In colonial seabirds, differences in the nesting or fledging success have been associated with differences in nest position within the breeding aggregation (subcolony): less successful nests are located on the periphery, with more successful nests closer to the center. For Pygoscelid penguins, central nests tend to be larger, with nest size being an indicator of individual quality because stones must be gathered singly, so more stones reflect more individual effort. Competition for nest materials, including the collection of materials from another’s nest, has also frequently been described in penguins and other colonial seabirds. We used the data collected during the incubation stage from a total of 20 subcolonies at two separate breeding colonies of Adélie penguins (Pygoscelis adeliae) on Ross Island (Antarctica) to test the influence of nest position on breeding success. We also investigated how competition for nest stones could occur at different intensities depending on size of the subcolony, nest position, and quality within a subcolony. We found that peripheral nests experienced lower breeding success and higher number of individuals attempting to remove stones with higher removal success rates than from nests toward the center. The higher costs associated with maintaining and defending nests that incur higher removal pressure could be an additional factor involved in the lower breeding success of peripheral nests.
Landslide detection and warning systems are important tools for mitigation of potential hazards in landslide prone areas. Traditionally, warning systems for shallow landslides have been informed by rainfall intensity-duration thresholds. More recent advances have introduced the concept of hydrometeorological thresholds that are informed not only by rainfall, but also by subsurface hydrological measurements. Previously, hydrometeorological thresholds have been shown to improve capabilities for forecasting shallow landslides, and they may ultimately be adapted to more generalized landslide forecasting. We present HydroMet, a code developed in Python by the U.S. Geological Survey, which allows users to guide the automated estimation of hydrometeorological thresholds for a site or area of interest, with the flexibility to select preferred threshold variables for the antecedent hydrologic conditions and the triggering meteorological conditions. Users can import hydrologic time-series data, including rainfall, soil-water content, and pore-water pressure, along with the times of known landslide occurrences, and then conduct objective optimization of warning thresholds using receiver operating characteristics. HydroMet presents many additional options, including selecting the threshold formula, the timescale of possible threshold variables, and the skill statistics used for optimization. Users can develop dual-stage thresholds for watch and warning alerts, with a lower, risk-averse threshold to avoid missed alarms and a less conservative threshold to minimize false alarms. Users may also choose to split their inventory data into calibration and evaluation subsets to independently evaluate the performance of optimized thresholds. We present output and applications of HydroMet using monitoring data from landslide-prone areas in the U.S. to demonstrate its utility and ability to produce thresholds with limited missed and false alarms for informing the next generation of reliable landslide warning systems.
","language":"English","publisher":"MDPI","doi":"10.3390/w13131752","usgsCitation":"Conrad, J.L., Morphew, M.D., Baum, R.L., and Mirus, B.B., 2021, HydroMet: A new code for automated objective optimization of hydrometeorological thresholds for landslide initiation: Water, v. 13, no. 3, 1752, 17 p., https://doi.org/10.3390/w13131752.","productDescription":"1752, 17 p.","ipdsId":"IP-129944","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":451750,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13131752","text":"Publisher Index Page"},{"id":386788,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Conrad, Jacob L. 0000-0001-8112-5355","orcid":"https://orcid.org/0000-0001-8112-5355","contributorId":260658,"corporation":false,"usgs":true,"family":"Conrad","given":"Jacob","email":"","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":818377,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morphew, Michael D. 0000-0003-0072-1652","orcid":"https://orcid.org/0000-0003-0072-1652","contributorId":207959,"corporation":false,"usgs":false,"family":"Morphew","given":"Michael","email":"","middleInitial":"D.","affiliations":[{"id":37668,"text":"USGS, Student- Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":818378,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":818379,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":818380,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229496,"text":"70229496 - 2021 - Long-term African dust delivery to the eastern Atlantic Ocean from the Sahara and Sahel regions: Evidence from Quaternary paleosols on the Canary Islands, Spain","interactions":[],"lastModifiedDate":"2022-03-09T12:51:18.585777","indexId":"70229496","displayToPublicDate":"2021-06-25T06:48:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Long-term African dust delivery to the eastern Atlantic Ocean from the Sahara and Sahel regions: Evidence from Quaternary paleosols on the Canary Islands, Spain","docAbstract":"Africa is the most important source of dust in the world today and dust storms from that continent frequently deposit sediment on the nearby Canary Islands. Many investigators have inferred African dust inputs to Canary Islands paleosols based only on the presence of quartz. However, some local rocks do contain this mineral, so quartz alone is insufficient proof of dust deposition. Further, it is not known whether the Sahara Desert or the Sahel region is more important as a dust source. We address these issues by study of sequences of Pleistocene aeolian sands on the islands of Lanzarote and Fuerteventura. Aeolian sands are composed mostly of marine carbonate minerals and locally derived volcanic minerals. They date from the early-middle Pleistocene to the Holocene. Trace element geochemistry shows that the soils formed from both locally derived basalt and African dust. Major element geochemistry and clay mineralogy indicate that dust additions to the Canary Islands likely come from both the Sahara and Sahel. Dust delivered from the Sahel indicates that droughts in that region have had a history extending through much of the Quaternary. Accretionary-inflationary profile development, from dust accretion, is evident in the upward growth of Canary Islands paleosols.
Geochemical data are presented for more than 1,500 archived stream-sediment samples and accompanying quality control samples. The archived sediments were reanalyzed to improve the stream geochemical dataset for Alaska and to support ongoing U.S. Geological Survey (USGS) studies. Sediment samples were primarily from the USGS Mineral Resources Program’s sample archive in Denver, Colorado, but a few were from the Alaska Geological & Geophysical Surveys’ Geologic Materials Center in Anchorage, Alaska. All samples were submitted to the USGS contract laboratory, AGAT Laboratories, for analysis. All samples were analyzed using a 60-element analytical method involving fusion of the sample by sodium peroxide, dissolution of the fusion cake by nitric acid, and elemental analysis by inductively coupled plasma-optical emission spectroscopy and inductively coupled plasma-mass spectroscopy. Additionally, 106 samples from the Nixon Fork area were analyzed by a second multi-element method involving decomposition by a mixture of hydrochloric, nitric, perchloric, and hydrofluoric acids and the elemental analysis of the resulting solution by inductively coupled plasma-optical emission spectroscopy and inductively coupled plasma-mass spectroscopy. The latter method was used because the detection limit is lower for several elements including As, Cd, Pb, and Sb. Mercury concentrations in 296 samples from southeast Alaska were determined using a cold-vapor atomic absorption spectrometry method. The concentration data from the archived samples are presented along with concentration data from the standard reference material that was submitted with the samples.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211058","usgsCitation":"Wang, B., Case, G.N.D., Granitto, M., Labay, K.A., Shew, N.B., Ingraham, A.D., Bueghly, Z.C., Azain, J.S., Karl, S.M., and Kelley, K.D., 2021, Chemical analysis of archived stream-sediment samples, Alaska: U.S. Geological Survey Open-File Report 2021–1058, 13 p., https://doi.org/10.3133/ofr20211058.","productDescription":"Report: vi, 13 p.; 2 Tables; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118502","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":386720,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2021/1058/ofr20211058_table1.2.csv","text":"Table 1.2","size":"87 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021-1058 table 1.2"},{"id":386719,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2021/1058/ofr20211058_table1.1.csv","text":"Table 1.1","size":"691 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021-1058 table 1.1"},{"id":386718,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1058/ofr20211058.pdf","text":"Report","size":"1.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1058"},{"id":386717,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1058/coverthb.jpg"},{"id":386721,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FR1D6Y","text":"USGS data release","description":"USGS data 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Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
A two-dimensional hydraulic model; water-surface profiles; and digital maps of water-surface elevation, velocities, and water depths were developed for a 6.7-mile reach of Joachim Creek within and near the city of De Soto, Missouri. Water-surface profiles were generated for the 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probability (10-, 25-, 50-, 100-, and 500-year recurrence interval) flows. Digital maps of water-surface elevation, water depth, and velocity were generated for the 1- and 0.2-percent annual exceedance probability flows. Water-surface elevations and inundation extents of generated profiles and maps were substantially lower than similar products produced for the 2019 flood-insurance study that included the study reach. The differences in water-surface elevations can be attributed to differences in input streamflows and hydraulic simulation techniques.
The water-surface elevations generated for the 1- and 0.2-percent annual exceedance probability flows were used to assess the vulnerability and inundation depths of 231 selected structures within the city of De Soto. Results indicate that 157 to 177 of the 231 structures were affected at the 1-percent annual exceedance probability flow, depending on the adjacent grade elevation used for reference. Between 185 and 198 structures were affected at the 0.2-percent annual exceedance probability flow, depending on grade elevation. Inundation depths at the affected structures were 0.02 to 9.28 feet (ft), depending on the flow and adjacent grade reference.
