The Joint Agency Commercial Imagery Evaluation (JACIE) is a collaboration between six Federal agencies that are major users and producers of satellite land remote sensing data. In recent years, the JACIE group has observed ever-increasing numbers of remote sensing satellites being launched. This rapidly growing wave of new systems creates a need for a single reference for land remote sensing satellites that provides basic system specifications and linkage to any JACIE assessment that may have been completed on existing systems. This volume has been assembled by the Requirements, Capabilities, and Analysis for Earth Observation Project under the U.S. Geological Survey National Land Imaging Program as a contribution to the JACIE community. This is the third edition of the JACIE compendium, which is planned to be updated and released annually.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1500","usgsCitation":"Ramaseri Chandra, S.N., Christopherson, J.B., Casey, K.A., Lawson, J., and Sampath, A., 2022, 2022 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium: U.S. Geological Survey Circular 1500, 279 p., https://doi.org/10.3133/cir1500. [Supersedes USGS Circular 1468.]","productDescription":"xiii, 279 p.","numberOfPages":"298","onlineOnly":"N","ipdsId":"IP-139076","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":407832,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1500/circ1500.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1500"},{"id":407831,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1500/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Formally protected areas are an important component of wildlife conservation, but face limitations in their effectiveness for migratory species. Improved stewardship of working lands around protected areas is one solution for conservation planning, but private working lands are vulnerable to development. In the Greater Yellowstone Ecosystem (GYE), ungulates such as elk (Cervus canadensis) use both protected areas and private lands throughout their annual migrations. We studied patterns of landownership, protection, and conservation challenges within the ranges of migratory elk in the GYE. We used GPS data from 1088 elk in 26 herds to define herd-level seasonal ranges, and extracted covariates related to landownership and protection, land use, and human infrastructure and development. All elk herds used land encompassing >1 ownership type. Most elk herds (92.3 % of herds, n = 24) used the highest proportion of private land in the winter (mean = 36.2 % private land). Most elk herds' winter ranges contained the highest building densities (mean = 1.24 buildings/km2), fence densities (mean = 1.02 km fence/km2), and cattle grazing (mean = 1.9 cows/km2), compared to migratory and summer ranges. Out of all ranges, 36.5 % of ranges did not have any zoning regulations, indicating the potential for future development. Our results show that elk in the GYE rely on habitat outside of protected areas, and face landscape-scale conservation challenges such as habitat fragmentation from human development, particularly in winter ranges. Future conservation strategies for wildlife in this system need to encompass coordination across both public and private land to ensure migratory connectivity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2022.109752","usgsCitation":"Gigliottia, L.C., Wenjing Xu, Zuckerman, G., Atwood, M.P., Eric K. Cole, Alyson Courtemanch, Dewey, S., Gude, J.A., Hnilicka, P., Kauffman, M., Kroetz, K., Lawson, A., Leonard, B., MacNulty, D., Maichak, E., McWhirter, D., Mong, T.W., Proffitt, K., Scurlock, B., Stahler, D., and Middleton, A.D., 2022, Wildlife migrations highlight importance of both private lands and protected areas in the Greater Yellowstone Ecosystem: Biological Conservation, v. 275, 109752, 9 p., https://doi.org/10.1016/j.biocon.2022.109752.","productDescription":"109752, 9 p.","ipdsId":"IP-141698","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":446239,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2022.109752","text":"Publisher Index Page"},{"id":430023,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.79292022701935,\n 45.50034062999211\n ],\n [\n -111.79292022701935,\n 43.92815809407176\n ],\n [\n -109.67116747387536,\n 43.92815809407176\n ],\n [\n -109.67116747387536,\n 45.50034062999211\n ],\n [\n -111.79292022701935,\n 45.50034062999211\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"275","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gigliottia, Laura C.","contributorId":338702,"corporation":false,"usgs":false,"family":"Gigliottia","given":"Laura","email":"","middleInitial":"C.","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":903466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wenjing Xu","contributorId":338703,"corporation":false,"usgs":false,"family":"Wenjing Xu","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":903467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zuckerman, Gabriel","contributorId":338704,"corporation":false,"usgs":false,"family":"Zuckerman","given":"Gabriel","email":"","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":903468,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Atwood, M. 