Long-term environmental monitoring surveys are designed to achieve a desired precision (measured by variance) of resource conditions based on natural variability information. Over time, increases in resource variability and in data use to address issues focused on small areas with limited sample sizes require bolstering of attainable precision. It is often prohibitive to do this by increasing sampling effort. In cases with spatially overlapping monitoring surveys, composite estimation offers a statistical way to obtain a precision-weighted combination of survey estimates to provide improved population estimates (more accurate) with improved precisions (lower variances). We present a composite estimator for overlapping surveys, a summary of compositing procedures, and a case study to illustrate the procedures and benefits of composite estimation. The study uses the two terrestrial monitoring surveys administered by the Bureau of Land Management (BLM) that entirely overlap. Using 2015–18 data and 13 land-health indicators, we obtained and compared survey and composite indicator estimates of percent area meeting land-health standards for sagebrush communities in Wyoming’s Greater Sage-Grouse (Centrocercus urophasianus) Core and NonCore conservation areas on BLM-managed lands. We statistically assessed differences in indicator estimates between the conservation areas using composite estimates and estimates of the two surveys individually. We found composite variance to be about six to 24 units lower than 37% of the survey variances and composite estimates to differ by about six to 10 percentage points from six survey estimates. The composite improvements resulted in finding 11 indicators to statistically differ (p <0.05) between the conservation areas compared to only six and seven indicators for the individual surveys. Overall, we found composite estimation to be an efficient and useful option for improving environmental monitoring information where two surveys entirely overlap and suggest how this estimation method could be beneficial where environmental surveys partially overlap and in small area applications.
The City of Tucson water utility, Tucson Water, began releasing treated effluent into the Santa Cruz River channel near downtown Tucson in 2019. This recharge project—the Heritage Project—is intended to create a reach of consistent flow in the channel and recharge water to the aquifer. Tracking the dispersal of recharged water is important for management decisions because groundwater movement depends on spatially variable characteristics of the subsurface and cannot be fully predicted in advance. Groundwater-level measurements in wells are useful, but the relation between water storage and groundwater-level change depends on the unknown storage coefficient of the aquifer. To estimate storage changes caused by recharge of reclaimed effluent released into the channel for the Heritage Project, the U.S. Geological Survey (USGS) collected repeat microgravity data along the Santa Cruz River in Tucson, Arizona, from 2019 to 2022. This method augments groundwater-level monitoring by providing a direct quantitative measurement of changes in the quantity of water stored in the subsurface.
Preliminary results of the monitoring through 2022 showed consistent storage increases only near and upstream from the Heritage Project outfall site. Initially high storage increases at some locations west of the channel and in line with Sentinel Peak reached roughly steady state in later times. North of Sentinel Peak, a storage increase from 2020 to 2021 was followed by a storage decrease from 2021 to 2022. Storage changes in the area north of Sentinel Peak appear to be related to the number of days flows in the channel were observed farther downstream from the outfall site (at USGS streamgage 09482500). This observation is likely due to the potential formation of a clogging layer that would allow surface water to disperse farther horizontally (downstream) before infiltrating. This phenomenon has been observed downstream of other recharged effluent projects and has been reduced by large flows in the channel, such as those occurring during large runoff events. There were no large or consistent storage increases near the Water Quality Assurance Revolving Fund (WQARF) sites included in this study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235115","collaboration":"Prepared in cooperation with Tucson Water, a department of the City of Tucson","usgsCitation":"Wildermuth, L.M., and Conrad, J.L., 2024, Monitoring aquifer-storage change from artificial recharge with repeat microgravity along Santa Cruz River, Tucson, Arizona, 2019–22: U.S. Geological Survey Scientific Investigations Report 2023–5115, 20 p., https://doi.org/10.3133/sir20235115.","productDescription":"Report: v, 20 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-142205","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":426842,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5115/sir20235115.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":426841,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5115/covrthb.jpg"},{"id":426838,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AFRZDF","text":"USGS Data Release","description":"Wildermuth, L.M., and Conrad, J.L., 2023, Repeat microgravity data from Santa Cruz River, Tucson, Arizona, 2019– 2022: U.S. Geological Survey data release, https://doi.org/10.5066/P9AFRZDF.","linkHelpText":"Repeat microgravity data from Santa Cruz River, Tucson, Arizona, 2019– 2022"},{"id":499385,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116175.htm","linkFileType":{"id":5,"text":"html"}},{"id":426845,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235115/full"},{"id":426844,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5115/images"},{"id":426843,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5115/sir20235115.xml"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.92595968619914,\n 32.858435478248595\n ],\n [\n -111.92595968619914,\n 31.25702551496481\n ],\n [\n -110.04729757682384,\n 31.25702551496481\n ],\n [\n -110.04729757682384,\n 32.858435478248595\n ],\n [\n -111.92595968619914,\n 32.858435478248595\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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U.S. Geological Survey
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The U.S. Geological Survey, in cooperation with the National Marine Sanctuary Program of the National Oceanic and Atmospheric Administration, has conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary (SBNMS) region since 1993. The area being mapped using geophysical and geological data includes the SBNMS and the surrounding region, which totals approximately 3,700 square kilometers (km2) and is subdivided into 18 quadrangles. The seabed is a glaciated terrain that is topographically and texturally diverse. Quadrangle 5, the subject of this scientific investigations map, has an area of 211 km2 and has water depths that range from 23 meters (m) on the Stellwagen Bank crest to 105 m in the Stellwagen Basin. Seven map types, each at a scale of 1:25,000, depict seabed topography, ruggedness, backscatter intensity, distribution of geologic substrates, sediment mobility, distribution of fine- and coarse-grained sand, and substrate mud content. These maps show the distribution of geologic substrates on the crest and western flank of the south-central part of Stellwagen Bank and in Stellwagen Basin to the west. Interpretations of multibeam sonar bathymetric and seabed backscatter imagery, photographs, video imagery, and grain-size analyses were used to create the geology-based maps. Data from 729 stations were analyzed, including 620 sediment samples. The geologic substrate maps of quadrangle 5 show the distribution of 20 substrates that represent a wide range of textures, such as mobile and rippled sand, immobile sand, sand that partially veneers gravel, boulder ridges, and mud. Mapped substrates are characterized by sediment grain-size composition, surface morphology, substrate layering, the mobility or immobility of substrate surfaces, and water depth range. This scientific investigations map portrays the major geological elements (substrates, topographic features, and processes) of environments in quadrangle 5. It is intended to provide a foundation for research into present and past sediment transport processes in a complex terrain, provide insights into the ecological requirements of invertebrate and vertebrate species that utilize the various substrates, and to support seabed management in the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3515","collaboration":"Prepared in cooperation with the National Oceanic and Atmospheric Administration","programNote":"Coastal/Marine Hazards and Resources Program","usgsCitation":"Valentine, P.C., and Cross, V.A., 2024, Seabed maps showing topography, ruggedness, backscatter intensity, sediment mobility, and the distribution of geologic substrates in quadrangle 5 of the Stellwagen Bank National Marine Sanctuary region offshore of Boston, Massachusetts: U.S. Geological Survey Scientific Investigations Map 3515, 8 sheets, scale 1:25,000, 27-p. pamphlet, https://doi.org/10.3133/sim3515.","productDescription":"Pamphlet: v, 27 p.; 8 Sheets: 26.96 × 32.43 inches or smaller; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-082905","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science 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Ridges"},{"id":426641,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3515/sim3515.XML"},{"id":426640,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3515/coverthb.jpg"},{"id":426646,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3515/sim3515_mapC.pdf","text":"Map C","size":"26.1 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Backscatter Intensity and Sun-Illuminated Topography"},{"id":426647,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3515/sim3515_mapD1.pdf","text":"Map D, Sheet 1","size":"2.33 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Distribution of Geologic Substrates: Seabed geology and station data types"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 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384 Woods Hole Road
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Woods Hole, MA 02543–1598
Fire is a critical driver of giant sequoia (Sequoiadendron giganteum [Lindl.] Buchholz) regeneration. However, fire suppression combined with the effects of increased temperature and severe drought has resulted in fires of an intensity and size outside of the historical norm. As a result, recent mega-fires have killed a significant portion of the world's sequoia population (13%–19%), and uncertainty surrounds whether severely affected groves will be able to recover naturally, potentially leading to a loss of grove area. To assess the likelihood of natural recovery, we collected spatially explicit data assessing mortality, crown condition, and regeneration within four giant sequoia groves that were severely impacted by the SQF- (2020) and KNP-Complex (2021) wildfires within Sequoia and Kings Canyon National Parks. In total, we surveyed 5.9 ha for seedlings and assessed the crown condition of 1104 giant sequoias. To inform management, we used a statistical methodology that robustly quantifies the uncertainty in inherently “noisy” seedling data and takes advantage of readily available remote sensing metrics that would make our findings applicable to other recently burned groves. A loss of giant sequoia grove area would be a consequence of giant sequoia tree mortality followed by a failure of natural regeneration. We found that areas that experienced very high-severity fire (above ~800 RdNBR) are at substantial risk for the loss of grove area, with tree mortality rapidly increasing and giant sequoia seedling density simultaneously decreasing with fire severity. Such high-severity areas comprised 17.8, 142.0, 14.6, 1.6 ha and ~90%, ~14%, ~53%, and ~27% of Board Camp, Redwood Mountain, Suwanee, and New Oriole Lake groves, respectively. In all sampling areas, we found that seedling densities fell far below the average density measured after prescribed fires, where seedling numbers were almost certainly adequate to maintain giant sequoia populations and postfire conditions were more in keeping with historical norms. Importantly, spatial pattern is also important in assessing the risk of grove loss, and in two groves, Suwanee and New Oriole Lake, the high-severity patches were not always contiguous, potentially making some areas more resilient to regeneration failure due to the proximity of surviving trees.