Flood elevations were computed for Joachim Creek using a two-dimensional, finite-volume numerical modeling application for river hydraulics. The hydraulic model was calibrated using high-water marks from the April 18, 2013, flood and the maximum measured streamflow at the U.S. Geological Survey streamgage Joachim Creek at De Soto, Mo. (station 07019500), on September 8, 2018. The calibrated model was then used to compute the hydraulic conditions associated with the 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probability flows. The simulated water-surface elevations and digital elevation model (derived from light detection and ranging data having a 0.60-ft vertical accuracy and a 1.97-ft horizontal resolution) were used to generate products including water-surface profiles and maps of inundated area, water depth, and velocities using model postprocessing software.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215058","collaboration":"Prepared in cooperation with the City of De Soto, Missouri","usgsCitation":"Hix, K.D., Rydlund, P.H., and Heimann, D.C., 2021, Two-dimensional hydraulic analyses of Joachim Creek, De Soto, Missouri: U.S. Geological Survey Scientific Investigations Report 2021–5058, 28 p., https://doi.org/10.3133/sir20215058.","productDescription":"Report: viii, 28 p.; Appendix; 2 Data Releases; Dataset","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-124332","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386714,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS dataset","linkHelpText":"— USGS water data for the Nation"},{"id":386713,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92MQYE7","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial data and model archive associated with the two-dimensional hydraulic analysis of Joachim Creek, De Soto, Missouri"},{"id":386712,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ACXXI7","text":"USGS data release","description":"USGS data release","linkHelpText":"PeakFQ software input files and selected output files for selected long-term streamgages near Jefferson County, Missouri, through water year 2019"},{"id":386711,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5058/sir20215058_table1.1.csv","text":"Table 1.1 (.csv format)","size":"18.2 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2021–5058 Appendix 1.1","linkHelpText":"— Summary of water-surface elevations and depths at selected structures in the city of De Soto, Missouri, for 1- and 0.2-percent annual exceedance probability streamflows"},{"id":386710,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5058/sir20215058_table1.1.xlsx","text":"Table 1.1 (.xlsx format)","size":"34.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2021–5058 Appendix 1.1","linkHelpText":"— Summary of water-surface elevations and depths at selected structures in the city of De Soto, Missouri, for 1- and 0.2-percent annual exceedance probability streamflows"},{"id":386709,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5058/sir20215058.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5058"},{"id":386708,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5058/coverthb.jpg"}],"country":"United States","state":"Missouri","county":"Jefferson County","otherGeospatial":"Joachim 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Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Increasing demand for the limited water resources of the United States continues to put pressure on resource management agencies to balance the competing needs of ecosystem health with municipal, agricultural, and other uses. To meet these needs, the U.S. Geological Survey conducted a multiyear study to evaluate water resources in the upper Rio Grande Basin in the southwestern United States. The upper Rio Grande Basin extends from south-central Colorado, through New Mexico, into west Texas near Fort Quitman, including parts of Chihuahua, Mexico. The upper Rio Grande Basin consists of a sequence of alluvial basins that formed in the Rio Grande rift approximately 30 million years ago.
This report describes the hydrogeology of the upper Rio Grande Basin and how the groundwater resources in the basin have changed from 1980 to 2015. The hydrogeologic framework includes the horizontal delineation of the alluvial basins within the upper Rio Grande Basin from the headwaters in Colorado to Fort Quitman, Texas, including part of Mexico. Groundwater-level measurements from existing State and Federal data were used to construct groundwater-level altitude and groundwater-level change maps.
Of the 2,699 wells with groundwater-level data used in this study, 1,055 wells had data for only a single 5-year period, 703 wells had data for 50 percent or more of the 35 years of the study, and only 57 wells have 5-year groundwater-level data for the entire study period. The median decline in water levels in the upper Rio Grande Basin was 0.13 foot (ft) per 5-year period, and declines were measured in 53 percent of the 703 wells that contained data for 50 percent or more of the study period. Rates of groundwater-level decline greater than 1 ft per 5-year period were measured in 17 percent of the wells, greater than 2 ft per 5-year period, in 3 percent of the wells, and greater than 3 ft per 5-year period, in 1 percent of the wells. Overall, groundwater levels rose in 6 percent of the 703 wells that contained data for 50 percent or more of the study period, and in 4 percent of the wells, groundwater levels rose by 1 ft or more per 5-year period.
Groundwater-level changes in wells with consecutive 5-year measurement periods exhibited the most variability in the Española, Middle Rio Grande, and Mesilla/Conejos-Médanos alluvial basins. The largest declines in groundwater-level altitudes in individual wells were observed in the Española alluvial basin during 1995–2000, in the Palomas alluvial basin during 2010–2015, and in the Jornada del Muerto alluvial basin during 2005–10. The largest rises in groundwater-level altitudes in individual wells were observed in the Española alluvial basin during 2005–10, in the Middle Rio Grande alluvial basin during 1995–2000, and in the Mesilla/Conejos-Médanos alluvial basin during 1980–85.
Changes in groundwater storage throughout the study period varied by alluvial basin, likely based largely on changes in groundwater withdrawals because of increased demands during drier periods and population growth. All alluvial basins except the Tularosa-Hueco alluvial basin were evaluated for changes in groundwater storage from 1980 to 2015. Extremely limited data availability in 2010–15 for the Tularosa-Hueco alluvial basin led to this 5-year period being dropped from the groundwater-level change map and storage analysis for this basin.
In the San Luis Valley in southern Colorado, efforts to reverse groundwater depletion in the unconfined aquifer recovered approximately 250,000 acre-feet in storage between late 2013 and early 2018, following the implementation of a “pay-to-pump” groundwater program. However, severe drought that persists in the upper Rio Grande Basin, particularly in southern Colorado, has undone some of the conservation efforts. Within the Española alluvial basin, groundwater storage varied because municipal demand increased the demand on groundwater resources and conservation efforts were implemented. A groundwater-flow model evaluated for the Española alluvial basin indicated declines in groundwater storage from 1947 through 1982. Groundwater storage decreased in the Española alluvial basin in 1980–85, 1985–90, 1990–95, 1995–2000, and 2005–10 and increased in 2000–05 and 2010–15 leading to groundwater storage in 2015 about even with that in 1985.