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2022 - Corrigendum: Associations between cyanobacteria and indices of secondary production in the western basin of Lake Erie","interactions":[],"lastModifiedDate":"2024-09-26T13:40:43.394886","indexId":"70258749","displayToPublicDate":"2022-10-03T08:32:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Corrigendum: Associations between cyanobacteria and indices of secondary production in the western basin of Lake Erie","docAbstract":"In the last year, we became aware that data used in our above-referenced manuscript from 2018 published in Limnology and Oceanography contained significant errors. In the 2018 manuscript, we found that indices of secondary production were negatively correlated to indices of cyanobacterial abundance and toxicity. Unfortunately, one of our indices of cyanobacterial abundance (biovolume) and our measurement of toxicity (microcystin concentration) were inaccurate in the data we used in the 2018 manuscript. Upon discovering these errors, we immediately began correcting the repositories where these data were available. Having corrected those data repositories, we are now reporting on the re-analysis of the data using the methods previously described in the 2018 manuscript. Although the relationships are slightly different, our interpretation is that the conclusions of the 2018 manuscript are still valid using the corrected data. The data errors we experienced were traced to a spreadsheet that was used to share data among the research team, which had errors caused by mistakes in copy-pasting formulas instead of data and ‘inadvertent’ edits that were saved by the spreadsheet's auto-save function. We apologize to the scientific community for these errors.
","language":"English","publisher":"ASLO","doi":"10.1002/lno.12216","usgsCitation":"Larson, J.H., Evans, M.A., Kennedy, R., Bailey, S., Loftin, K.A., Laughrey, Z.R., Femmer, R.A., Schaeffer, J., Richardson, W., Wynne, T., Nelson, J.C., and Duris, J.W., 2022, Corrigendum: Associations between cyanobacteria and indices of secondary production in the western basin of Lake Erie: Limnology and Oceanography, v. 67, no. 11, p. 2617-2620, https://doi.org/10.1002/lno.12216.","productDescription":"4 p.","startPage":"2617","endPage":"2620","ipdsId":"IP-138062","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":462275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.51669311523438,\n 41.51063406062076\n ],\n [\n -82.77923583984375,\n 41.51063406062076\n ],\n [\n -82.77923583984375,\n 42.04011410708205\n ],\n [\n -83.51669311523438,\n 42.04011410708205\n ],\n [\n -83.51669311523438,\n 41.51063406062076\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"67","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-10-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":913946,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":149358,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":913947,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kennedy, Robert J 0000-0003-2135-5022","orcid":"https://orcid.org/0000-0003-2135-5022","contributorId":215686,"corporation":false,"usgs":false,"family":"Kennedy","given":"Robert J","affiliations":[{"id":39305,"text":"Former UMESC employee - 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2022 - Last Glacial Maximum and early deglaciation in the Stura Valley, southwestern European Alps","interactions":[],"lastModifiedDate":"2022-10-04T11:59:18.208422","indexId":"70237184","displayToPublicDate":"2022-10-03T06:55:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Last Glacial Maximum and early deglaciation in the Stura Valley, southwestern European Alps","docAbstract":"We combined data from geomorphologic surveys, glacial modelling, and 10Be exposure ages of boulders on moraines, to investigate the Last Glacial Maximum (LGM) and the early retreat glacial phases in the Stura Valley of the Maritime Alps. We used the exposure ages to reconstruct the timing of standstills or readvances which interrupted the post-LGM withdrawal, initiated ∼24 ka. We mapped and dated the frontal moraines of a first glacial standstill/readvance at a short distance (∼7 km) from the maximum external limit of the LGM, which occurred at ∼22 ka, and a second one at ∼19 ka (Bühl stadial). This morpho-chronologic succession is congruent with that obtained in the adjacent Gesso Valley and, combined with the similarity of Equilibrium Line Altitude values, demonstrates a consistent glacial response in the Maritime Alps to climatic forcing.