Communities throughout Massachusetts face the potential effects of climate change, ranging from more extreme rainfall to more pronounced and frequent droughts. Understanding the effects of climate change on hydrology is important to State and community officials to evaluate the potential effects on infrastructure and water systems. To better understand the effects of climate change on hydrology, the U.S. Geological Survey, in partnership with Cornell University and Tufts University, conducted a study in cooperation with the Massachusetts Executive Office of Energy and Environmental Affairs to develop tools for projecting 21st-century climate and hydrologic characteristics in Massachusetts.
A stochastic weather generator was developed to project future climatic characteristics for Massachusetts. The stochastic weather generator estimates daily precipitation, minimum temperature, and maximum temperature for 17 warming scenarios (from 0 to 8 degrees Celsius, in 0.5-degree increments). To project future hydrologic characteristics, the stochastic weather generator output data were input to the Precipitation-Watershed Modeling System deterministic watershed model for the Squannacook River watershed, which is the watershed selected as the pilot study location for investigating future hydrologic characteristics. Hydrologic data output from the deterministic watershed model were then input to a stochastic watershed model developed for this study to correct model errors (model errors are often observed in the output from deterministic models at the high- and low-flow extremes). The output from the stochastic watershed model was then used to characterize hydrology for the 17 warming scenarios. For the Squannacook River watershed, the results project more extreme flood and low streamflows under the warming scenarios.
Output from the tools allows the characterization of future streamflows for the years 2030, 2050, 2070, and 2090, which expands our understanding of 21st-century climatic and hydrologic risk in Massachusetts. These tools could improve Federal, State, and community officials’ ability to mitigate the effects of climate change over the next several decades.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235134","collaboration":"Prepared in cooperation with the Massachusetts Executive Office of Energy and Environmental Affairs","usgsCitation":"Olson, S.A., Shabestanipour, G., Lamontagne, J., and Steinschneider, S., 2024, Characterizing future streamflows in Massachusetts using stochastic modeling—A pilot study: U.S. Geological Survey Scientific Investigations Report 2023–5134, 19 p., https://doi.org/10.3133/sir20235134.","productDescription":"Report: v, 19 p.; Data Release","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-149673","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":499396,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116176.htm","linkFileType":{"id":5,"text":"html"}},{"id":426599,"rank":6,"type":{"id":30,"text":"Data 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7, 8, and 9 for quarter 3 (July–September) of 2023. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
This is the first quarterly report to include analysis results for Landsat 9, which was launched in September 2021. The inclusion of Landsat 9 analysis results was dependent on two factors: a complete reprocessing of the Landsat 9 data archive and enough time elapsing to begin formulating lifetime trends. In April 2023, all Landsat 9 image data acquired since the satellite’s launch were reprocessed to take advantage of calibration updates identified by the ECCOE Landsat Cal/Val Team. Additional information about the Landsat 9 reprocessing effort is available at https://www.usgs.gov/landsat-missions/news/upcoming-reprocessing-all-landsat-9-data.
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U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The U.S. Geological Survey (USGS) collected data on the vertical distribution of hydraulic head, specific capacity, and water quality using aquifer-interval-isolation tests and other vertical profiling methods in 15 boreholes completed in fractured sedimentary bedrock in Northampton, Warminster, and Warwick Townships, Bucks County, Pennsylvania during 2018–19. This work was done, in cooperation with the U.S. Navy, to support detailed investigations at and near the former Naval Air Warfare Center (NAWC) Warminster, where groundwater contamination with per- and polyfluoroalkyl substances (PFAS) had become a concern since 2014. Two PFAS compounds, perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA), have been measured in groundwater samples from supply and monitoring wells at or near NAWC Warminster in concentrations above U.S. Environmental Protection Agency health advisory levels for drinking water. The area is underlain by the Triassic Stockton Formation, which predominantly consists of sandstone interbedded with shale and siltstone beds and forms a layered fractured-rock aquifer used for private, industrial, and public drinking water supply.
The vertical distribution of aquifer properties and water quality was assessed through hydraulic tests and sampling of aquifer intervals using a straddle-packer system (13 boreholes) or depth-discrete point sampling under known borehole-flow conditions (2 boreholes). Geophysical and video logs collected by USGS during 2017–19 were used to identify potential water-bearing fractures in 15 boreholes, which ranged in depth from 210 to 604 feet (ft) and included 6 boreholes drilled in 2018 and 9 existing wells on or near the former NAWC Warminster. Measured borehole flow was predominantly downward in most of the deepest boreholes (greater than 400 ft), which were commonly located at the highest land-surface elevations, with inflow from fractures at relatively shallow depths and outflow through fractures near or below depths of 500 ft below land surface. Hydraulic head differences measured during packer tests were up to about 60 ft between shallow and deep intervals. Borehole flow was predominantly upward in most boreholes less than 400 ft in depth and farther from, and at lower land-surface elevations than, the former NAWC Warminster. Total borehole specific capacity ranged from about 0.07 to 41 gallons per minute per foot [(gal/min)/ft]. Specific-capacity values for individual intervals ranged from 0.02 to 40.0 (gal/min)/ft, with a median of 1.14 (gal/min)/ft and a large range in values at most depths.
Differences in water quality of samples as indicated by field properties (pH, dissolved oxygen, and specific conductance) and concentrations of dissolved major ions, PFOA, and PFOS were apparent among isolated intervals in the boreholes. Summed concentrations of PFOA and PFOS ranged from about 11 to 10,780 nanograms per liter (ng/L) and were greater than the 2016 U.S. Environmental Protection Agency health advisory of 70 ng/L for summed PFOA and PFOS concentrations in 62 of 104 intervals and discrete depths tested. The mass ratio of PFOS to PFOA was generally higher than 1.0 in samples with summed PFOA and PFOS concentrations greater than 70 ng/L, with ratio values as high as 8.7. In many boreholes, summed concentrations of PFOA and PFOS were positively related to chloride concentrations, which were elevated above natural-background values [less than 10 milligrams per liter] in most samples and as high as 717 milligrams per liter. Sources of the elevated chloride other than, or in addition to, common rock salt (sodium chloride) were indicated by chloride to sodium molar ratios greater than 1.0. Water-quality data indicated that sampled water from some intervals with lower hydraulic heads may be affected by water from intervals with higher hydraulic heads because of vertical flow in open boreholes; samples from these intervals with lower hydraulic heads may not be fully representative due to some component of cross contamination and should be interpreted with caution.