Based on gridded groundwater-level altitudes, groundwater storage decreased in the Middle Rio Grande Basin from 1980 to 2015, except for during the 1980–85, 2000–05, and 2010–15 periods with an overall cumulative storage decrease from 1980 to 2015. Groundwater-flow models evaluated for the Middle Rio Grande alluvial basin showed groundwater storage in the Middle Rio Grande alluvial basin has been reduced since the mid-1950s through the end of the study period except for a brief recovery (reduction in storage outflow) in the mid-1980s. Simulated groundwater storage has also decreased in parts of the Palomas and Mesilla/Conejos-Médanos alluvial basins, and the northern part of the Conejos-Médanos alluvial basin starting in 1995 (excluding 2005 and 2007) and in the Tularosa-Hueco alluvial basin from the early 1940s to the end of the study period. Groundwater storage increased in the Mesilla/Conejos-Médanos alluvial basin during 1980–85 and slightly during 1990–95 and then decreased in the other 5-year periods. Groundwater storage in the Tularosa-Hueco alluvial basin increased from 1985 to 1990, but otherwise decreased, leading to an overall net groundwater-level decline in this part of the basin from 1980 to 2010.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215035","programNote":"Water Availability and Use Science Program","usgsCitation":"Houston, N.A., Thomas, J.V., Foster, L.K., Pedraza, D.E., and Welborn, T.L., 2021, Hydrogeologic framework and groundwater characterization in selected alluvial basins in the upper Rio Grande basin, Colorado, New Mexico, and Texas, United States, and Chihuahua, Mexico, 1980 to 2015: U.S. Geological Survey Scientific Investigations Report 2021–5035, 71 p., https://doi.org/10.3133/sir20215035.","productDescription":"Report: viii, 71 p.; Data Release","numberOfPages":"71","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094878","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":436289,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJH17","text":"USGS data release","linkHelpText":"Electrical Resistivity Tomography (ERT) and Horizontal-to-Vertical Spectral Ratio (HVSR) Data Collected Within and Near Ellsworth Air Force Base, South Dakota, from 2014 to 2019"},{"id":386627,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7N58KBS","text":"USGS data release","linkHelpText":"Hydrogeologic, geologic, and water-level data for the groundwater component of the upper Rio Grande Focus Area Study, Colorado, New Mexico, and Texas, United States and Chihuahua, Mexico 2017"},{"id":386626,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5035/sir20215035.pdf","text":"Report","size":"22.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5035"},{"id":386625,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5035/coverthb.jpg"}],"country":"Mexico, United States","state":"Colorado, New Mexico, Texas, Chihuahua","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.5341796875,\n 36.94989178681327\n ],\n [\n -107.9736328125,\n 35.67514743608467\n ],\n [\n -107.75390625,\n 33.358061612778876\n ],\n [\n -107.9736328125,\n 31.728167146023935\n ],\n [\n -107.6220703125,\n 30.524413269923986\n ],\n [\n -106.787109375,\n 29.611670115197377\n ],\n [\n -105.9521484375,\n 29.38217507514529\n ],\n [\n -105.3369140625,\n 30.372875188118016\n ],\n [\n -105.6884765625,\n 31.615965936476076\n ],\n [\n -105.4248046875,\n 33.063924198120645\n ],\n [\n -104.80957031249999,\n 35.53222622770337\n ],\n [\n -104.853515625,\n 38.09998264736481\n ],\n [\n -106.5673828125,\n 38.75408327579141\n ],\n [\n -107.314453125,\n 37.96152331396614\n ],\n [\n -107.5341796875,\n 36.94989178681327\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
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This study presents the first large-scale assessment of cyanobacterial frequency and abundance of surface water near drinking water intakes across the United States. Public water systems serve drinking water to nearly 90% of the United States population. Cyanobacteria and their toxins may degrade the quality of finished drinking water and can lead to negative health consequences. Satellite imagery can serve as a cost-effective and consistent monitoring technique for surface cyanobacterial blooms in source waters and can provide drinking water treatment operators information for managing their systems. This study uses satellite imagery from the European Space Agency's Ocean and Land Colour Instrument (OLCI) spanning June 2016 through April 2020. At 300-m spatial resolution, OLCI imagery can be used to monitor cyanobacteria in 685 drinking water sources across 285 lakes in 44 states, referred to here as resolvable drinking water sources. First, a subset of satellite data was compared to a subset of responses (n = 84) submitted as part of the U.S. Environmental Protection Agency's fourth Unregulated Contaminant Monitoring Rule (UCMR 4). These UCMR 4 qualitative responses included visual observations of algal bloom presence and absence near drinking water intakes from March 2018 through November 2019. Overall agreement between satellite imagery and UCMR 4 qualitative responses was 94% with a Kappa coefficient of 0.70. Next, temporal frequency of cyanobacterial blooms at all resolvable drinking water sources was assessed. In 2019, bloom frequency averaged 2% and peaked at 100%, where 100% indicated a bloom was always present at the source waters when satellite imagery was available. Monthly cyanobacterial abundances were used to assess short-term trends across all resolvable drinking water sources and effect size was computed to provide insight on the number of years of data that must be obtained to increase confidence in an observed change. Generally, 2016 through 2020 was an insufficient time period for confidently observing changes at these source waters; on average, a decade of satellite imagery would be required for observed environmental trends to outweigh variability in the data. However, five source waters did demonstrate a sustained short-term trend, with one increasing in cyanobacterial abundance from June 2016 to April 2020 and four decreasing.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2021.117377","usgsCitation":"Coffer, M., Schaeffer, B., Foreman, K., Porteous, A., Loftin, K.A., Stumpf, R., Werdell, J., Urquhart, E., Albert, R., and Darling, J., 2021, Assessing cyanobacterial frequency and abundance at surface waters near drinking water intakes across the United States: Water Research, v. 201, no. 1, 117377, 13 p., https://doi.org/10.1016/j.watres.2021.117377.","productDescription":"117377, 13 p.","ipdsId":"IP-129698","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":451757,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2021.117377","text":"Publisher Index Page"},{"id":387133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"201","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coffer, Megan","contributorId":260848,"corporation":false,"usgs":false,"family":"Coffer","given":"Megan","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818966,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaeffer, Blake A.","contributorId":260849,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake A.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818967,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foreman, Katherine","contributorId":260850,"corporation":false,"usgs":false,"family":"Foreman","given":"Katherine","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818968,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Porteous, Alex","contributorId":260851,"corporation":false,"usgs":false,"family":"Porteous","given":"Alex","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818969,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loftin, Keith A. 0000-0001-5291-876X","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":221964,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":818970,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stumpf, Richard","contributorId":260852,"corporation":false,"usgs":false,"family":"Stumpf","given":"Richard","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":818971,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Werdell, Jeremy","contributorId":260853,"corporation":false,"usgs":false,"family":"Werdell","given":"Jeremy","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":818972,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Urquhart, Erin","contributorId":260854,"corporation":false,"usgs":false,"family":"Urquhart","given":"Erin","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":818973,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Albert, Ryan","contributorId":260855,"corporation":false,"usgs":false,"family":"Albert","given":"Ryan","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818974,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Darling, John","contributorId":260856,"corporation":false,"usgs":false,"family":"Darling","given":"John","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818975,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70221576,"text":"sir20215059 - 2021 - Borehole analysis, single-well aquifer testing, and water quality for the Burnpit well, Mount Rushmore National Memorial, South Dakota","interactions":[],"lastModifiedDate":"2021-06-25T11:51:29.973079","indexId":"sir20215059","displayToPublicDate":"2021-06-24T10:38:00","publicationYear":"2021","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":"2021-5059","displayTitle":"Borehole Analysis, Single-Well Aquifer Testing, and Water Quality for the Burnpit Well, Mount Rushmore National Memorial, South Dakota","title":"Borehole analysis, single-well aquifer testing, and water quality for the Burnpit well, Mount Rushmore National Memorial, South Dakota","docAbstract":"Mount Rushmore National Memorial (hereafter referred to as “the memorial”), in western South Dakota, is maintained by the National Park Service (NPS) and includes 1,278 acres of land in the east-central part of the Black Hills. An ongoing challenge for NPS managers at the memorial is providing water from sustainable and reliable sources for operations, staff, and the increasing number of visitors. In 2020, the U.S. Geological Survey (USGS) and NPS completed a hydrological study of the Burnpit well (well 5), a 580-foot-deep open hole groundwater well completed in metamorphic (crystalline) rock at the memorial. The purpose of this study was to estimate the geological and hydraulic properties of the aquifer supplying the well and to determine the water quality of the groundwater from the well. The study provides NPS staff and managers background information for assessing future uses for the well. Methods for data collection and analysis for the study included borehole and video camera analysis in 2020, aquifer testing by the NPS in 2009 and the USGS in 2020, and water-quality sampling in 2020.
Borehole camera video generally matched the lithology recorded in the well log. Fractures recorded in the well log and observed with the borehole camera, including more than 20 less prominent fractures and rough sidewall areas, indicated a fractured aquifer. The fractures are the primary conduits for groundwater flow through the rock and into the well.
Transmissivity was estimated for the upper and lower water-level drawdown zones at the Burnpit well with data from the NPS and USGS using the Theis and Cooper-Jacob methods. Transmissivity for the NPS test using the Theis method was 9.0 and 11 feet squared per day (ft2/d) for the upper and lower drawdown zones, respectively. Using the Cooper-Jacob method, the transmissivity was 22 and 14 ft2/d for the upper and lower drawdown zones of the aquifer, respectively. Transmissivity estimates from data from the USGS test were similar. The Theis method, applied to the upper and lower drawdown zones of the aquifer, produced transmissivity estimates of 7.7 and 10 ft2/d, and the Cooper-Jacob method produced estimates of 9.7 and 12 ft2/d, respectively.
Storativity (specific yield) estimated using the Theis method for the NPS aquifer-test data was 0.85 and 0.92 for the upper and lower drawdown zones of the aquifer, respectively. The Cooper-Jacob method applied to the NPS aquifer-test data produced storativity estimates of 0.11 and 0.50 for the upper and lower drawdown zones, respectively. The Theis method applied to the USGS aquifer-test data estimated storativity values of 0.77 and 1.0 for the upper and lower drawdown zones, respectively. The Cooper-Jacob method estimated storativity of 0.50 and 0.60 for the upper and lower drawdown zones of the USGS aquifer test, respectively. The estimated storativity values from the NPS and USGS aquifer tests for the upper and lower drawdown zones were higher than expected for limestones and schists.
The hypothetical equilibrium drawdown for the Burnpit well was estimated after the NPS test in 2009 at no more, and possibly less, than 35 gallons per minute. The NPS noted that the sustainable yield likely was overestimated because the water level did not stabilize during the NPS aquifer test. The specific capacity for the NPS aquifer test in 2009 was 0.16 gallon per minute per foot ([gal/min]/ft) of drawdown at 3 hours, and the specific capacity for the USGS aquifer test in 2020 was 0.13 (gal/min)/ft of drawdown at 3 hours. The rate of water-level recovery after pumping ceased was 0.017 and 0.013 (gal/min)/ft for the NPS and USGS aquifer tests, respectively. The water-level recovery rate was nearly an order of magnitude less than the specific capacity estimated during pumping, indicating that water levels in the Burnpit well may not recover quickly enough during pumping to provide for a continuous source of water.