Our data are chronologically consistent with those of the southern flank of the European Alps, stressing not only a general synchroneity of the LGM across the various sectors, but also that of a LGM recessional standstill or readvance at ∼22 ka. The short distance between the LGM moraines and the recessionary phase moraines, and minimal difference in ELA indicate a modest variation in the mass balance of the Maritime Alps glaciers during this time interval. A similar modest variation between LGM and the first recessional phase glacier mass balance is also found throughout the western sector of the Southern Alps but is considerably more pronounced for the glaciers of the central-eastern sectors. This behaviour can be explained by the interplay between the moisture supplied by southern currents sourced in the Western Mediterranean and that advected by the westerlies sourced in the North Atlantic, which affected the various sectors of the Southern Alps differently.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2022.107770","usgsCitation":"Ribolini, A., Spagnolo, M., Cyr, A.J., and Federici, P.R., 2022, Last Glacial Maximum and early deglaciation in the Stura Valley, southwestern European Alps: Quaternary Science Reviews, v. 295, 107770, 17 p., https://doi.org/10.1016/j.quascirev.2022.107770.","productDescription":"107770, 17 p.","ipdsId":"IP-140334","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":446241,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2022.107770","text":"Publisher Index Page"},{"id":435668,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HCE4EC","text":"USGS data release","linkHelpText":"Data release for cosmogenic beryllium-10 exposure ages of moraine boulders in the Stura Valley, Maritime Alps, northwestern Italy"},{"id":407853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy, France","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 7.305908203125,\n 43.97700467496408\n ],\n [\n 7.80029296875,\n 43.97700467496408\n ],\n [\n 7.80029296875,\n 44.32384807250689\n ],\n [\n 7.305908203125,\n 44.32384807250689\n ],\n [\n 7.305908203125,\n 43.97700467496408\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"295","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ribolini, Adriano 0000-0001-5851-8775","orcid":"https://orcid.org/0000-0001-5851-8775","contributorId":291770,"corporation":false,"usgs":false,"family":"Ribolini","given":"Adriano","email":"","affiliations":[{"id":62747,"text":"Department of Earth Sciences, University of Pisa, Italy","active":true,"usgs":false}],"preferred":false,"id":853586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spagnolo, Matteo 0000-0002-2753-338X","orcid":"https://orcid.org/0000-0002-2753-338X","contributorId":291771,"corporation":false,"usgs":false,"family":"Spagnolo","given":"Matteo","email":"","affiliations":[{"id":62748,"text":"Department of Geography & Environment, University of Aberdeen, UK","active":true,"usgs":false}],"preferred":false,"id":853587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cyr, Andrew J. 0000-0003-2293-5395 acyr@usgs.gov","orcid":"https://orcid.org/0000-0003-2293-5395","contributorId":3539,"corporation":false,"usgs":true,"family":"Cyr","given":"Andrew","email":"acyr@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":853588,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Federici, Paolo Roberto","contributorId":297167,"corporation":false,"usgs":false,"family":"Federici","given":"Paolo","email":"","middleInitial":"Roberto","affiliations":[{"id":64311,"text":"retired, Department of Earth Sciences, University of Pisa, Italy","active":true,"usgs":false}],"preferred":false,"id":853589,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237716,"text":"70237716 - 2022 - Urbanization of grasslands in the Denver area affects streamflow responses to rainfall events","interactions":[],"lastModifiedDate":"2022-10-20T11:54:45.648562","indexId":"70237716","displayToPublicDate":"2022-10-03T06:52:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Urbanization of grasslands in the Denver area affects streamflow responses to rainfall events","docAbstract":"A thorough understanding of how urbanization affects stream hydrology is crucial for effective and sustainable water management, particularly in rapidly urbanizing regions. This study presents a comprehensive analysis of changes in streamflow response to rainfall events across a rural to urban gradient in the semi-arid area of Denver, Colorado. We used 8 years of April to October instantaneous streamflow data in 21 watersheds ranging in size from 0.8 to 90 km2 and with impervious areas ranging from 1% to 47%. With these data, we applied a semi-automated method to identify a total of 2877 streamflow responses, which were analysed for event-based metrics of peak flow, runoff depth, runoff to rainfall ratio, time to peak, duration and number of streamflow responses to rainfall events. We also determined whether streamflow responses could be predicted by a precipitation threshold. Watersheds with >10% impervious cover had a precipitation threshold of 1–2 mm/hr needed to produce a streamflow response, compared to thresholds of 4–36 mm/hr for watersheds with less than 10% impervious surface cover. This lower precipitation threshold in more impervious watersheds led to more frequent streamflow responses. On average, streamflow responses had shorter duration and higher peak flows in watersheds with more impervious surface cover. In contrast to other regions, runoff depth, runoff to rainfall ratio and time to peak either gave mixed results or did not vary significantly with imperviousness. These alterations in streamflow response to rainfall events indicate the specific ways that urban development changes how streams respond to rain events in a semi-arid setting. This work points to the need for local adaptation of stormwater management to mitigate the effects of streamflow changes with urbanization.