Through a preliminary correlation of natural gamma and resistivity logs of boreholes drilled at and near the former NAWC Warminster, 11 lithologic units were identified and interpreted to strike northeast and dip to the northwest. Hydraulic heads were generally highest in isolated intervals that intercepted beds which, when projected up dip, crop out at the highest land-surface elevation on the former NAWC Warminster, indicating that the dipping-bed structure and topography are factors affecting the distribution of hydraulic head in the aquifer. The hydrogeologic framework in conjunction with the vertical distribution of hydraulic heads and water quality may assist in evaluating the locations of various PFAS sources and potential migration pathways of PFAS in groundwater at and near NAWC Warminster.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241007","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Senior, L.A., and Fiore, A.R., 2024, Results of 2018–19 water-quality and hydraulic characterization of aquifer intervals using packer tests and preliminary geophysical-log correlations for selected boreholes at and near the former Naval Air Warfare Center Warminster, Bucks County, Pennsylvania (ver. 1.1, January 2025): U.S. Geological Survey Open-File Report 2024–1007, 136 p., https://doi.org/10.3133/ofr20241007.","productDescription":"Report: xv, 136 p.; 5 Plates; Data Release","numberOfPages":"136","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-138405","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":426405,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241007/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1007 HTML"},{"id":426406,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1007/ofr20241007.XML","description":"OFR 2024-1007 XML"},{"id":426407,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1007/images/"},{"id":426403,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1007/coverthb2.jpg"},{"id":426404,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1007/ofr20241007.pdf","text":"Report","size":"9.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1007 PDF"},{"id":426408,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TC92B5","text":"USGS data release","linkHelpText":"Water-level data and selected field notes for aquifer-interval-isolation tests at and near the former Naval Air Warfare Center Warminster, Bucks County, Pennsylvania, 2018–19 (ver. 2.0, January 2024)"},{"id":426409,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2024/1007/ofr20241007_plates.pdf","text":"Plates 1–5","size":"921 KB"},{"id":481558,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2024/1007/ofr20241007_versionHist.txt","size":"949 B","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Naval Air Warfare Center Warminster","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.21874919403015,\n 40.292862181975266\n ],\n [\n -75.21874919403015,\n 40.12697956762551\n ],\n [\n -74.97075997042653,\n 40.12697956762551\n ],\n [\n -74.97075997042653,\n 40.292862181975266\n ],\n [\n -75.21874919403015,\n 40.292862181975266\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: March 2024; Version 1.1 January 2025","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
This report is an update to the presentation by Schulz (1989) introducing potential users to the creepmeter data collected between the publication of Schulz’s report and mid-2020. The creepmeter network monitors aseismic, surface slip at various locations on the Hayward, Calaveras, and San Andreas Faults in northern and central California. There are different designs of creepmeters and these are briefly described. For a majority of the creepmeters, these data are automatically sent to the U.S. Geological Survey (USGS) offices where they are stored and processed. In addition, for most of the creepmeters, occasional manual measurements are made and these are compared with digitally recorded data. For some sites, the comparisons indicated degradation of the electronic sensor and consequently corrections are made to the digital data. The largest transient deformation is that which followed the 2004, M6, Parkfield earthquake. Various functions found in the literature that have been used to model postseismic slip were tested with the observed postseismic behavior seen on the creepmeters in the vicinity of Parkfield, California. No single function adequately fit all the data from these Parkfield instruments. This report is a discussion and analysis of data from creepmeters deployed by the USGS. The discussion primarily focuses on instruments that are currently operating in 2020 or have operated quite recently but are no longer in service.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241011","usgsCitation":"Langbein, J., Bilham, R.G., Snyder, H.A., and Ericksen, T., 2024, Summary of Creepmeter Data from 1980 to 2020—Measurements Spanning the Hayward, Calaveras, and San Andreas Faults in Northern and Central California: U.S. Geological Survey Report 2024–1011, 110 p., https://doi.org/10.3133/ofr20241011.","productDescription":"vi, 110 p.","numberOfPages":"110","onlineOnly":"Y","ipdsId":"IP-143918","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":499206,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116172.htm","linkFileType":{"id":5,"text":"html"}},{"id":426750,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1011/ofr20241011.pdf","text":"Report","size":"60 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":426749,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1011/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.83784784258054,\n 37.99394764431494\n ],\n [\n -122.83784784258054,\n 34.52234572819374\n ],\n [\n -119.36616815508066,\n 34.52234572819374\n ],\n [\n -119.36616815508066,\n 37.99394764431494\n ],\n [\n -122.83784784258054,\n 37.99394764431494\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
350 N. Akron Rd.
Moffett Field, CA 94035
A massive bloom of the raphidophyte Heterosigma akashiwo occurred in summer 2022 in San Francisco Bay, causing widespread ecological impacts including events of low dissolved oxygen and mass fish kills. The rapidly evolving bloom required equally rapid management response, leading to the use of near-real-time image analysis of chlorophyll from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3. Standard algorithms failed to adequately capture the bloom, signifying a need to refine a two-band algorithm developed for coastal and inland waters that relates the red-edge part of the remote sensing reflectance spectrum to chlorophyll. While the bloom was the initial motivation for optimizing this algorithm, an extensive dataset of in-water validation measurements from both bloom and non-bloom periods was used to evaluate performance over a range of concentrations and community composition. The modified red-edge algorithm with a simplified atmospheric correction scheme outperformed existing standard products across diverse conditions, and given the modest computational requirements, was found suitable for operational use and near-real-time product generation. The final version of the algorithm successfully minimizes error for non-bloom periods when chlorophyll a is typically <30 mg m−3, while also capturing bloom periods of >100 mg m−3 chlorophyll a.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16061103","usgsCitation":"Kudela, R.M., Senn, D.B., Richardson, E.T., Bouma-Gregson, K., Bergamaschi, B.A., and Sim, L., 2024, Evaluation and refinement of chlorophyll-a algorithms for high-biomass blooms in San Francisco Bay (USA): Remote Sensing, v. 16, no. 6, 1103, 15 p., https://doi.org/10.3390/rs16061103.","productDescription":"1103, 15 p.","ipdsId":"IP-160723","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":440089,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16061103","text":"Publisher Index Page"},{"id":435018,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GXJHZ3","text":"USGS data release","linkHelpText":"Assessing spatial variability of nutrients, phytoplankton, and related water-quality constituents in the San Francisco Bay, California: 2021-2022 High-resolution mapping surveys"},{"id":427314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.07594813067364,\n 37.409521554962424\n ],\n [\n -122.01459141030173,\n 37.543473010939294\n ],\n [\n -122.3138751078345,\n 37.9439399615851\n ],\n [\n -122.2371288097209,\n 38.076947981745235\n ],\n [\n -122.4289707157553,\n 38.1493978144897\n ],\n [\n -122.49802549540809,\n 38.08298835733879\n ],\n [\n -122.5286656402067,\n 37.93788689033987\n ],\n [\n -122.4673236665621,\n 37.792501512759955\n ],\n [\n -122.35216906140471,\n 37.610380642182776\n ],\n [\n -122.07594813067364,\n 37.409521554962424\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Kudela, Raphael M.","contributorId":205181,"corporation":false,"usgs":false,"family":"Kudela","given":"Raphael","email":"","middleInitial":"M.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":897798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senn, David B.","contributorId":205182,"corporation":false,"usgs":false,"family":"Senn","given":"David","email":"","middleInitial":"B.","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":897799,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richardson, Emily T. 0000-0003-2696-8266","orcid":"https://orcid.org/0000-0003-2696-8266","contributorId":304430,"corporation":false,"usgs":true,"family":"Richardson","given":"Emily","email":"","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897800,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bouma-Gregson, Keith 0000-0002-0304-6034","orcid":"https://orcid.org/0000-0002-0304-6034","contributorId":311235,"corporation":false,"usgs":true,"family":"Bouma-Gregson","given":"Keith","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897801,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897802,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sim, Lawrence","contributorId":168731,"corporation":false,"usgs":false,"family":"Sim","given":"Lawrence","email":"","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":897803,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252931,"text":"70252931 - 2024 - Analysis of mitochondrial DNA sequence data demonstrates that monophyly of myotis occultus is complicated by greater sampling of myotis lucifugus","interactions":[],"lastModifiedDate":"2024-04-11T11:48:04.087404","indexId":"70252931","displayToPublicDate":"2024-03-18T06:45:03","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3451,"text":"Southwestern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Analysis of mitochondrial DNA sequence data demonstrates that monophyly of myotis occultus is complicated by greater sampling of myotis lucifugus","docAbstract":"The validity of Myotis occultus as a species unique from Myotis lucifugus has been a source of debate. Most recently, many authorities treat M. occultus as a distinct species, at least in part because a previous study showed that M. occultus and M. l. carissima (the subspecies that occurs in closest geographic proximity to M. occultus) form separate monophyletic clades based on sequences of two mitochondrial genes (cytochrome-b [cytb] and cytochrome oxidase subunit II [COII]). We re-evaluated the phylogenetic relationship between M. occultus and M. lucifugus based on mitochondrial sequences using an expanded dataset of cytb and COII sequences that originated from more genetically diverse specimens of M. lucifugus collected across a broader geographic area. Based on a phylogenetic analysis, we found that M. occultus sublineages embedded within a well-supported clade that included some specimens of M. lucifugus. These results indicate that the previous genetic analysis demonstrating that M. occultus and M. lucifugus form distinct monophyletic groups is unsupported by our larger dataset. Future research will likely need to focus on genetic work involving whole-genome sequencing of nuclear DNA to better resolve the true taxonomic relationship between M. occultus and M. lucifugus.
La valides de Myotis occultus como una especie distinta a Myotis lucifugus ha sido fuente de debate. Recientemente, muchas autoridades han considerado M. occultus como una especie diferente, en parte porque un estudio anterior mostró que M. occultus y M. l. carissima (la subespecie con la mayor proximidad geográfica a M. occultus) forman clados monofiléticos separados basados en secuencias de dos genes mitocondriales (el citocromo-b [cytb] y la subunidad II de citocromo oxidasa [COII]). Nosotros hemos reevaluado la relación filogenética entre M. occultus y M. lucifugus usando una ampliada colección de datos que contiene secuencias de los genes mitocondriales cytb y COII de especímenes de M. lucifugus genéticamente más diversos que fueron muestreados en un área geográfica más extensa. Nuestro análisis filogenético muestra que los sublinajes de M. occultus están incrustados dentro de un clado bien respaldado que incluye algunos especímenes de M. lucifugus. Estos resultados indican que el análisis genético anterior que demostró que M. occultus y M. lucifugus forman grupos monofiléticos distintos no está respaldado por nuestra más amplia colección de datos. Es probable que para resolver mejor la verdadera relación taxonómica entre M. occultus y M. lucifugus sea necesario el uso de secuenciación del genoma completo del ADN nuclear.