Water-quality samples were collected at the Burnpit well on June 24 and July 23, 2020, and analyzed for field-measured properties, major ions, metals, nutrients, and perchlorate. Iron, zinc, and lithium concentrations for unfiltered samples in the well were at least three times greater than the mean filtered sample concentrations reported for crystalline aquifers in the Black Hills. Manganese concentrations were less than the mean concentration for crystalline aquifers but exceeded the U.S. Environmental Protection Agency (EPA) secondary drinking-water standards. The iron concentration from the June 24 sample was about 11 times greater than the EPA secondary drinking-water standards and mean concentrations from crystalline aquifers in the Black Hills. Arsenic concentrations in Burnpit well samples collected in 2020 were greater than the EPA primary drinking-water standard and the mean concentration for crystalline aquifers in the Black Hills. Arsenic occurs naturally in the rock of crystalline aquifers, and concentrations from samples in the Black Hills commonly exceed the EPA primary drinking-water standard of 10 micrograms per liter. High concentrations of arsenic, iron, and manganese metals in the Burnpit well make groundwater from the well in its natural state unusable as a drinking-water source, and water treatment would be necessary to reduce the trace element concentrations to less than the EPA primary and secondary drinking-water standards. However, if the memorial has immediate nonpotable water requirements, such as for construction and fire suppression, groundwater from the Burnpit well could provide water without causing additional stress to current (2021) drinking-water sources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215059","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Eldridge, W.G., Hoogestraat, G.K., and Rice, S.E., 2021, Borehole analysis, single-well aquifer testing, and water quality for the Burnpit well, Mount Rushmore National Memorial, South Dakota: U.S. Geological Survey Scientific Investigations Report 2021–5059, 29 p., https://doi.org/10.3133/sir20215059.","productDescription":"Report: vii, 29 p.; Data Release; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-126498","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386673,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98OZQN9","text":"USGS data release","description":"USGS data release","linkHelpText":"Borehole video and aquifer test data for the Burnpit well, Mount Rushmore National Memorial, South Dakota, 2020"},{"id":386672,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5059/sir20215059.pdf","text":"Report","size":"2.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5059"},{"id":386674,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS dataset","linkHelpText":"— USGS water data for the Nation"},{"id":386671,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5059/coverthb.jpg"}],"country":"United States","state":"South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.0625,\n 43.40903821777055\n ],\n [\n -103.2440185546875,\n 43.40903821777055\n ],\n [\n -103.2440185546875,\n 44.52392653654213\n ],\n [\n -104.0625,\n 44.52392653654213\n ],\n [\n -104.0625,\n 43.40903821777055\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Amphibian larvae are commonly used as indicators of aquatic ecosystem health because they are susceptible to contaminants. However, there is limited information on how species characteristics and trophic position influence contaminant loads in larval amphibians. Importantly, there remains a need to understand whether grazers (frogs and toads [anurans]) and predators (salamanders) provide comparable information on contaminant accumulation or if they are each indicative of unique environmental processes and risks. To better understand the role of trophic position in contaminant accumulation, we analyzed composite tissues for 10 metals from larvae of multiple co-occurring anuran and salamander species from 20 wetlands across the United States. We examined how metal concentrations varied with body size (anurans and salamanders) and developmental stage (anurans) and how the digestive tract (gut) influenced observed metal concentrations. Across all wetlands, metal concentrations were greater in anurans than salamanders for all metals tested except mercury (Hg), selenium (Se), and zinc (Zn). Concentrations of individual metals in anurans decreased with increasing weight and developmental stage. In salamanders, metal concentrations were less correlated with weight, indicating diet played a role in contaminant accumulation. Based on batches of similarly sized whole-body larvae compared to larvae with their digestive tracts removed, our results indicated that tissue type strongly affected perceived concentrations, especially for anurans (gut represented an estimated 46–97% of all metals except Se and Zn). This suggests the reliability of results based on whole-body sampling could be biased by metal, larval size, and development. Overall, our data shows that metal concentrations differs between anurans and salamanders, which suggests that metal accumulation is unique to feeding behavior and potentially trophic position. To truly characterize exposure risk in wetlands, species of different life histories, sizes and developmental stages should be included in biomonitoring efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2021.117638","usgsCitation":"Smalling, K., Oja, E.B., Cleveland, D.M., Davenport, J.D., Eagles-Smith, C., Campbell Grant, E.H., Kleeman, P.M., Halstead, B., Stemp, K.M., Tornabene, B., Bunnell, Z.J., and Hossack, B., 2021, Metal accumulation varies with life history, size, and development of larval amphibians: Environmental Pollution, v. 287, 117638, 10 p., https://doi.org/10.1016/j.envpol.2021.117638.","productDescription":"117638, 10 p.","ipdsId":"IP-127103","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern 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0000-0001-6142-8703","orcid":"https://orcid.org/0000-0001-6142-8703","contributorId":261172,"corporation":false,"usgs":true,"family":"Bunnell","given":"Zachary","email":"","middleInitial":"J","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819416,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":819417,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70221878,"text":"70221878 - 2021 - Exploring the potential of electrospray-Orbitrap for stable isotope analysis using nitrate as a model","interactions":[],"lastModifiedDate":"2021-07-12T13:41:12.547617","indexId":"70221878","displayToPublicDate":"2021-06-24T08:37:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":761,"text":"Analytical Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Exploring the potential of electrospray-Orbitrap for stable isotope analysis using nitrate as a model","docAbstract":"Widely used isotope ratio mass spectrometers have limited capabilities to measure metabolites, drugs, or small polyatomic ions without the loss of structural isotopic information. A new approach has recently been introduced that uses electrospray ionization Orbitrap to measure multidimensional isotope signatures of intact polar compounds. Using nitrate as a model compound, this study aims to establish performance metrics for comparisons with conventional IRMS at the natural abundance level. We present a framework on how to convert isotopolog intensities to δ values that are commonly used in the isotope geochemistry community. The quantification of seven nitrate isotopologs provides multiple pathways for obtaining the primary N and O δ values including non-mass-dependent O isotope variations, as well as opportunities to explore nonrandom isotopic distributions (i.e., clumping effects) within molecular nitrate. Using automation and the adaptation of measurement principles that are specific to isotope ratio analysis, nitrate δ15NAIR, δ18OVSMOW, and δ17OVSMOW were measured with a long-term precision of 0.4‰ or better for isotopic reference materials and purified nitrate from environmental samples. In addition, we demonstrate promising results for unpurified environmental samples in liquid form. With these new developments, this study connects the two largely disparate mass spectrometry fields of bioanalytical MS and isotope ratio MS, thus providing a route to measure new isotopic signatures in diverse organic and inorganic solutes.","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.analchem.1c00944","usgsCitation":"Hilkert, A., Bohlke, J., Mroczkowski, S.J., Fort, K.L., Aizikov, K., Wang, X.T., Kopf, S.H., and Neubauer, C., 2021, Exploring the potential of electrospray-Orbitrap for stable isotope analysis using nitrate as a model: Analytical Chemistry, v. 93, p. 9139-9148, https://doi.org/10.1021/acs.analchem.1c00944.","productDescription":"10 p.","startPage":"9139","endPage":"9148","ipdsId":"IP-126882","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":387105,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","noUsgsAuthors":false,"publicationDate":"2021-06-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Hilkert, Andreas","contributorId":260947,"corporation":false,"usgs":false,"family":"Hilkert","given":"Andreas","email":"","affiliations":[{"id":52734,"text":"Thermo-Fisher Scientific","active":true,"usgs":false}],"preferred":false,"id":819178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":819179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mroczkowski, Stanley J. 0000-0001-8026-6025 smroczko@usgs.gov","orcid":"https://orcid.org/0000-0001-8026-6025","contributorId":2628,"corporation":false,"usgs":true,"family":"Mroczkowski","given":"Stanley","email":"smroczko@usgs.gov","middleInitial":"J.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":819180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fort, Kyle L.","contributorId":260948,"corporation":false,"usgs":false,"family":"Fort","given":"Kyle","email":"","middleInitial":"L.","affiliations":[{"id":52734,"text":"Thermo-Fisher Scientific","active":true,"usgs":false}],"preferred":false,"id":819181,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aizikov, Konstantin","contributorId":260949,"corporation":false,"usgs":false,"family":"Aizikov","given":"Konstantin","email":"","affiliations":[{"id":52734,"text":"Thermo-Fisher Scientific","active":true,"usgs":false}],"preferred":false,"id":819182,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Xinchen T.","contributorId":260950,"corporation":false,"usgs":false,"family":"Wang","given":"Xinchen","email":"","middleInitial":"T.","affiliations":[{"id":13422,"text":"Boston College","active":true,"usgs":false}],"preferred":false,"id":819183,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kopf, Sebastian H.","contributorId":260951,"corporation":false,"usgs":false,"family":"Kopf","given":"Sebastian","email":"","middleInitial":"H.","affiliations":[{"id":51970,"text":"U Colorado","active":true,"usgs":false}],"preferred":false,"id":819184,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Neubauer, Cajetan","contributorId":260952,"corporation":false,"usgs":false,"family":"Neubauer","given":"Cajetan","email":"","affiliations":[{"id":51970,"text":"U Colorado","active":true,"usgs":false}],"preferred":false,"id":819185,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70224562,"text":"70224562 - 2021 - Experimental warming across a tropical forest canopy height gradient reveals minimal photosynthetic and respiratory acclimation","interactions":[],"lastModifiedDate":"2021-09-28T12:44:23.554388","indexId":"70224562","displayToPublicDate":"2021-06-24T07:42:26","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9360,"text":"Plant, Cell, and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Experimental warming across a tropical forest canopy height gradient reveals minimal photosynthetic and respiratory acclimation","docAbstract":"Tropical forest canopies cycle vast amounts of carbon, yet we still have a limited understanding of how these critical ecosystems will respond to climate warming. We implemented in situ leaf-level + 3°C experimental warming from the understory to the upper canopy of two Puerto Rican tropical tree species, Guarea guidonia and Ocotea sintenisii. After approximately 1 month of continuous warming, we assessed adjustments in photosynthesis, chlorophyll fluorescence, stomatal conductance, leaf traits and foliar respiration. Warming did not alter net photosynthetic temperature response for either species; however, the optimum temperature of Ocotea understory leaf photosynthetic electron transport shifted upward. There was no Ocotea respiratory treatment effect, while Guarea respiratory temperature sensitivity (Q10) was down-regulated in heated leaves. The optimum temperatures for photosynthesis (Topt) decreased 3–5°C from understory to the highest canopy position, perhaps due to upper canopy stomatal conductance limitations. Guarea upper canopy Topt was similar to the mean daytime temperatures, while Ocotea canopy leaves often operated above Topt. With minimal acclimation to warmer temperatures in the upper canopy, further warming could put these forests at risk of reduced CO2 uptake, which could weaken the overall carbon sink strength of this tropical forest.