Cobalt is an indispensable element for the manufacture of strategic technologies including advanced batteries, jet engines, rare-earth permanent magnets, petroleum catalysts, and tool parts that enable construction, manufacturing, and mining. Cobalt routinely scores high in mineral supply risk assessments due to the concentration of cobalt mine production in the Democratic Republic of the Congo (DRC). This stands in contrast to the fact that DRC cobalt mine production had a 20% compound annual growth rate from 1995 through 2020—in large part due to investments by Chinese firms beginning in the mid-2000s. Given this continuous growth, one may ask why this supply is perceived to be so risky. This analysis illuminates the causes of historic disruptions to DRC cobalt mine and refinery production by analyzing country-level production, historical reports, and cobalt prices back to 1924. The results indicate that the main causes of supply disruptions were damage to transportation routes, underinvestment in maintaining nationalized mining assets, and the disintegration of the DRC economy during the early 1990s. On the other hand, cobalt mine production increased 50% from 1977 to 1979 despite two secessionist conflicts in DRC's cobalt producing region and increased seven-fold from 1996 to 2003 despite two African wars over the DRC and its resources. These results indicate that—barring another economic disintegration or mining industry nationalization—DRC mine production will likely continue to be the dominant supplier of the world's growing demand for cobalt in lithium-ion batteries. These results also indicate that sustained development of transportation (and other) infrastructure in Africa, as well as support for good governance in the DRC may prove key to the continued stability of DRC cobalt mine supplies.
The Bushy Park Reservoir is a relatively shallow impoundment in southeastern South Carolina. The reservoir, located under a semi-tropical climate, is the principal water supply for the city of Charleston, South Carolina, and the surrounding areas including the Bushy Park Industrial Complex. Although there was an adequate supply of freshwater in the reservoir in 2022, water-quality concerns are present over taste-and-odor and saltwater-intrusion issues. From 2013 to 2015, the U.S. Geological Survey (USGS), in cooperation with the Charleston Water System, engaged in a multi-year study of the hydrology and hydrodynamics of Bushy Park Reservoir to better understand factors affecting water-quality conditions in the reservoir. As part of this study, Charleston Water System worked with Tetra Tech, Inc., a consulting and engineering firm, to develop a Bushy Park Reservoir hydrodynamic and water-quality modeling framework, built upon earlier efforts by both Tetra Tech and the U.S. Army Corps of Engineers. At the completion of the new modeling framework, the USGS was requested to evaluate the calibrated hydrodynamic and water-quality model.
The Bushy Park Reservoir Environmental Fluid Dynamics Code (EFDC) model was calibrated for the time period from January 1, 2012, to December 31, 2015. The general modeling approach for the newly revised modeling framework, as briefly detailed in this report, was developed with EFDC. The EFDC is a grid-based modeling package that can simulate three-dimensional flow, transport, and water quality in surface-water systems. This report evaluated the capacity of Tetra Tech’s Bushy Park EFDC model to simulate water discharge, water circulation, surface elevations, temperature, salinity, and other water-quality parameters.
The USGS model review focused specifically on the following criteria: (1) determine if the model, with additional effort, could be developed into an adequate planning tool for Bushy Park Reservoir; (2) assess the capacity of the model to specifically address water-quality issues in the reservoir related to taste-and-odor and saltwater intrusions; and, (3) evaluate three preliminary water-management scenarios related to reduced water withdrawals in the reservoir and the effect on saltwater intrusion.