Model development in the Next Generation Attenuation-East (NGA-East) project included two components developed concurrently and independently: (1) earthquake ground-motion models (GMMs) that predict the median and aleatory variability of various intensity measures conditioned on magnitude and distance, derived for a reference hard-rock site condition with an average shear-wave velocity in the upper 30 m (VS30) = 3000 m/s; and (2) a site amplification model that modifies intensity measures for softer site conditions. We investigate whether these models, when used in tandem, are compatible with ground-motion recordings in central and eastern North America (CENA) using an expanded version of the NGA-East database that includes new events from November 2011 (end date of NGA-East data curation) to April 2022. Following this expansion, the data set has 187 events, 2096 sites, and 16,272 three-component recordings, although the magnitude range remains limited (∼4 to 5.8). We compute residuals using 17 NGA-East GMMs and three data selection criteria that reflect within-CENA regional variations in ground-motion attributes. Mixed-effects regression of the residuals reveals a persistent pattern in which ground motions are overpredicted at short periods (0.01–0.6 s, including peak ground acceleration (PGA)) and underpredicted at longer periods. These misfits are regionally variable, with the Texas–Oklahoma–Kansas region having larger absolute misfits than other parts of CENA. Two factors potentially influencing these misfits are (1) differences in the site amplification models used to adjust the data to the reference condition during NGA-East GMM development relative to CENA amplification models applied since the 2018 National Seismic Hazard Model (NSHM), and (2) potential bias in simulation-based factors used to adjust ground motions from the hard-rock reference condition to a VS30 = 760 m/s condition. We provide adjustment factors and their epistemic uncertainties and discuss implications for applications.
","language":"English","publisher":"Sage Journals","doi":"10.1177/87552930241231825","usgsCitation":"Ramos-Sepulveda, M.E., Stewart, J.P., Parker, G.A., Moschetti, M.P., Thompson, E.M., Brandenberg, S.J., Hashash, Y.M., and Rathje, E., 2024, Data-driven adjustments for combined use of NGA-East hard-rock ground motion and site amplification models: Earthquake Spectra, v. 40, no. 2, p. 1132-1157, https://doi.org/10.1177/87552930241231825.","productDescription":"26 p.","startPage":"1132","endPage":"1157","ipdsId":"IP-153071","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":440108,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/87552930241231825","text":"Publisher Index 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\"}}]}","volume":"40","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Ramos-Sepulveda, Maria E.","contributorId":294748,"corporation":false,"usgs":false,"family":"Ramos-Sepulveda","given":"Maria","email":"","middleInitial":"E.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":897170,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stewart, Jonathan P.","contributorId":100110,"corporation":false,"usgs":false,"family":"Stewart","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":897171,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Parker, Grace Alexandra 0000-0002-9445-2571","orcid":"https://orcid.org/0000-0002-9445-2571","contributorId":237091,"corporation":false,"usgs":true,"family":"Parker","given":"Grace","email":"","middleInitial":"Alexandra","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":897172,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897173,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":897174,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brandenberg, Scott J.","contributorId":303895,"corporation":false,"usgs":false,"family":"Brandenberg","given":"Scott","email":"","middleInitial":"J.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":897175,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hashash, Youssef M A","contributorId":146595,"corporation":false,"usgs":false,"family":"Hashash","given":"Youssef","email":"","middleInitial":"M A","affiliations":[{"id":15289,"text":"University of Illinois, Ven Te Chow Hydrosystems 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The plan is to add and update this manual’s case definitions periodically as warranted; thus, this manual will never be completed, and readers should download the latest versions of specific chapters (that is, definitions) when available. Constructive suggestions from readers are welcome and will help guide adjustments as this project progresses.
The purpose of this manual is to provide case definitions for selected diseases of importance to wildlife in Canada and the United States. Case definitions provide standard sets of criteria for classifying the degree of certainty of a particular diagnosis and help improve surveillance data quality and comparability. Better data and standardization allow for improved data sharing, which increases geographic and species surveillance coverage and permits more robust analyses.
The definitions included in this manual have been developed by veterinary pathologists, epidemiologists, and wildlife biologists primarily from the U.S. Geological Survey National Wildlife Health Center (NWHC) and Canadian Wildlife Health Cooperative (CWHC). Pathologists from each organization reviewed and finalized the definitions. Each case definition has been peer reviewed by two scientific experts before publication.
This manual begins with the case definition template. This generic template includes four sections: “Individual, Place, and Time Criteria for Diagnosis and Testing,” “Field Criteria for Diagnosis,” “Laboratory Criteria for Diagnosis,” and “Epidemiological Linkage Criteria for Diagnosis” and can be used to guide development of new case definitions. Information in each section is then combined to provide an overall case classification. Disease diagnoses are classified as “Confirmed,” “Presumptive,” or “Suspected;” and evidence of a pathogen or toxin is classified as “Exposed” or “Present/Detected.” Each subsequent chapter is then a case definition for a specific disease of wildlife, and infectious and non-infectious diseases are included.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm19","collaboration":"Prepared in cooperation with the Canadian Wildlife Health Cooperative","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":426174,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/19/tm19.pdf","size":"0.98 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":426173,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/19/coverthb2.jpg"},{"id":426538,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/19/downloads/","text":"Editable template","size":"121 kB","linkHelpText":"—[Disease/Condition] Case Definition [Template] for Wildlife"}],"contact":"Director, National Wildlife Health Center
U.S. Geological Survey
6006 Schroeder Road
Madison, WI 53711
Genomes are heterogeneous during the early stages of speciation, with small ‘islands’ of DNA appearing to reflect strong adaptive differences, surrounded by vast seas of relative homogeneity. As species diverge, secondary contact zones between them can act as an interface and selectively filter through advantageous alleles of hybrid origin. Such introgression is another important adaptive process, one that allows beneficial mosaics of recombinant DNA (‘rivers’) to flow from one species into another. Although genomic islands of divergence appear to be associated with reproductive isolation, and genomic rivers form by adaptive introgression, it is unknown whether islands and rivers tend to be the same or different loci. We examined three replicate secondary contact zones for the Yosemite toad (Anaxyrus canorus) using two genomic data sets and a morphometric data set to answer the questions: (1) How predictably different are islands and rivers, both in terms of genomic location and gene function? (2) Are the adaptive genetic trait loci underlying tadpole growth and development reliably islands, rivers or neither? We found that island and river loci have significant overlap within a contact zone, suggesting that some loci are first islands, and later are predictably converted into rivers. However, gene ontology enrichment analysis showed strong overlap in gene function unique to all island loci, suggesting predictability in overall gene pathways for islands. Genome-wide association study outliers for tadpole development included LPIN3, a lipid metabolism gene potentially involved in climate change adaptation, that is island-like for all three contact zones, but also appears to be introgressing (as a river) across one zone. Taken together, our results suggest that adaptive divergence and introgression may be more complementary forces than currently appreciated.
The Rocky Mountain Population (RMP) of greater sandhill cranes uses a key stopover area, the San Luis Valley (SLV) in Colorado. Parameters of migration phenology can differ between autumn and spring and are affected by weather and environmental factors. We hypothesized that sandhill cranes in the SLV would have a longer stopover duration in autumn than in spring, and that wind assistance, crosswinds, temperature change, barometric air pressure, and surface water area would influence persistence probability. We used data from sandhill cranes fitted with transmitters that spanned autumn and spring, 2015-2022. We used an open robust design mark-recapture model to estimate stopover duration, arrival probability, and persistence probability. We examined the effects of weather and surface water on the persistence probability for 106 sandhill cranes in the SLV. Stopover duration was longer in autumn than in spring and had higher variability across years. Arrival probability to the SLV peaked on 13 October in autumn and 21 February in spring. Persistence probability declined around mid-December in autumn and mid-March in spring. We found that several weather covariates influenced persistence in both seasons. In autumn, sandhill cranes departed the SLV with higher tailwinds, lower crosswinds, and higher surface water availability. In spring, sandhill cranes departed the SLV with lower crosswinds and higher barometric air pressure at the surface and higher wind speeds at altitudes of about 3,000 m. The effect of wind speed was stronger later in the spring. Given the lower variability of arrival and persistence probability and shorter stopover duration in spring compared to autumn, we suspect that RMP sandhill cranes are using a time-minimization strategy during spring. However, given the use of supportive winds and weather conditions ideal for soaring, RMP sandhill cranes appear to be using strategies that save energy in both seasons. Our study identifies the optimal timing of water management and surveys for RMP sandhill cranes and confirms that weather influences their persistence. Understanding differences in migration patterns between seasons and the factors that influence persistence at stopover sites will also be important for anticipating phenological impacts from climate change and land use alterations.
