"},{"id":353319,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1080/ds1080_appendix2_SE_MO_ChemData.xlsx","text":"Appendix 2. Chemical Data","size":"692 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 1080 Chemical Data","linkHelpText":"Definition and characterization of data fields for geochemical and modal data for igneous rocks in the St. Francois Mountains, southeast Missouri (Excel file)"},{"id":353318,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1080/ds1080_appendix1_MO15_17_Field_Notes.xlsx","text":"Appendix 1. Field Notes","size":"28.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 1080 Field Notes, Excel File","linkHelpText":"Definition and characterization of data fields for field notes for igneous rocks of the St. Francois Mountains, southeast Missouri collected between 2015 and 2017 (Excel file)"}],"country":"United States","state":"Missouri","otherGeospatial":"St. Francois Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.603759765625,\n 36.848856608486905\n ],\n [\n -89.98901367187499,\n 36.848856608486905\n ],\n [\n -89.98901367187499,\n 38.496593518947584\n ],\n [\n -92.603759765625,\n 38.496593518947584\n ],\n [\n -92.603759765625,\n 36.848856608486905\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geology, Geophysics and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046