Overall, the model was able to simulate discharge, flow velocity, and water-surface elevations with generally good agreement between the simulated and measured values. Specifically, the model was able to demonstrate good agreement for discharge at two USGS continuous discharge locations (USGS station 02172002; USGS station 02172040), with Wilmott index of agreements of 0.86 and 0.75, respectively. A total of seven USGS streamgages, located on the West Branch of the Cooper River, Durham Canal, and the Cooper River, were available for water-surface elevations, with index of agreements ranging from 0.74 to 0.99. However, model-simulated water-surface elevation ranges were appreciably high (compared to measured ranges) for two locations near Pinopolis Dam, farthest upstream on the West Branch of the Cooper River. This result may indicate that too much simulated tidal energy propagated through the model domain.
For water temperature, 16 calibration stations were available for at least part of the 4-year simulation. The index of agreement range for temperature comparisons was from 0.95 to 1.00, indicating excellent agreement between the measured and simulated results. One of the primary future applications for the Bushy Park Reservoir EFDC model is to determine the extent of saltwater intrusions. A wide range in the salinity prediction quality was simulated with the model. The prediction quality ranged from an index of agreement of 0.15 at Cooper River approximately 2.75 miles southeast of the Tee, South Carolina, to 0.92 at West Branch Cooper River near Moncks Corner, South Carolina. Although the model did not accurately simulate some of the larger salinity deviations resulting during individual hydrologic events, the seasonal salinity trends were adequately simulated with the model during the study period (2012–15). Therefore, it may be difficult to simulate extreme hydrologic events, such as during large storms, where high salinity water is exchanged with Bushy Park Reservoir. There was agreement in model simulation with the measured data either on the quantitative index of agreement values or qualitative agreement in the seasonal salinity data trends.
For water quality, the index of agreement values were generally low for total nitrogen, ammonia, nitrate, total Kjeldahl nitrogen, total phosphorus, and orthophosphate. Although general trends were adequately simulated at specific stations, particularly for Bushy Park Reservoir, the model-simulated fit was low across all the constituents described above with index of agreements usually below 0.50. A limitation for simulating nutrient concentrations across the model domain was the lack of characterization for the constituents directly entering Bushy Park Reservoir, or the lack of data directly attributed to the boundary condition (for example, the Cooper River). The other two calibrated water-quality constituents (besides the nutrients mentioned above) were dissolved oxygen and chlorophyll a. Dissolved oxygen varied from index of agreement values from 0.58 to 0.94 for 11 stations, generally indicating agreement with the available measured data. Chlorophyll a, calibrated for seven stations, had a wider range from 0.11 to 0.74 for the index of agreement.
With the current modeling framework, taste-and-odor events, related to cyanobacterial blooms, cannot be directly simulated. However, indirect estimates of cyanobacteria concentrations may be obtained by using the chlorophyll a model outputs, which represent total phytoplankton biomass, and the phytoplankton biovolume data by group (diatoms, green algae, cyanobacteria and others) collected from 2012 to 2015. For the Bushy Park Reservoir modeling framework to be used directly for taste-and-odor issues, cyanobacteria must be simulated and calibrated based on observations of cyanobacteria biomass concentrations. In addition to the cyanobacteria sampling conducted within the reservoir between 2012 and 2015, the new model calibration would also require new algae biomass data-collection efforts to characterize the external sources of cyanobacteria entering the Bushy Park Reservoir from tributaries, as well as the internal cycling, production, and decay of cyanobacteria in the hydrologic system.
Further improvements to the EFDC model would include expanding the collection of boundary condition datasets, such as water-quality monitoring to determine improved nutrient loads into the model domain. Along with improved water-quality monitoring for the major boundary conditions, continuous discharge, for both Foster Creek and the Back River, would further constrain the flow balance and the loads into Bushy Park Reservoir. In addition to better boundary-condition characterization, it is important to better characterize possible shortcomings specifically to the model domain, such as the grid resolution, bathymetry, and numerical hydrodynamic errors. Further consideration of the model may involve a sensitivity analysis to determine if errors in the simulation outputs, such as discharge, water-surface elevations, and salinity, were more likely caused by poor boundary condition characterization or, specifically, the model setup.