Integrating recent scientific knowledge into management decisions supports effective natural resource management and can lead to better resource outcomes. However, finding and accessing scientific knowledge can be time consuming and costly. To assist in this process, the U.S. Geological Survey has created a series of annotated bibliographies on topics of management concern for lands in the western United States (U.S.). Oil and gas development on public lands is a long-standing and substantial component of local and regional economies and has expanded in recent decades, particularly on public lands in the western U.S. This development is associated with extensive networks of pipelines, roads, and processing facilities, across which reclamation is Federally mandated following initial well pad development (“interim” reclamation) and once resource extraction is complete (“final” reclamation). Reclamation is critical for recovering ecological services to energy-affected lands, including vegetation productivity, wildlife habitat, water and air quality, and soil stability (for example, resistance to wind and water erosion). However, reclamation of oil and gas affected lands in the western U.S. has proved challenging due to an array of regulatory and environmental factors, such as minimally developed soils, short growing seasons, herbivory, high winds, invasive species, rugged terrain, and in particular, arid climates associated with low total precipitation, high evapotranspiration rates, and highly variable precipitation patterns. We compiled and summarized journal articles, government reports, technical reports, proceedings, and theses and dissertations relevant to oil and gas reclamation. We constrained our search to products published on or before December 31, 2020 but did not limit our search by a starting date; the earliest product resulting from this effort was published in March 1969. Second, we manually scanned the last 15 years (2005-2020) of tables of contents in journals, bibliographies, and proceedings of which we were aware would contain articles highly relevant to this bibliography. We carried out the search for these products through multiple means: (1) performing a structured search of two reference databases, (2) examining articles published since 2005 in highly relevant scientific journals and conference proceedings, and (3) reviewing additional material suggested by authors of products identified in steps 1 and 2. Our search was intentionally broad in order to identify as much relevant work as possible, much of which is professionally applied and tested within the industry of oil and gas reclamation, but which remains unpublished in scientific journals. We refined the initial list of products by removing: (1) duplicates, (2) products not written in English, (3) products that were not relevant to the arid ecosystems of western North America, (4) products that were not released as research, data products, or review articles in journals or as formal scientific reports, and (5) products with data which were not relevant to reclamation of oil and gas-affected lands, or for which the study did not present new data, findings, or syntheses relevant to reclamation of oil and gas-affected lands.
We summarized each product using a consistent structure (background, objectives, methods, location, findings, and implications) and assigned standardized management topics to each. Management topics are intended to aid online searching within the bibliography and are described in more detail in the Methods Section of this report; they include what type of disturbance the product addresses (well pads, mining, pipelines), what aspect of oil and gas reclamation they pertain to (practices, standards, monitoring), what type of data are present in the product (for instance soil or vegetation recovery data), and an indication if the product were from a source other than a published, peer-reviewed outlet (such as dissertations or unpublished professional reports – these are identified as grey literature). The review process for this annotated bibliography included an initial internal colleague review of each summary, requesting input on each summary from an author of the original product, and a formal peer-review. Our initial searches resulted in 3,197 total products, of which 290 met our criteria for inclusion. “Reclamation Practices” is by far the management topic most addressed, followed by “Reclamation Monitoring,” for example, products assessing what and how monitoring methods are used to track and measure reclamation outcome. This document may be accessed at https://doi.org/10.3133/ofr20231068 or from the U.S. Geological Survey Publication Warehouse (https://pubs.usgs.gov/). The 1-page product summaries herein will also be used to create a bibliography at https://apps.usgs.gov/science-for-resource-managers that includes links to each original product, where available, and in which subject matter will be searchable by topic, location, and year. The studies compiled and summarized here may inform planning and management actions that seek to reclaim landscapes across the western U.S. which have been affected by oil and gas development.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231068","usgsCitation":"Mann, R.K., McCormick, M.L., Munson, S.M., Cooper, H.F., Bryant, L.C., Swenson, J.K., Johnston, L.A., Wilson, S.L., and Duniway, M.C., 2024, Annotated bibliography of scientific research relevant to oil and gas reclamation best management practices in the western United States, published from 1969 through 2020: U.S. Geological Survey Open-File Report 2023–1068, 210 p., https://doi.org/10.3133/ofr20231068.","productDescription":"viii, 210 p.","ipdsId":"IP-133481","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":426562,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1068/images"},{"id":426561,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1068/ofr20231068.xml"},{"id":426560,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1068/ofr20231068.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":426559,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1068/covrthb.jpg"},{"id":426563,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231068/full"}],"contact":"Southwest Biological Science Center
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Life history diversity can significantly affect population dynamics and effects of management actions. For instance, variation in individual responses to environmental variability can reduce extirpation risk to populations, as the portfolio effect dampens temporal variability in abundance. Moreover, differences in habitat use may cause individuals to respond differently to habitat management and climate variability. To explore the role of life history diversity in population trajectories, population models need to incorporate within-population variation. Integrated population modeling (IPM) is a population modeling approach that offers several advantages for sharing information and propagating uncertainty across datasets. In this study, we developed an IPM for an endangered population of Chinook salmon (Oncorhynchus tshawytscha) in the Wenatchee River, Washington, USA, that accounts for diversity in juvenile life histories, spawning location, and return age. Our analysis revealed that diversity in the age of juvenile emigration from natal streams had a portfolio effect, resulting in a 20% reduction in year-to-year variability in adult abundance in population projections. Our population viability analysis suggests that management interventions may be necessary to meet recovery goals, and our model should be useful for simulating the outcomes of proposed actions.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2023-0118","usgsCitation":"Sorel, M.H., Jorgensen, J.C., Zabel, R.W., Scheuerell, M.D., Murdoch, A.R., Kamphaus, C.M., and Converse, S.J., 2024, Incorporating life history diversity in an integrated population model to inform viability analysis: Canadian Journal of Fisheries and Aquatic Sciences, v. 81, no. 5, p. 535-548, https://doi.org/10.1139/cjfas-2023-0118.","productDescription":"14 p.","startPage":"535","endPage":"548","ipdsId":"IP-153564","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":440129,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1139/cjfas-2023-0118","text":"External Repository"},{"id":430133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.65380079789186,\n 48.385800374959246\n ],\n [\n -122.85878973700216,\n 48.35952757767319\n ],\n [\n -122.85878973700216,\n 45.39007143816454\n ],\n [\n -118.65380079789189,\n 45.42475769972472\n ],\n [\n -118.65380079789186,\n 48.385800374959246\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"81","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sorel, Mark H.","contributorId":171739,"corporation":false,"usgs":false,"family":"Sorel","given":"Mark","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":903859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgensen, Jeffrey C.","contributorId":339208,"corporation":false,"usgs":false,"family":"Jorgensen","given":"Jeffrey","email":"","middleInitial":"C.","affiliations":[{"id":36612,"text":"National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":903860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zabel, Richard W.","contributorId":272049,"corporation":false,"usgs":false,"family":"Zabel","given":"Richard","email":"","middleInitial":"W.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":903861,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scheuerell, Mark David 0000-0002-8284-1254","orcid":"https://orcid.org/0000-0002-8284-1254","contributorId":288621,"corporation":false,"usgs":true,"family":"Scheuerell","given":"Mark","email":"","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903862,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murdoch, Andrew R.","contributorId":339213,"corporation":false,"usgs":false,"family":"Murdoch","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":903863,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kamphaus, Cory M.","contributorId":339215,"corporation":false,"usgs":false,"family":"Kamphaus","given":"Cory","email":"","middleInitial":"M.","affiliations":[{"id":39287,"text":"Yakama Nation Fisheries","active":true,"usgs":false}],"preferred":false,"id":903864,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":903865,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70252274,"text":"70252274 - 2024 - Spatial extent drives patterns of relative climate change sensitivity for freshwater fishes of the United States","interactions":[],"lastModifiedDate":"2024-03-22T11:57:21.606818","indexId":"70252274","displayToPublicDate":"2024-03-14T06:54:28","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Spatial extent drives patterns of relative climate change sensitivity for freshwater fishes of the United States","docAbstract":"Assessing the sensitivity of freshwater species to climate change is an essential component of prioritizing conservation efforts for threatened freshwater ecosystems and organisms. Sensitivity to climate change can be systematically evaluated for multiple species using geographic attributes such as range size and climate niche breadth, and using species traits associated with climate change sensitivity. These systematic evaluations produce relative rankings of species sensitivity to aid conservation prioritization and to identify relatively sensitive species that may otherwise be understudied or overlooked. Due in part to biogeographic constraints, species assemblages change across regions and spatial extents; yet, the degree to which spatial factors influence relative rankings of species sensitivity is unclear. The spatial extent of multispecies analyses may alter relative rankings of species climate sensitivity; alternatively, relative climate sensitivity may be conserved among spatial scales, resulting in consistent identification of sensitive species among regions and spatial extents. We investigated how spatial extent influences our understanding of relative climate sensitivity for 137 native freshwater fishes of the United States that were representative of taxonomic, trait, and geographic diversity. Using publicly available occurrence data from the Global Biodiversity Information Facility, we calculated a systematic, geographically derived index of climate change sensitivity for study species at national and regional extents, including within four major hydrologic subregions of the United States. We examined the effects of spatial extent on the relative ranking of climate sensitivity among species, and we explored relationships among climate sensitivity, species traits, and conservation status at regional and national extents. We found that climate sensitivity rankings of species were influenced by spatial extent in some specific instances, but that relative rankings were largely conserved across spatial scales. However, correlations among geographically derived climate sensitivity rankings and species traits associated with climate sensitivity were variable across scales and regions, suggesting that links between geographic rarity and species traits may be scale-dependent in some cases. Finally, we found few associations between climate sensitivity and current conservation status among species. Systematic approaches to quantifying climate sensitivity may offer an opportunity to identify sensitive but overlooked species for pre-listing actions such as monitoring or conservation agreements.