The conservation of native insect pollinators is hampered by a lack of information about environmental factors influencing pollinator communities. We investigated how insect pollinator communities, composed of bees (Hymenoptera), butterflies and moths (Lepidoptera), and flies (Diptera), are influenced by spatial and temporal aspects of the environment in sagebrush steppe shrublands. We assessed hypotheses regarding spatial autocorrelation of communities, inter-annual variability in communities, influence of elevation on timing of emergence, and influence of weather on seasonal changes in relative abundance of different pollinator taxa. We captured 27,310 insects from 6 bee families, 27 butterfly and moth families, and 3 fly families. The occurrence of insect pollinators among sampling plots was not spatially autocorrelated, indicating that insect communities may be structured by habitat variation and microclimates over relatively fine spatial scales. Pollinator familial richness, diversity, abundance, and timing of emergence were most strongly positively associated with spatiotemporal variation in minimum daily temperatures at the ground surface during the active season. Emergence timing was positively correlated with growing degree days and percent humidity, regardless of elevation. All pollinator groups varied in abundance throughout their active season, peaking in early July (bees), late July (flies), or early August (butterflies and moths). Our findings suggest that changes in nighttime temperatures, which have been steadily increasing over the last several decades as a result of climate change, may have strong effects on sagebrush steppe pollinator communities. Also, non-bee pollinators may provide particularly important pollination in this vast ecosystem during the warmest time of the year.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2021.e01691","usgsCitation":"Rohde, A., and Pilliod, D.S., 2021, Spatiotemporal dynamics of insect pollinator communities in sagebrush steppe associated with weather and vegetation: Global Ecology and Conservatuin, v. 29, e01691, 16 p., https://doi.org/10.1016/j.gecco.2021.e01691.","productDescription":"e01691, 16 p.","ipdsId":"IP-111292","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"links":[{"id":451760,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2021.e01691","text":"Publisher Index Page"},{"id":436292,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JL1WEN","text":"USGS data release","linkHelpText":"Insect community responses to climate and weather across elevation gradients in the Sagebrush Steppe, eastern Oregon 2012 and 2013"},{"id":389154,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.2840576171875,\n 41.9921602333763\n ],\n [\n -117.00439453125,\n 41.9921602333763\n ],\n [\n -117.00439453125,\n 44.15068115978094\n ],\n [\n -119.2840576171875,\n 44.15068115978094\n ],\n [\n -119.2840576171875,\n 41.9921602333763\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rohde, Ashley 0000-0003-4939-3047 arohde@usgs.gov","orcid":"https://orcid.org/0000-0003-4939-3047","contributorId":5250,"corporation":false,"usgs":true,"family":"Rohde","given":"Ashley","email":"arohde@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":823211,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":823212,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221581,"text":"70221581 - 2021 - Sources and risk factors for nitrate and microbial contamination of private household wells in the fractured dolomite aquifer of northeastern Wisconsin","interactions":[],"lastModifiedDate":"2021-06-24T14:54:40.481459","indexId":"70221581","displayToPublicDate":"2021-06-23T09:52:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1542,"text":"Environmental Health Perspectives","active":true,"publicationSubtype":{"id":10}},"title":"Sources and risk factors for nitrate and microbial contamination of private household wells in the fractured dolomite aquifer of northeastern Wisconsin","docAbstract":"Groundwater quality in the Silurian dolomite aquifer in northeastern Wisconsin, USA, has become contentious as dairy farms and exurban development expand.
We investigated private household wells in the region, determining the extent, sources, and risk factors of nitrate and microbial contamination.
Total coliforms, Escherichia coli, and nitrate were evaluated by synoptic sampling during groundwater recharge and no-recharge periods. Additional seasonal sampling measured genetic markers of human and bovine fecal-associated microbes and enteric zoonotic pathogens. We constructed multivariable regression models of detection probability (log-binomial) and concentration (gamma) for each contaminant to identify risk factors related to land use, precipitation, hydrogeology, and well construction.
Total coliforms and nitrate were strongly associated with depth-to-bedrock at well sites and nearby agricultural land use, but not septic systems. Both human wastewater and cattle manure contributed to well contamination. Rotavirus group A, Cryptosporidium, and Salmonella were the most frequently detected pathogens. Wells positive for human fecal markers were associated with depth-to-groundwater and number of septic system drainfield within
These findings may inform policies to minimize contamination of the Silurian dolomite aquifer, a major water supply for the U.S. and Canadian Great Lakes region.
","language":"English","publisher":"Environmental health Perspectives","doi":"10.1289/EHP7813","usgsCitation":"Borchardt, M.A., Stokdyk, J.P., Kieke, B.A., Muldoon, M.A., Spencer, S.K., Firnstahl, A.D., Bonness, D., Hunt, R., and Burch, T., 2021, Sources and risk factors for nitrate and microbial contamination of private household wells in the fractured dolomite aquifer of northeastern Wisconsin: Environmental Health Perspectives, v. 129, no. 6, 067004, 18 p., https://doi.org/10.1289/EHP7813.","productDescription":"067004, 18 p.","ipdsId":"IP-120190","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":451762,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1289/ehp7813","text":"Publisher Index Page"},{"id":386701,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Kewaunee County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-87.3761,44.6754],[-87.3774,44.674],[-87.381,44.6636],[-87.3858,44.6545],[-87.3911,44.6473],[-87.3944,44.6442],[-87.3966,44.6378],[-87.4045,44.6302],[-87.4085,44.6257],[-87.4137,44.6235],[-87.4223,44.6145],[-87.4263,44.61],[-87.4341,44.6056],[-87.442,44.6011],[-87.4428,44.5934],[-87.4468,44.5893],[-87.4502,44.5816],[-87.4544,44.5721],[-87.4604,44.5622],[-87.4664,44.555],[-87.4738,44.5455],[-87.476,44.5369],[-87.4761,44.5305],[-87.4796,44.5223],[-87.4851,44.5106],[-87.488,44.4974],[-87.4959,44.4706],[-87.5046,44.4575],[-87.5041,44.4534],[-87.5062,44.4457],[-87.5064,44.4375],[-87.5074,44.4279],[-87.5121,44.4188],[-87.5163,44.408],[-87.5191,44.3998],[-87.5212,44.3907],[-87.5209,44.3816],[-87.5218,44.3734],[-87.5232,44.3688],[-87.5279,44.3602],[-87.5351,44.3521],[-87.5386,44.3422],[-87.5368,44.338],[-87.5408,44.3331],[-87.5454,44.3277],[-87.6445,44.3273],[-87.7665,44.3271],[-87.7655,44.4146],[-87.7646,44.5017],[-87.7643,44.5888],[-87.7628,44.6477],[-87.7582,44.6522],[-87.7555,44.6558],[-87.7547,44.6608],[-87.7507,44.6667],[-87.7435,44.673],[-87.7389,44.6775],[-87.6413,44.6757],[-87.5193,44.6753],[-87.4384,44.6754],[-87.3973,44.6753],[-87.3761,44.6754]]]},\"properties\":{\"name\":\"Kewaunee\",\"state\":\"WI\"}}]}","volume":"129","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":151033,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":818173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stokdyk, Joel P. 0000-0003-2887-6277 jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kieke, Burney A","contributorId":195802,"corporation":false,"usgs":false,"family":"Kieke","given":"Burney","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":818175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muldoon, Maureen A.","contributorId":198974,"corporation":false,"usgs":false,"family":"Muldoon","given":"Maureen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":818176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spencer, Susan K.","contributorId":181738,"corporation":false,"usgs":false,"family":"Spencer","given":"Susan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":818177,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Firnstahl, Aaron D. 0000-0003-2686-7596 afirnstahl@usgs.gov","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":168296,"corporation":false,"usgs":true,"family":"Firnstahl","given":"Aaron","email":"afirnstahl@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818178,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bonness, Davina","contributorId":260613,"corporation":false,"usgs":false,"family":"Bonness","given":"Davina","email":"","affiliations":[{"id":52618,"text":"Kewaunee County","active":true,"usgs":false}],"preferred":false,"id":818179,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hunt, Randall J. 0000-0001-6465-9304","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":16118,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818180,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Burch, Tucker R.","contributorId":195801,"corporation":false,"usgs":false,"family":"Burch","given":"Tucker R.","affiliations":[],"preferred":false,"id":818181,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70221582,"text":"70221582 - 2021 - Quantitative microbial risk assessment for contaminated private wells in the fractured dolomite aquifer of Kewaunee County, Wisconsin","interactions":[],"lastModifiedDate":"2021-06-24T14:50:56.03881","indexId":"70221582","displayToPublicDate":"2021-06-23T09:46:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1542,"text":"Environmental Health Perspectives","active":true,"publicationSubtype":{"id":10}},"title":"Quantitative microbial risk assessment for contaminated private wells in the fractured dolomite aquifer of Kewaunee County, Wisconsin","docAbstract":"Private wells are an important source of drinking water in Kewaunee County, Wisconsin. Due to the region’s fractured dolomite aquifer, these wells are vulnerable to contamination by human and zoonotic gastrointestinal pathogens originating from land-applied cattle manure and private septic systems.