Three model scenarios were run with the revised Bushy Park Reservoir model: (1) reduced withdrawals from one of the large intake-discharge locations for Bushy Park Reservoir, the Williams Station; (2) elevated (above background levels) ocean water level causing saltwater intrusion from the ocean through Durham Canal into Bushy Park Reservoir; and (3) overtopping of the Back River Dam at the southernmost end of Bushy Park Reservoir. For the reduced withdrawals scenarios, the largest shift in flow resulted near the Williams Station intake, with the next largest flow change at the southern end of Bushy Park Reservoir, and a net increase in flow out of the Bushy Park Reservoir to the Cooper River by way of the Durham Canal. The effect resulting from scenario 3 on water quality and salinity was small, with larger increases for dissolved oxygen than other constituents at several monitoring stations. For the two scenarios related to saltwater intrusion (including dam overtopping), the changes in salinity generally were found to dissipate in the following 2 weeks and generally back to baseline salinity conditions within 3 months. This result did vary depending on the severity of the storm or length of the dam overtopping event.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221079","collaboration":"Prepared in cooperation with Charleston Water System","usgsCitation":"Smith, E.A., Akasapu-Smith, M., Petkewich, M.D., and Conrads, P.A., 2022, Evaluation of the Bushy Park Reservoir three-dimensional hydrodynamic and water-quality model, South Carolina, 2012–15: U.S. Geological Survey Open-File Report 2022–1079, 35 p., https://doi.org/10.3133/ofr20221079.","productDescription":"Report: ix, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087955","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501828,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113612.htm","linkFileType":{"id":5,"text":"html"}},{"id":407629,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1079/coverthb.jpg"},{"id":407630,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1079/ofr20221079.pdf","text":"Report","size":"5.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1079"},{"id":407632,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1079/ofr20221079.XML"},{"id":407633,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1079/images/"},{"id":407634,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NG4NVX","text":"USGS data release","linkHelpText":"Water quality data for Bushy Park Reservoir, South Carolina 2013–2015"},{"id":407631,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221079/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1079"}],"country":"United States","state":"South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.39794921875,\n 32.676372772089806\n ],\n [\n -79.661865234375,\n 32.676372772089806\n ],\n [\n -79.661865234375,\n 33.458942753687616\n ],\n [\n -80.39794921875,\n 33.458942753687616\n ],\n [\n -80.39794921875,\n 32.676372772089806\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
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720 Gracern Road, Suite 129
Columbia, SC 29210
We examined the relative importance of landscape features on estuarine fish trophic structure and dependence on terrestrial organic matter (OMterr) in four barrier island lagoon systems along the Alaskan Beaufort Sea coast. Our study compared two relatively large lagoon systems characterized by high river discharge and relatively free ocean water exchanges (central region near Prudhoe Bay, Alaska) with two highly protected lagoons characterized by low river discharge and limited exchange with ocean waters (eastern region near Kaktovik, Alaska). We hypothesized that freshwater discharge would be a strong determinant of food web structure for both resident marine and diadromous fishes if more discharge increases availability of OMterr relative to lagoons with limited or no river inputs. To consider differences in trophic characteristics in fishes between study regions, we estimated community-wide measures of trophic structure (hereafter, community metrics) and the relative use of OMterr from mixing models using stable isotope composition (δ13C and δ15N; muscle tissue) among 12 species and identified the influences of region and body size. Fish captured in lagoons well protected by barrier islands had more distinct and diverse isotopic niches relative to those in more exposed lagoons based on community metrics. The use of OMterr by nearshore fishes in both regions was substantial and was >50% for diadromous species. Between regions, OMterr use differed in 6 of the 8 species considered but was not consistently higher in one region. The relative importance of OMterr varied with fish size in 7 of 10 species considered, with more OMterr used by smaller individuals. This work highlights the importance of OMterr to Arctic fishes and fisheries, some of which are of subsistence importance, even when feeding grounds are primarily marine. We propose that landscape features, particularly barrier islands, play an important role in structuring nearshore food webs. Barrier islands may provide a previously undocumented ecosystem service of increasing food web complexity, which may promote system resilience.