StreamStats is a U.S. Geological Survey (USGS) web application that provides streamflow statistics, such as the 1-percent annual exceedance probability peak flow, the mean flow, and the 7-day, 10-year low flow, to the public through a map-based user interface. These statistics are used in many ways, such as in the design of roads, bridges, and other structures; in delineation of floodplains for land-use zoning and setting of insurance rates; for regulatory purposes, such as the permitting of wastewater discharges; and for hydrologic and climate change studies. StreamStats was first developed for Massachusetts and released in 2001. The application provided users with the ability to obtain streamflow statistics computed from data collected at USGS streamgages and to obtain estimates of streamflow statistics for user-selected ungaged sites. Massachusetts StreamStats used geographic information system software and digital mapping to compute drainage-basin characteristics, which were then used in statistical models to estimate streamflow statistics for the user-selected sites. The statistical models were in the form of equations that were developed through a process known as regression analysis. StreamStats was the first known web application with the ability to do interactive geoprocessing.
The utility of Massachusetts StreamStats was instantly apparent, leading the USGS to develop a version of StreamStats that could be implemented nationally. USGS State offices normally were required to develop custom regression equations and prepare local digital mapping data needed for implementing StreamStats for their States. Funding needed to complete this work usually was provided through cooperative agreements between the USGS and State agencies. In 2004, Idaho became the first to be released in the national version of StreamStats. By 2023, 44 States were fully implemented and six were undergoing implementation.
StreamStats has undergone many modifications over the years to keep up with changes to the underlying software and to add functionality. Customized functionality and separate linked StreamStats applications were developed for several States. Meeting the high demand for additions and improvements to StreamStats while also adhering to budgetary constraints has, at times, been challenging. The StreamStats development team has identified numerous additional improvements that could be made to provide better performance and more functionality. The lessons learned from the experience of building and operating StreamStats for nearly 25 years could be relevant to others interested in pursuing efforts of a similar scale.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1514","usgsCitation":"Ries, K.G., III, Steeves, P.A., and McCarthy, P., 2024, StreamStats—A quarter century of delivering web-based geospatial and hydrologic information to the public, and lessons learned: U.S. Geological Survey Circular 1514, 40 p., https://doi.org/10.3133/cir1514.","productDescription":"viii, 40 p.","numberOfPages":"40","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-102663","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":499073,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116170.htm","linkFileType":{"id":5,"text":"html"}},{"id":426017,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1514/cir1514.XML","description":"CIR 1514 XML"},{"id":426018,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1514/images/"},{"id":426016,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1514/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"CIR 1514 HTML"},{"id":426015,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1514/cir1514.pdf","text":"Report","size":"7.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1514 PDF"},{"id":426014,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1514/coverthb.jpg"}],"contact":"National Coordinator, StreamStats
U.S. Geological Survey
1728 Lampman Drive, Suite D
Billings, MT 59102
Alaska’s Arctic coast has some of the highest coastal erosion rates in the world, primarily driven by permafrost thaw and increasing wave energy. In the Arctic, a warming climate is driving sea ice cover to decrease in space and time. A lack of long-term observational wave data along Alaska’s coast challenges the ability of engineers, scientists, and planners to study and address threats and effects from wave-driven erosion and flooding. To overcome the lack of available observational wave data in the nearshore in this study by the U.S. Geological Survey, waves were downscaled with the Simulating WAves Nearshore numerical wave model (SWAN) for the hindcast period of 1979 to 2019 from the United States-Canada border to the Bering Sea utilizing nine model domains. For each domain, the model was forced at the open boundary with 2,500 representative “sea states,” which are likely combinations of significant wave heights, mean wave periods, mean wave directions, and wind speeds and directions. The sea states were obtained from the European Centre for Medium-Range Weather Forecasts “ERA5” dataset for reanalysis of winds and waves using a multivariant maximum-dissimilarity algorithm. The SWAN runs created a downscaled wave database at each grid point, which was used to reconstruct the 40-year time series in the nearshore along the 5- and 10-meter isobaths at locations approximately 400 m apart and corresponding to transects spaced approximately 50 m alongshore, as developed for USGS shoreline-change assessments. Reconstructed time series were compared to observations to validate the numerical model and the downscaled wave database method and showed overall good agreements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231094","programNote":"Prepared in cooperation with Deltares USA and the University of California, Santa Cruz","usgsCitation":"Engelstad, A.C., Erikson, L.H., Reguero, B.G., Gibbs, A.E., and Nederhoff, K., 2024, Database and time series of nearshore waves along the Alaskan coast from the United States-Canada border to the Bering Sea: U.S. Geological Survey Open-File Report 2023–1094, 23 p., https://doi.org/10.3133/ofr20231094.","productDescription":"Report: v, 23 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","ipdsId":"IP-132323","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":499201,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116169.htm","linkFileType":{"id":5,"text":"html"}},{"id":426586,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231094/full"},{"id":426585,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1094/images"},{"id":426584,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1094/ofr20231094.xml"},{"id":426583,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1094/covrthb.jpg"},{"id":426582,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1094/ofr20231094.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":426581,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P931CSO9","text":"USGS Data Release","description":"Engelstad, A.C., Erikson, L.H., Reguero, B.G., Gibbs, A.E., Nederhoff, K.M., 2024, Nearshore wave time-series along the coast of Alaska computed with a numerical wave model: U.S. Geological Survey data release, https://doi.org/10.5066/P931CSO9.","linkHelpText":"Nearshore wave time-series along the coast of Alaska computed with a numerical wave model"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -168.34514806758992,\n 65.52631288766165\n ],\n [\n -140.04436681759015,\n 65.52631288766165\n ],\n [\n -140.04436681759015,\n 71.42344314984271\n ],\n [\n -168.34514806758992,\n 71.42344314984271\n ],\n [\n -168.34514806758992,\n 65.52631288766165\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
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The social system of animals involves a complex interplay between physiology, natural history, and the environment. Long relied upon discrete categorizations of “social” and “solitary” inhibit our capacity to understand species and their interactions with the world around them. Here, we use a globally distributed camera trapping dataset to test the drivers of aggregating into groups in a species complex (martens and relatives, family Mustelidae, Order Carnivora) assumed to be obligately solitary. We use a simple quantification, the probability of being detected in a group, that was applied across our globally derived camera trap dataset. Using a series of binomial generalized mixed-effects models applied to a dataset of 16,483 independent detections across 17 countries on four continents we test explicit hypotheses about potential drivers of group formation. We observe a wide range of probabilities of being detected in groups within the solitary model system, with the probability of aggregating in groups varying by more than an order of magnitude. We demonstrate that a species’ context-dependent proclivity toward aggregating in groups is underpinned by a range of resource-related factors, primarily the distribution of resources, with increasing patchiness of resources facilitating group formation, as well as interactions between environmental conditions (resource constancy/winter severity) and physiology (energy storage capabilities). The wide variation in propensities to aggregate with conspecifics observed here highlights how continued failure to recognize complexities in the social behaviors of apparently solitary species limits our understanding not only of the individual species but also the causes and consequences of group formation.
","language":"English","publisher":"National Academy of Sciences of the United States of America","doi":"10.1073/pnas.2312252121","usgsCitation":"Twining, J., Sutherland, C., Zalewski, A., Cove, M., Birks, J., Wearn, O.R., Haysom, J., Wereszczuk, A., Manzo, E., Bartolommei, P., Mortelliti, A., Evans, B., Gerber, B., McGreevy, T., Ganoe, L.S., Masseloux, J., Mayer, A.E., Wierzbowska, I., Loch, J., Akins, J., Drummey, D., McShea, W., Manke, S., Pardo, L., Boyce, A., Li, S., Binti Ragai, R., Sukmasuang, R., Villafane Trujillo, A.J., Lopez-Gonzalez, C., Lara-Diaz, N.E., Cosby, O., Waggershauser, C.N., Bamber, J., Stewart, F., Fisher, J., Fuller, A.K., Perkins, K., and Powell, R.A., 2024, Using global remote camera data of a solitary species complex to evaluate the drivers of group formation: PNAS, v. 121, no. 12, e2312252121, 8 p., https://doi.org/10.1073/pnas.2312252121.","productDescription":"e2312252121, 8 p.","ipdsId":"IP-151912","costCenters":[{"id":199,"text":"Coop Res Unit 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,{"id":70257405,"text":"70257405 - 2024 - Habitat amount and edge effects, not perch proximity, nest exposure, or vegetation diversity affect cowbird parasitism in agricultural landscapes","interactions":[],"lastModifiedDate":"2024-08-30T15:53:54.80827","indexId":"70257405","displayToPublicDate":"2024-03-11T08:39:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Habitat amount and edge effects, not perch proximity, nest exposure, or vegetation diversity affect cowbird parasitism in agricultural landscapes","docAbstract":"Prior research documented relationships between brown-headed cowbird (Molothrus ater) brood parasitism and edge effects, proximity of perches, and nest exposure. Those relationships have not been evaluated in agroecosystems containing extremes of fragmentation and vegetation diversity.
We compared three existing hypotheses on how cowbirds locate host nests with two new hypotheses regarding habitat amount and vegetation diversity to determine how the configuration and location of agricultural conservation practices affect grassland bird nest parasitism rates and predicted rates for eight common conservation practices.