We determined the magnitude of the health burden associated with contamination of private wells in Kewaunee County by feces-borne gastrointestinal pathogens.
This study used data from a year-long countywide pathogen occurrence study as inputs into a quantitative microbial risk assessment (QMRA) to predict the total cases of acute gastrointestinal illness (AGI) caused by private well contamination in the county. Microbial source tracking was used to associate predicted cases of illness with bovine, human, or unknown fecal sources.
Results suggest that private well contamination could be responsible for as many as 301 AGI cases per year in Kewaunee County, and that 230 and 12 cases per year were associated with a bovine and human fecal source, respectively. Furthermore, Cryptosporidium parvum was predicted to cause 190 cases per year, the most out of all 8 pathogens included in the QMRA.
This study has important implications for land use and water resource management in Kewaunee County and informs the public health impacts of consuming drinking water produced in other similarly vulnerable hydrogeological settings.
","language":"English","doi":"10.1289/EHP7815","usgsCitation":"Burch, T., Stokdyk, J.P., Spencer, S.K., Kieke, B.A., Firnstahl, A.D., Muldoon, M.A., and Borchardt, M.A., 2021, Quantitative microbial risk assessment for contaminated private wells in the fractured dolomite aquifer of Kewaunee County, Wisconsin: Environmental Health Perspectives, v. 129, no. 6, 067003, 9 p., https://doi.org/10.1289/EHP7815.","productDescription":"067003, 9 p.","ipdsId":"IP-120047","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":451765,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1289/ehp7815","text":"Publisher Index Page"},{"id":386700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Kewaunee County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-87.3761,44.6754],[-87.3774,44.674],[-87.381,44.6636],[-87.3858,44.6545],[-87.3911,44.6473],[-87.3944,44.6442],[-87.3966,44.6378],[-87.4045,44.6302],[-87.4085,44.6257],[-87.4137,44.6235],[-87.4223,44.6145],[-87.4263,44.61],[-87.4341,44.6056],[-87.442,44.6011],[-87.4428,44.5934],[-87.4468,44.5893],[-87.4502,44.5816],[-87.4544,44.5721],[-87.4604,44.5622],[-87.4664,44.555],[-87.4738,44.5455],[-87.476,44.5369],[-87.4761,44.5305],[-87.4796,44.5223],[-87.4851,44.5106],[-87.488,44.4974],[-87.4959,44.4706],[-87.5046,44.4575],[-87.5041,44.4534],[-87.5062,44.4457],[-87.5064,44.4375],[-87.5074,44.4279],[-87.5121,44.4188],[-87.5163,44.408],[-87.5191,44.3998],[-87.5212,44.3907],[-87.5209,44.3816],[-87.5218,44.3734],[-87.5232,44.3688],[-87.5279,44.3602],[-87.5351,44.3521],[-87.5386,44.3422],[-87.5368,44.338],[-87.5408,44.3331],[-87.5454,44.3277],[-87.6445,44.3273],[-87.7665,44.3271],[-87.7655,44.4146],[-87.7646,44.5017],[-87.7643,44.5888],[-87.7628,44.6477],[-87.7582,44.6522],[-87.7555,44.6558],[-87.7547,44.6608],[-87.7507,44.6667],[-87.7435,44.673],[-87.7389,44.6775],[-87.6413,44.6757],[-87.5193,44.6753],[-87.4384,44.6754],[-87.3973,44.6753],[-87.3761,44.6754]]]},\"properties\":{\"name\":\"Kewaunee\",\"state\":\"WI\"}}]}","volume":"129","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burch, Tucker R.","contributorId":195801,"corporation":false,"usgs":false,"family":"Burch","given":"Tucker R.","affiliations":[],"preferred":false,"id":818182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stokdyk, Joel P. 0000-0003-2887-6277 jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spencer, Susan K.","contributorId":210972,"corporation":false,"usgs":false,"family":"Spencer","given":"Susan","email":"","middleInitial":"K.","affiliations":[{"id":38162,"text":"United States Department of Agriculture Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":818184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kieke, Burney A","contributorId":195802,"corporation":false,"usgs":false,"family":"Kieke","given":"Burney","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":818185,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Firnstahl, Aaron D. 0000-0003-2686-7596 afirnstahl@usgs.gov","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":168296,"corporation":false,"usgs":true,"family":"Firnstahl","given":"Aaron","email":"afirnstahl@usgs.gov","middleInitial":"D.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818186,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muldoon, Maureen A.","contributorId":198974,"corporation":false,"usgs":false,"family":"Muldoon","given":"Maureen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":818187,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":210973,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":38162,"text":"United States Department of Agriculture Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":818188,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229073,"text":"70229073 - 2021 - Ecological engineering with oysters enhances coastal resilience efforts","interactions":[],"lastModifiedDate":"2022-03-01T12:07:46.755897","indexId":"70229073","displayToPublicDate":"2021-06-23T09:32:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1454,"text":"Ecological Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Ecological engineering with oysters enhances coastal resilience efforts","docAbstract":"Coastal areas are especially vulnerable to habitat loss, sea-level rise, and other climate change effects. Oyster-dominated eco-engineered reefs have been promoted as integral components of engineered habitats enhancing coastal resilience through provision of numerous ecological, morphological, and socio-economic services. However, the assessed ‘success’ of these eco-engineered oyster reefs remains variable across projects and locations, with their general efficacy in promoting coastal resilience, along with related services, often mixed at best. Understanding factors influencing the success of these eco-engineered habitats as valuable coastal management tools could greatly inform related future efforts. Here, we review past studies incorporating reef-building oysters for coastal resilience and enhanced ecosystem services. Our aims are to better understand their utility and limitations, along with critical knowledge gaps to better advance future applicability. Success depends largely on site selection, informed by physical, chemical and biological factors, and adjacent habitats and bottom types. Better understanding of oyster metapopulation dynamics, tolerance and adaptation to changing conditions, and interactions with adjacent habitats will help to better identify suitable locations, and design more effective eco-engineered reefs. These eco-engineered reefs provide a useful tool to assist in developing coastal resilience in the face of climate change and sea level rise.