Complex seismic velocity structure near the earthquake source can affect rupture dynamics and strongly modify the seismic waveforms recorded near the fault. Fault‐zone waves are commonly observed in continental crustal settings but are less clear in subduction zones due to the spatial separation between seismic stations and the plate boundary fault. We observed anomalously long duration S waves from earthquake clusters located near the interface of the subducted Gorda plate north of the Mendocino triple junction. In contrast, earthquakes located just a few kilometers below each cluster show impulsive S waves. A nodal array experiment was conducted around the Northern California Seismic Network station KCT for two months to investigate the origin of the complex S waves. Beamforming analysis shows that the S waves contain three arrivals that have different horizontal slownesses, which we term S1, S2, and S coda. Similar analysis on P waves also show two arrivals with different horizontal slownesses, which we term P1 and P2. P1 and S1 have larger horizontal slowness than P2 and S2, respectively, indicating that the phase pairs are body waves with different ray paths. Building upon a seismic refraction profile, we construct 1D velocity models and test different thicknesses and VP/VS ratios for the subducted oceanic crust. The arrival times and relative slownesses of P1/P2 and S1/S2 phases indicate that they are the direct and the Moho reflected phases, respectively. Their properties are consistent with a crustal thickness of ∼6 km and a moderate VP/VS ratio (∼1.8). The S coda is more difficult to characterize but has a clear dominant frequency that likely reflects the near‐source velocity and attenuation structure. Our study indicates that waveforms from earthquakes near the interface of the subducted slab can be used to infer detailed structural information about the plate‐boundary zone at seismogenic depths.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120210261","usgsCitation":"Gong, J., and McGuire, J.J., 2022, Waveform signatures of earthquakes located close to the subducted Gorda Plate interface: Bulletin of the Seismological Society of America, v. 112, no. 5, p. 2440-2453, https://doi.org/10.1785/0120210261.","productDescription":"14 p.","startPage":"2440","endPage":"2453","ipdsId":"IP-133636","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":419498,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Gorda Plate interface","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124,\n 41\n ],\n [\n -125.5,\n 41\n ],\n [\n -125.5,\n 40\n ],\n [\n -124,\n 40\n ],\n [\n -124,\n 41\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"112","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gong, Jianhua","contributorId":317847,"corporation":false,"usgs":false,"family":"Gong","given":"Jianhua","email":"","affiliations":[{"id":34004,"text":"Scripps Institute of Oceanography","active":true,"usgs":false}],"preferred":false,"id":879443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":220939,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":879444,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70247723,"text":"70247723 - 2022 - Limits to coseismic landslides triggered by Cascadia Subduction Zone earthquakes","interactions":[],"lastModifiedDate":"2023-08-15T14:51:21.183298","indexId":"70247723","displayToPublicDate":"2022-10-02T09:47:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Limits to coseismic landslides triggered by Cascadia Subduction Zone earthquakes","docAbstract":"Landslides are a significant hazard and dominant feature throughout the landscape of the Pacific Northwest. However, the hazard and risk posed by coseismic landslides triggered by great Cascadia Subduction Zone (CSZ) earthquakes is highly uncertain due to a lack of local and global data. Despite a wealth of other geologic evidence for past earthquakes on the Cascadia Subduction Zone, no landslides have been definitively linked to such earthquakes, even in areas otherwise highly susceptible to failure. While shallow landslides may not leave a lasting topographical signature in the landscape, there are thousands of deep-seated landslides in Cascadia, and these deposits often persist for hundreds of years and multiple earthquake cycles. Synthesizing newly developed inventories of dated large deep-seated landslides in the Oregon Coast Range, we use statistical methods to estimate the proportion of these types of landslides that could have been triggered during past great Cascadia Subduction Zone earthquakes. Statistical analysis of high-precision dendrochronology ages of landslide-dammed lakes and surface roughness-dated bedrock landslides reveal Cascadia Subduction Zone earthquakes may have triggered 0–15 % of large deep-seated landslides in the Oregon Coast Range over multiple earthquake cycles. Our results refine estimates from previous studies and further suggest that coseismic triggering accounts for a small fraction of the total deep-seated bedrock landslides mapped in coastal Cascadia. However, if the real rate of coseismic landslide triggering during CSZ earthquakes is near our estimated upper bound for the 1700 CSZ earthquake, we estimate up to 2400 coseismic large deep-seated landslides could occur in the Oregon Coast Range in a single earthquake. These findings suggest Cascadia is consistent with global observations from other subduction zones and that coseismic landslides may still represent a serious geohazard in the region.