We assessed cowbird parasitism of grassland bird nests on corn and soybean farms in Iowa, USA, and measured perch proximity, nest exposure, edge effects, habitat amount, and vegetation diversity for each nest. We fit a global generalized linear mixed-effects model and compared importance of model parameters using odds ratios. We predicted parasitism likelihood for every subset model and averaged predictions to explore individual effects.
The variables that most influenced parasitism rates included main effects for nest initiation day-of-season (OR = 0.71, CI95 = 0.60–0.84) and the landscape variables of distance to nearest crop edge (0.63, 0.51–0.76) and proportion of grass land cover within 660 m (0.75, 0.57–1.00). We found little support that perch proximity, nest exposure, or native vegetation diversity affected parasitism. We also assessed parasitism likelihood by conservation practice and found no significant differences.
Our results provide evidence to support the edge effect and habitat amount hypotheses, but not the nest exposure, vegetation diversity, or perch proximity hypotheses.
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\"}}]}","volume":"39","noUsgsAuthors":false,"publicationDate":"2024-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Stephenson, Matthew D.","contributorId":342652,"corporation":false,"usgs":false,"family":"Stephenson","given":"Matthew D.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":910259,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yuza, Kyla L.","contributorId":342654,"corporation":false,"usgs":false,"family":"Yuza","given":"Kyla","email":"","middleInitial":"L.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":910260,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schulte, Lisa A.","contributorId":342657,"corporation":false,"usgs":false,"family":"Schulte","given":"Lisa A.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":910261,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910262,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251898,"text":"ofr20241013 - 2024 - Growth, survival, and cohort formation of juvenile Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2021–22 monitoring report","interactions":[],"lastModifiedDate":"2024-12-04T14:29:22.266119","indexId":"ofr20241013","displayToPublicDate":"2024-03-11T08:24:27","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1013","displayTitle":"Growth, Survival, and Cohort Formation of Juvenile Lost River (Deltistes luxatus) and Shortnose Suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2021–22 Monitoring Report","title":"Growth, survival, and cohort formation of juvenile Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2021–22 monitoring report","docAbstract":"The work reported in this publication provides updated data and interpretation for sampling years 2015 and 2022 of the juvenile monitoring project. The study objectives, background, study area, species description, and methods remained the same or similar throughout the years, while the executive summary, results, and discussion were updated each year. Therefore much of this paper was originally presented in previous reports (Bart and others 2020a, b; Bart and others, 2021; Burdick and others, 2016; Burdick and others, 2018; Martin and others, 2022) and is repeated here for the reader’s convenience.
Populations of federally endangered Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir (hereinafter, Clear Lake), California, are experiencing long-term decreases in abundance. Upper Klamath Lake populations are decreasing not only because of adult mortality, which is relatively low, but also because they are not being balanced by recruitment of young adult suckers into adult spawning aggregations.
Long-term monitoring of juvenile sucker populations is conducted to (1) determine if there are annual and species-specific differences in production, survival, and growth; (2) better understand when juvenile sucker mortality is greatest; and (3) identify potential causes of high juvenile sucker mortality particularly in Upper Klamath Lake. The U.S. Geological Survey (USGS) monitoring program, begun in 2015, tracks cohorts through summer months and among years in Upper Klamath and Clear Lakes. Data on juvenile suckers captured in trap nets are used to provide information on annual variability in age-0 sucker production, juvenile sucker apparent survival, growth, species composition, and health.
Upper Klamath Lake indices of year-class strength suggest that the 2022 age-0 cohort is the lowest since standardized monitoring began. The 2021 cohort, like most cohorts, had moderately low catch rates their first year of life, with a steep drop off during the second year. Although the 2020 cohort persisted through the September 2022 sampling, this cohort was sparsely represented after the first year with no representatives from this cohort captured from July 2021 through July 2022. Despite apparently low fall through spring apparent survival, the relatively large 2019 cohort persisted in our 2020–21 samples, but has not been detected since June 2021. Klamath largescale (Catostomus snyderi) and shortnose suckers were only differentiated from each other starting in 2020. Shortnose suckers dominated the age-1 catch in 2020 and 2022, whereas age-1 Klamath largescale suckers were slightly more prevalent in 2021. Although there were occasionally age-2 and older suckers captured, none of these fish were Lost River suckers. Except for 2015, 2017, and 2021, there were more age-0 Lost River suckers than presumed shortnose suckers in Upper Klamath Lake. However, in all years sampled, there were more age-1 presumed shortnose suckers than Lost River suckers.
Age distribution of suckers captured in Clear Lake indicates greater juvenile survival than in Upper Klamath Lake. Most juvenile suckers captured throughout the years were from the 2016 and 2017 cohorts; however, by 2022 most of these fish were no longer susceptible to standard trap nets and were not as prevalent in 2022 juvenile catches, and these suckers presumedly recruited to the adult population. As the 2016 and 2017 cohorts catches declined, so did the catch in overall numbers of suckers. Excluding age-0 catches, the 2016 cohort catches peaked at age-3 and the 2017 catches peaked at age-2. In 2022, the majority of the catch was composed of age-3 to age-5 suckers. The majority of suckers captured in Clear Lake during this multiyear project were classified as the combination of Klamath largescale suckers and shortnose suckers from the Lost River Basin, from the 2016 and 2017 cohorts. The few suckers identified as Lost River or definitive shortnose suckers were from the 2016 and 2017 cohorts. A lack of age-0 suckers captured in Clear Lake during years with low spawning tributary inflow or lake levels suggested that low water prevented spawning and year class formation. However, recent data indicate that some cohorts with Klamath largescale and shortnose sucker genetics that were not captured as age-0 suckers were detected in later years at age-1 or age-2. This finding indicates that juvenile suckers in Clear Lake may spend one or more years in the tributaries and that these cohorts may primarily be represented by Klamath largescale suckers.
The first 7 years of this monitoring program indicated different patterns in recruitment and survival of juvenile suckers between Upper Klamath and Clear Lakes. Since the monitoring program began in 2015, age-0 sucker catch rates, interpreted as indices of year-class strength, were greatest in Upper Klamath Lake in 2016 and 2019. In those years, Lost River suckers made up the majority of age-0 sucker catches. However, in 2017 and 2020, the age-1 sucker catches from these cohorts were mainly composed of shortnose suckers or suckers with genetic markers of both Klamath largescale and shortnose suckers, indicating a low first year survival for Lost River suckers even when age-0 catches were high. Age-0 suckers do not fully recruit to our sampling gear in Upper Klamath Lake until August, experience high mortality by September, and are almost undetectable in subsequent years. In Clear Lake, suckers are often not captured until age-1 or age-2 and juvenile annual survival appears much greater; however, there does appear to be a drop-off in catch rates as the suckers age and become less susceptible to the fishing gear.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241013","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Martin, B.A., Caldwell, J.M., Krause, J.R., and Harris, A.C., 2024, Growth, survival, and cohort formation of juvenile Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2021–22 monitoring report: U.S. Geological Survey Open-File Report 2024–1013, 39 p., https://doi.org/10.3133/ofr20241013.","productDescription":"Report: vi, 39 p.; 1 Data Release","onlineOnly":"Y","ipdsId":"IP-159115","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":426337,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1013/ofr20241013.XML"},{"id":426335,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93KYGEG","text":"USGS data release","description":"USGS data release","linkHelpText":"Upper Klamath Lake and Clear Lake sampling for suckers from 2015 through 2022. Reston, Virginia: U.S. Geological Survey, Klamath Falls Field Station, Klamath Falls, Oregon"},{"id":426334,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241013/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1013"},{"id":426333,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1013/ofr20241013.pdf","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1013"},{"id":426332,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1013/ofr20241013.jpg"},{"id":426336,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1013/images"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Clear Lake Reservoir, Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.01266997658502,\n 41.93494622821743\n ],\n [\n -121.26100788006954,\n 41.93494622821743\n ],\n [\n -121.26100788006954,\n 41.786587280075025\n ],\n [\n -121.01266997658502,\n 41.786587280075025\n ],\n [\n -121.01266997658502,\n 41.93494622821743\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.78179025160858,\n 42.649405814487636\n ],\n [\n -122.11246478619483,\n 42.649405814487636\n ],\n [\n -122.11246478619483,\n 42.22404414347665\n ],\n [\n -121.78179025160858,\n 42.22404414347665\n ],\n [\n -121.78179025160858,\n 42.649405814487636\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Measuring genetic diversity of wild species using DNA-based data remains resource intensive and time consuming for nearly all species. However, genetic assessments are needed for global conservation commitments, including the Convention on Biological Diversity, and for governments and managers to evaluate conservation progress, as well as prioritizing species and populations to preserve and recover genetic diversity (e.g., via genetic rescue). Recently, indicators were developed for tracking and reporting genetic diversity status and trends for hundreds of species. The indicators quantify two simple proxies of within-population and among-population genetic diversity and adaptive potential: small effective population size (Ne < 500) and the loss of genetically distinct populations. The indicators must balance scientific credibility, practicality, and simplicity. In the present article, we summarize the advantages of these pragmatic indicators, address critiques by scientists for simplifying assumptions and by policymakers for complexity, and propose potential solutions and next steps. We aim to support practitioners putting indicators into policy, action, legislation, and reporting.