The U.S. Geological Survey (USGS) has been using its Circular 891 for evaluating uncertainty in coal resource assessments for more than 35 years. Calculated cell tonnages are assigned to four qualitative reliability classes depending exclusively on distance to the nearest drill hole. The main appeal of this methodology, simplicity, is also its main drawback. Reliability may depend so marginally on distance to the nearest drill hole that, over time, it has become evident that Circular 891 is inadequate for modeling reliability and is limited by other shortcomings. The present publication describes the use of geostatistics as an approach allowing a more satisfactory performance than that which is achieved following Circular 891. Geostatistics takes advantage of partly random and partly organized fluctuations in attributes such as coal thickness, coal density, and elevation of the top of a coal bed, borrowing concepts and tools that have been standard features in statistics and risk analysis for decades. Considering that readers interested in this study may not have the background to go directly into the details of the methodology, we start by explaining geostatistical concepts and modeling techniques. The remainder of the publication is devoted to formulating the assessment methodology, applying it to data from the Fillmore Ranch coal bed in the Fort Union Formation in Wyoming, and explaining the computer software applied for performing calculations and displays. The assessment methodology has been designed to report three different forms of resources: coal in place, coal mineable by surface mining methods, and coal mineable by underground mining methods. These three types of resources are reported graphically by displaying both the magnitude and the reliability of total coal resources and resources at the cell scale. In the case of the Fillmore Ranch coal bed example, there is a 90-percent probability that the resources in place are 9.687 ± 0.383 billion short tons (bst), while the coal available for underground mining is 2.279 ± 0.160 bst, and that available for surface mining is only 0.240 ± 0.025 bst because of the steep dip to the west away from the outcrop. These magnitudes are derived from numerical probability distributions not following any specific form.
Geology, Energy & Minerals Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 954
Reston, VA 20192
The handbooks and synchronized MP4 recordings provide hands-on instruction for creating and analyzing vegetation live fractional cover (LFC) maps. The methods and protocols used in the instruction materials follow those developed and recorded in Rangoonwala and Ramsey (2019). The LFC mapping and geographic information system (GIS) analyses highlight the consortium of rangeland conservancies covering the semiarid central region of Kenya (approximately 44,000 square kilometers).
The instruction materials are separated into two parts: processing and map-product creation based on remote-sensing images and GIS analyses of the created maps for rangeland management. The image processing is conducted using the advanced and professional software package SeNtinels Application Platform (SNAP) that is supported and maintained by the European Space Agency. SNAP is a free image analyses software package available for download. It is largely icon driven but offers simple to advanced program inserts and batch processing. The GIS analyses are conducted using the software package Quantum Geographic Information System (QGIS), another free and downloadable software. QGIS is compatible with numerous software, including the Esri suite of ArcGIS software and database structure. The image data includes both high-spatial-resolution Sentinel-2 optical data and Sentinel-1 synthetic aperture radar (SAR). Both datasets are freely available via a public portal that is maintained by the European Space Agency.
The image-processing instruction handbook covers all aspects of acquiring and processing satellite-image data and importing vector-data sources into SNAP. The GIS analysis handbook covers final creation of map products from the maps created in SNAP and creation of GIS procedures in QGIS that are needed to manage the rangeland resources for wildlife and pastoral grazing. Although focused on the Kenyan conservancies and their semiarid environment, the processing methods and procedures are applicable for similar environments and management, and to a large part, even for integrated mapping and GIS functionality of any managed landscape resource.
The instruction handbooks are synchronized to MP4 training videos created with U.S. Geological Survey-licensed Camtasia 9 software.
The workbook and MP4 video combinations are suitable for a single user or a workshop setting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211001","collaboration":"Prepared in cooperation with the U.S. Agency for International Development","usgsCitation":"Rangoonwala, A., and Ramsey, E., III, 2021, Grassland live fractional cover map creation and Geographic Information System (GIS) analysis for rangeland management supporting Kenya Northern Rangelands Trust Conservancies: U.S. Geological Survey Report 2021–1001, 59 p., https://doi.org/10.3133/ofr20211001.","productDescription":"Report: v, 59 p.; 2 Companion Files","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119513","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":386330,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1001/coverthb.jpg"},{"id":386331,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001.pdf","text":"Report","size":"5.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1001"},{"id":386397,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_SNAP_video/ofr20211001_SNAP_video_player.html","text":"Section I—SNAP Video Player","linkHelpText":"— SeNtinels Application Platform (SNAP)"},{"id":386334,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_QGIS_video/ofr20211001_QGIS_video_player.html","text":"Section II—QGIS Video Player","description":"OFR 2021–1001 Video Player","linkHelpText":"— Quantum Geographic Information System (QGIS)"},{"id":386578,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_SNAP_video/","text":"Section I—SNAP package"},{"id":391246,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_QGIS_shapefile","text":"Section II—QGIS Shapefiles"},{"id":386579,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_QGIS_video/","text":"Section II—QGIS package"}],"country":"Kenya","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[40.993,-0.85829],[41.58513,-1.68325],[40.88477,-2.08255],[40.63785,-2.49979],[40.26304,-2.57309],[40.12119,-3.27768],[39.80006,-3.68116],[39.60489,-4.34653],[39.20222,-4.67677],[37.7669,-3.67712],[37.69869,-3.09699],[34.07262,-1.05982],[33.90371,-0.95],[33.89357,0.10981],[34.18,0.515],[34.6721,1.17694],[35.03599,1.90584],[34.59607,3.05374],[34.47913,3.5556],[34.005,4.24988],[34.6202,4.84712],[35.29801,5.506],[35.81745,5.33823],[35.81745,4.77697],[36.15908,4.44786],[36.85509,4.44786],[38.12091,3.59861],[38.43697,3.58851],[38.67114,3.61607],[38.89251,3.50074],[39.55938,3.42206],[39.85494,3.83879],[40.76848,4.25702],[41.1718,3.91909],[41.85508,3.91891],[40.98105,2.78452],[40.993,-0.85829]]]},\"properties\":{\"name\":\"Kenya\"}}]}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, Louisiana 70506
The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained solely from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project.
An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. As of 2020, the network consisted of 120 wells and piezometers. A piezometer is a specialized well open to a specific depth in the aquifer, often of small diameter and nested with other piezometers screened at different depths. The USGS, in cooperation with the ABCWUA, the New Mexico Office of the State Engineer, and Bernalillo County, measures water levels from the wells and piezometers in the network; this report, prepared in cooperation with the ABCWUA, presents water-level data collected by USGS personnel at the sites through water year 2020 (October 1, 2019, through September 30, 2020). Water levels that were collected from discontinued wells in previous water years were published in previous USGS reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1139","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Jurney, E.R., and Bell, M.T., 2021, Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2020: U.S. Geological Survey Data Series 1139, 40 p., https://doi.org/10.3133/ds1139.","productDescription":"iv, 40 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-128111","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":386657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1139/coverthb.jpg"},{"id":386658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1139/ds1139.pdf","text":"Report","size":"6.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":386659,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/ds/1139/images"}],"country":"United States","state":"New Mexico","otherGeospatial":"Albuquerque Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.3583984375,\n 34.261756524459805\n ],\n [\n -106.14990234375,\n 34.261756524459805\n ],\n [\n -106.14990234375,\n 35.65729624809628\n ],\n [\n -107.3583984375,\n 35.65729624809628\n ],\n [\n -107.3583984375,\n 34.261756524459805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Between 2012 and 2016, California suffered one of the most severe droughts on record. During this period Sequoiadendron giganteum (giant sequoias) in the Sequoia and Kings Canyon National Parks (SEKI), California, USA experienced canopy water content (CWC) loss, unprecedented foliage senescence, and, in a few cases, death. We present an assessment of the vulnerability of giant sequoia populations to droughts that is currently lacking and needed for management. We used a temporal trend of remotely sensed CWC obtained between 2015 and 2017, and recently georeferenced giant sequoia crowns to quantify the vulnerability of 7,408 individuals in 10 groves in the northern portion of SEKI. CWC is sensitive to changes in liquid water in tree canopies; therefore, it is a useful metric for quantifying the response of sequoia trees to drought. Temporal trends indicated that 9% of giant sequoias had a significant decline or consistently low CWC, suggesting these trees were likely operating at low photosynthetic capacity and potentially at high risk to drought stress. We also found that 20% of the giant sequoias had an increase or consistently high level of CWC, indicating these trees were at low risk to drought stress. These vulnerability categories were used in a random forest model with a combination of topographic, fire-related, and climate variables to generate high-resolution vulnerability risk maps. These maps show that higher risk is associated with lower elevation and higher climate water deficit. We also found that sequoias at higher elevations but located near meadows had higher vulnerability risk. These results and the vulnerability maps can identify vulnerable sequoias that may be difficult to save or locations of refugia to be protected, and thus may aid forest managers in preparation for future droughts.