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/biosci/biae006","usgsCitation":"Hoban, S.M., da Silva, J.M., Hughes, A.C., Hunter, M., Kalamujic Stroil, B., Laikre, L., Mastretta-Yanes, A., Millette, K., Paz-Vinas, I., Ruiz Bustos, L., Shaw, R.E., Vernesi, C., and Genetics, T.F., 2024, Too simple, too complex, or just right? Advantages, challenges, and guidance for indicators of genetic diversity: BioScience, biae006, 12 p., https://doi.org/10.1093/biosci/biae006.","productDescription":"biae006, 12 p.","ipdsId":"IP-153586","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":440155,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biae006","text":"Publisher Index Page"},{"id":426885,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2024-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoban, Sean M. 0000-0002-0348-8449","orcid":"https://orcid.org/0000-0002-0348-8449","contributorId":206582,"corporation":false,"usgs":false,"family":"Hoban","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":897065,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"da Silva, Jessica M.","contributorId":290139,"corporation":false,"usgs":false,"family":"da Silva","given":"Jessica","email":"","middleInitial":"M.","affiliations":[{"id":62352,"text":"South African National Biodiversity Institute, Kirstenbosch Research Centre, Rhodes Drive, Private Bag X7, 7735 Cape Town, South Africa","active":true,"usgs":false}],"preferred":false,"id":897066,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hughes, Alice C.","contributorId":329501,"corporation":false,"usgs":false,"family":"Hughes","given":"Alice","email":"","middleInitial":"C.","affiliations":[{"id":78621,"text":"University of Hong Kong, China","active":true,"usgs":false}],"preferred":false,"id":897067,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":214958,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":897068,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kalamujic Stroil, Belma","contributorId":334957,"corporation":false,"usgs":false,"family":"Kalamujic Stroil","given":"Belma","email":"","affiliations":[{"id":80293,"text":"University of Sarajevo-Institute for Genetic Engineering and Biotechnology","active":true,"usgs":false}],"preferred":false,"id":897069,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Laikre, Linda","contributorId":261151,"corporation":false,"usgs":false,"family":"Laikre","given":"Linda","affiliations":[{"id":24562,"text":"Stockholm University","active":true,"usgs":false}],"preferred":false,"id":897070,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mastretta-Yanes, Alicia","contributorId":301222,"corporation":false,"usgs":false,"family":"Mastretta-Yanes","given":"Alicia","email":"","affiliations":[{"id":65333,"text":"Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO)","active":true,"usgs":false}],"preferred":false,"id":897071,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Millette, Katie L","contributorId":291311,"corporation":false,"usgs":false,"family":"Millette","given":"Katie L","affiliations":[{"id":62680,"text":"Department of Biology, McGill University","active":true,"usgs":false}],"preferred":false,"id":897072,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Paz-Vinas, Ivan","contributorId":239614,"corporation":false,"usgs":false,"family":"Paz-Vinas","given":"Ivan","email":"","affiliations":[{"id":47934,"text":"Laboratoire Ecologie Fonctionnelle et Environnement, Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":897073,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ruiz Bustos, Lucia","contributorId":334958,"corporation":false,"usgs":false,"family":"Ruiz Bustos","given":"Lucia","email":"","affiliations":[{"id":80294,"text":"Conservation Areas Director, World Wildlife Fund","active":true,"usgs":false}],"preferred":false,"id":897074,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shaw, Robyn E.","contributorId":260838,"corporation":false,"usgs":false,"family":"Shaw","given":"Robyn","email":"","middleInitial":"E.","affiliations":[{"id":52690,"text":"Environmental and Conservation Sciences, Murdoch University, Perth, Australia","active":true,"usgs":false}],"preferred":false,"id":897075,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Vernesi, Cristiano","contributorId":239922,"corporation":false,"usgs":false,"family":"Vernesi","given":"Cristiano","email":"","affiliations":[{"id":48051,"text":"Dept. of Sustainable Agroecosystems and Bioresources, Research and Innovation Centre - Fondazione Edmund Mach","active":true,"usgs":false}],"preferred":false,"id":897076,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Genetics, the Coalition for Conservation","contributorId":334959,"corporation":false,"usgs":false,"family":"Genetics","given":"the Coalition","email":"","middleInitial":"for Conservation","affiliations":[],"preferred":false,"id":897077,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70254441,"text":"70254441 - 2024 - Paleogene mid-crustal intrusions in the Ruby Mountains–East Humboldt Range metamorphic core complex, northeastern Nevada, USA","interactions":[],"lastModifiedDate":"2024-05-24T12:16:26.609714","indexId":"70254441","displayToPublicDate":"2024-03-09T07:12:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Paleogene mid-crustal intrusions in the Ruby Mountains–East Humboldt Range metamorphic core complex, northeastern Nevada, USA","docAbstract":"Middle Eocene to early Oligocene intrusions, widespread in the Ruby Mountains–East Humboldt Range metamorphic core complex, Nevada, USA, provide insights into a major Paleogene magmatic episode and its relation to tectonism in the northeastern Great Basin. These intrusions, well-exposed in upper Lamoille Canyon, range in composition from gabbro to leucomonzogranite. They form small plutons, sheets, and dikes that intrude the metamorphic and granitic infrastructure of the core complex. Two types of Paleogene monzogranite were recognized. The first is exemplified by two of the larger intrusive bodies, the Snow Lake Peak and Castle Lake intrusions, which occur as sheet-like bodies near and at the structural base of metamorphosed Neoproterozoic and Cambrian Prospect Mountain Quartzite where it is inverted above Cambrian and Ordovician marble of Verdi Peak in the Lamoille Canyon nappe. Swarms of dikes are associated with these intrusions. U-Pb (zircon) ages range ca. 40–33 Ma and typically display relatively simple and minor inheritance. The rocks have the lowest εHf (zircon) and εNd (whole rock) of any of the middle Cenozoic granites. The second type of monzogranite, Overlook type, typically occurs as thin, isolated dikes and leucosome-like bodies in Late Cretaceous granites of the infrastructure, with no obvious relationship to the large monzogranite bodies. Overlook-type monzogranite displays complex zircon inheritance, yields igneous ages ca. 37–32 Ma, and has εHf (zircon) and εNd (whole rock) identical to those of Late Cretaceous granites in the core complex. These isotopic and field data indicate that Overlook-type monzogranite formed in situ through anatexis of host Cretaceous granites. A pervasive thermal event was required to stimulate this crustal melting.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02673.1","usgsCitation":"Snoke, A., Barnes, C., Howard, K., Romanoski, A., Premo, W.R., Hetherington, C., Strike, A., Frost, C., Copeland, P., and Lee, S., 2024, Paleogene mid-crustal intrusions in the Ruby Mountains–East Humboldt Range metamorphic core complex, northeastern Nevada, USA: Geosphere, v. 20, no. 2, p. 577-620, https://doi.org/10.1130/GES02673.1.","productDescription":"44 p.","startPage":"577","endPage":"620","ipdsId":"IP-153033","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":440164,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02673.1","text":"Publisher Index Page"},{"id":429247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.21976098588017,\n 42.94002735616252\n ],\n [\n -117.21976098588017,\n 39.87569247364536\n ],\n [\n -113.15481957963019,\n 39.87569247364536\n ],\n [\n -113.15481957963019,\n 42.94002735616252\n ],\n [\n -117.21976098588017,\n 42.94002735616252\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Snoke, A.W.","contributorId":336903,"corporation":false,"usgs":false,"family":"Snoke","given":"A.W.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":901374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnes, C.B.","contributorId":336904,"corporation":false,"usgs":false,"family":"Barnes","given":"C.B.","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":901375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Keith A. 0000-0002-6462-2947","orcid":"https://orcid.org/0000-0002-6462-2947","contributorId":264832,"corporation":false,"usgs":true,"family":"Howard","given":"Keith A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":901376,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romanoski, A.","contributorId":336905,"corporation":false,"usgs":false,"family":"Romanoski","given":"A.","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":901377,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Premo, Wayne R. 0000-0001-9904-4801 wpremo@usgs.gov","orcid":"https://orcid.org/0000-0001-9904-4801","contributorId":1697,"corporation":false,"usgs":true,"family":"Premo","given":"Wayne","email":"wpremo@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":901378,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hetherington, C.","contributorId":336906,"corporation":false,"usgs":false,"family":"Hetherington","given":"C.","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":901379,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Strike, A.","contributorId":336909,"corporation":false,"usgs":false,"family":"Strike","given":"A.","email":"","affiliations":[{"id":80908,"text":"Lander, WY","active":true,"usgs":false}],"preferred":false,"id":901380,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frost, C.","contributorId":336910,"corporation":false,"usgs":false,"family":"Frost","given":"C.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":901381,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Copeland, P.","contributorId":336912,"corporation":false,"usgs":false,"family":"Copeland","given":"P.","email":"","affiliations":[{"id":36391,"text":"University of Houston","active":true,"usgs":false}],"preferred":false,"id":901382,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lee, S-Y","contributorId":336914,"corporation":false,"usgs":false,"family":"Lee","given":"S-Y","affiliations":[{"id":80909,"text":"CTL Group","active":true,"usgs":false}],"preferred":false,"id":901383,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ]}