Conservation actions to safeguard climate change vulnerable species may not be utilized due to a variety of perceived barriers. Assisted colonization, the intentional movement and release of an organism outside its historical range, is one tool available for species predicted to lose habitat under future climate change scenarios, particularly for single island or single mountain range endemic species. Despite the existence of policies that allow for this action, to date, assisted colonization has rarely been utilized for species of conservation concern in the Hawaiian Islands. Given the potential for climate driven biodiversity loss, the Hawaiian Islands are a prime location for the consideration of adaptation strategies. We used first-person interviews with conservation decision makers, managers, and scientists who work with endangered species in the Hawaiian Islands to identify perceived barriers to the use of assisted colonization. We found that assisted colonization was often not considered or utilized due to a lack of expertize with translocations; ecological risk and uncertainty, economic constraints, concerns regarding policies and permitting, concerns with public perception, and institutional resistance. Therefore, conservation planners may benefit from decision tools that integrate risk and uncertainty into decision models, and compare potential outcomes among conservation actions under consideration, including assisted colonization. Within a decision framework that addresses concerns, all conservation actions for climate sensitive species, including assisted colonization, may be considered in a timely manner.
Understanding of morphodynamic processes associated with large-scale floods has recently improved following significant advances of modern technologies. Nevertheless, a clear link between flood discharge and in-channel sedimentation processes remains to be resolved. The hydrological and geomorphological data available for the meandering Powder River (Montana, USA) since 1977 makes it a perfect laboratory to investigate connections between flood discharge and point-bar sedimentation processes. This study focuses on a point-bar that accreted laterally ca 70 m during a 50-year recurrence flood, which lasted about 14 days in May 1978. In September 2018, a trench ca 2 m deep and 70 m long was excavated through the axial point-bar deposits, and the 1978 flood deposits were delineated based on georeferenced pre-flood and post-flood cross-section surveys. Sedimentological data show that point-bar deposits accumulated at the early and late flood stages, when the flow was confined to the channel, and have similarities with classical facies models in terms of palaeocurrent patterns and vertical grain-size trend. However, during high-stage flood conditions, when the flow overtopped the bar, cross-cutting of the bar and armouring were typical processes. Integration of sedimentological and palaeo-hydrological data highlight that the relation between channel cross-sectional area and flood discharge play a key role in preserving bar deposits. The integrated approach adopted here provides a basis for advancing palaeoflood hydrology beyond the stage of estimating peak discharges to the next stage of estimating palaeoflood hydrographs.
An International Virtual Workshop on Global Seismology and Tectonics (IVWGST‐2020) was organized by the Geoscience and Technology Division of Council of Scientific and Industrial Research—North East Institute of Science and Technology, Jorhat, India from 14 to 25 September 2020. This workshop predominantly catered to undergraduate, postgraduate, and Ph.D. students, scientists, and academicians from across the globe. The primary motive of IVWGST-2020 was to inspire the participating students, perturbed by the unprecedented situation brought about by the COVID-19 pandemic, with quality lecture sessions, so as to lift their spirits. The virtual workshop served as a conduit for the students and researchers to directly interact with several pioneers and prominent geoscience researchers from around the world. Lectures, via Microsoft Teams, were given by 15 eminent speakers from diverse geoscience forums and institutions, and were attended by more than 1000 participants, mostly students and researchers, from 30 different countries. This report briefly summarizes the agenda, describes our experiences hosting the virtual workshop, and documents the challenges faced.
Control methods that target specific traits of an invasive species can produce results contrary to the aims of management. If targeted phenotypes exhibit heritability, then it follows that the invasive species could evolve greater resistance to the applied control measures over time. Additional complications emerge if those traits targeted by control are also inversely related to reproductive success. Given this, prudent considerations for invasive species management are to quantify the heritability of traits selected through control measures and gauge their relationship with reproductive success. Herein we provide a case study utilizing long-term field data and a multi-generational pedigree of an experimentally-closed population of brown treesnakes (N = 426; Boiga irregularis) on Guam. We employed an “animal model” to estimate the narrow-sense heritability (h2) for annual body condition, a trait related to both susceptibility to a primary tool used for brown treesnake control (i.e., live-lure traps) and annual reproductive success. Annual body condition displayed significant heritability [h2 = 0.149 (95% highest posterior density interval: 0.059–0.220)]. Considering a negative effect of body condition on susceptibility to trap capture but positive effect on reproductive success, significant heritability of body condition suggests the potential for live-lure traps to lose efficacy over time while also eliciting an undesirable effect on brown treesnake fecundity. Our results highlight the potential for negative repercussions that can stem from management actions, while also serving to underscore the evolutionary implications that are often overlooked but subsumed within invasive species control.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-021-02588-3","usgsCitation":"Levine, B., Douglas, M.R., Yackel Adams, A.A., Lardner, B., Reed, R., Savidge, J.A., and Douglas, M.E., 2021, Trait heritability and its implications for the management of an invasive vertebrate: Biological Invasions, v. 23, p. 3447-3456, https://doi.org/10.1007/s10530-021-02588-3.","productDescription":"10 p.","startPage":"3447","endPage":"3456","ipdsId":"IP-124543","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":386980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","noUsgsAuthors":false,"publicationDate":"2021-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Levine, Brenna A","contributorId":243207,"corporation":false,"usgs":false,"family":"Levine","given":"Brenna A","affiliations":[{"id":38022,"text":"University of Tulsa","active":true,"usgs":false}],"preferred":false,"id":818727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, Marlis R","contributorId":243208,"corporation":false,"usgs":false,"family":"Douglas","given":"Marlis","email":"","middleInitial":"R","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":818728,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":818729,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lardner, Bjorn","contributorId":225066,"corporation":false,"usgs":false,"family":"Lardner","given":"Bjorn","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":818730,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":818731,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Savidge, Julie A.","contributorId":175196,"corporation":false,"usgs":false,"family":"Savidge","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":818732,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Douglas, Michael E","contributorId":243209,"corporation":false,"usgs":false,"family":"Douglas","given":"Michael","email":"","middleInitial":"E","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":818733,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228553,"text":"70228553 - 2021 - Assessing the robustness of time-to-event models for estimating unmarked wildlife abundance using remote cameras","interactions":[],"lastModifiedDate":"2022-02-14T20:47:10.973293","indexId":"70228553","displayToPublicDate":"2021-06-22T15:46:21","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the robustness of time-to-event models for estimating unmarked wildlife abundance using remote cameras","docAbstract":"Recently developed methods, including time-to-event and space-to-event models, estimate the abundance of unmarked populations from encounter rates with camera trap arrays, addressing a gap in noninvasive wildlife monitoring. However, estimating abundance from encounter rates relies on assumptions that can be difficult to meet in the field, including random movement, population closure, and an accurate estimate of movement speed. Understanding how these models respond to violation of these assumptions will assist in making them more applicable in real-world settings. We used simulated walk models to test the effects of violating the assumptions of the time-to-event model under four scenarios: (1) incorrectly estimating movement speed, (2) violating closure, (3) individuals moving within simplified territories (i.e., movement restricted to partially overlapping circles), (4) and individuals clustering in preferred habitat. The time-to-event model was robust to closure violations, territoriality, and clustering when cameras were placed randomly. However, the model failed to estimate abundance accurately when movement speed was incorrectly estimated or cameras were placed nonrandomly with respect to habitat. We show that the time-to-event model can provide unbiased estimates of abundance when some assumptions that are commonly violated in wildlife studies are not met. Having a robust method for estimating the abundance of unmarked populations with remote cameras will allow practitioners to monitor a more diverse array of populations noninvasively. With the time-to-event model, placing cameras randomly with respect to animal movement and accurately estimating movement speed allows unbiased estimation of abundance. The model is robust to violating the other assumptions we tested.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2388","usgsCitation":"Loonam, K.E., Lukacs, P.M., Ausband, D.E., Mitchell, M.S., and Robinson, H., 2021, Assessing the robustness of time-to-event models for estimating unmarked wildlife abundance using remote cameras: Ecological Applications, v. 31, no. 6, e02388, 11 p., https://doi.org/10.1002/eap.2388.","productDescription":"e02388, 11 p.","ipdsId":"IP-117335","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-07-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Loonam, Kenneth E.","contributorId":276117,"corporation":false,"usgs":false,"family":"Loonam","given":"Kenneth","email":"","middleInitial":"E.","affiliations":[{"id":48645,"text":"umt","active":true,"usgs":false}],"preferred":false,"id":834570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lukacs, Paul M.","contributorId":101240,"corporation":false,"usgs":true,"family":"Lukacs","given":"Paul","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":834571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ausband, David Edward 0000-0001-9204-9837","orcid":"https://orcid.org/0000-0001-9204-9837","contributorId":275329,"corporation":false,"usgs":true,"family":"Ausband","given":"David","email":"","middleInitial":"Edward","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834572,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mitchell, Michael S. 0000-0002-0773-6905 mmitchel@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-6905","contributorId":3716,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"mmitchel@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834569,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robinson, Hugh S.","contributorId":276113,"corporation":false,"usgs":false,"family":"Robinson","given":"Hugh S.","affiliations":[{"id":48645,"text":"umt","active":true,"usgs":false}],"preferred":false,"id":834573,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}