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2022.108477","usgsCitation":"Grant, A.R., Struble, W., and LaHusen, S.R., 2022, Limits to coseismic landslides triggered by Cascadia Subduction Zone earthquakes: Geomorphology, v. 418, 108477, 9 p., https://doi.org/10.1016/j.geomorph.2022.108477.","productDescription":"108477, 9 p.","ipdsId":"IP-135543","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":419819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Oregon Coast Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.28385322714739,\n 46.162939083720374\n ],\n [\n -124.07715534899053,\n 46.16100010342029\n ],\n [\n -124.77236586647153,\n 42.306995247191736\n ],\n [\n -122.40618418763233,\n 42.306995247191736\n ],\n [\n -122.28385322714739,\n 46.162939083720374\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"418","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":880166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Struble, William 0000-0002-8163-5088","orcid":"https://orcid.org/0000-0002-8163-5088","contributorId":241913,"corporation":false,"usgs":false,"family":"Struble","given":"William","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":880167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaHusen, Sean Richard 0000-0003-4246-4439","orcid":"https://orcid.org/0000-0003-4246-4439","contributorId":294677,"corporation":false,"usgs":true,"family":"LaHusen","given":"Sean","email":"","middleInitial":"Richard","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":880168,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237667,"text":"70237667 - 2022 - Climate and land use driven ecosystem homogenization in the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2022-10-18T14:46:34.062232","indexId":"70237667","displayToPublicDate":"2022-10-02T09:39:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Climate and land use driven ecosystem homogenization in the Prairie Pothole Region","docAbstract":"The homogenization of freshwater ecosystems and their biological communities has emerged as a prevalent and concerning phenomenon because of the loss of ecosystem multifunctionality. The millions of prairie-pothole wetlands scattered across the Prairie Pothole Region (hereafter PPR) provide critical ecosystem functions at local, regional, and continental scales. However, an estimated loss of 50% of historical wetlands and the widespread conversion of grasslands to cropland make the PPR a heavily modified landscape. Therefore, it is essential to understand the current and potential future stressors affecting prairie-pothole wetland ecosystems in order to conserve and restore their functions. Here, we describe a conceptual model that illustrates how (a) historical wetland losses, (b) anthropogenic landscape modifications, and (c) climate change interact and have altered the variability among remaining depressional wetland ecosystems (i.e., ecosystem homogenization) in the PPR. We reviewed the existing literature to provide examples of wetland ecosystem homogenization, provide implications for wetland management, and identify informational gaps that require further study. We found evidence for spatial, hydrological, chemical, and biological homogenization of prairie-pothole wetlands. Our findings indicate that the maintenance of wetland ecosystem multifunctionality is dependent on the preservation and restoration of heterogenous wetland complexes, especially the restoration of small wetland basins.
","language":"English","publisher":"MPDI","doi":"10.3390/w14193106","usgsCitation":"McLean, K., Mushet, D., and Sweetman, J., 2022, Climate and land use driven ecosystem homogenization in the Prairie Pothole Region: Water, v. 14, no. 19, 3106, 19 p., https://doi.org/10.3390/w14193106.","productDescription":"3106, 19 p.","ipdsId":"IP-142441","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446248,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14193106","text":"Publisher Index Page"},{"id":408480,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alberta, Iowa, Manitoba, Minnesota, Montana, North Dakota, Saskatchewan, South Dakota","otherGeospatial":"Prairie Potholes Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.712890625,\n 43.58039085560784\n ],\n [\n -94.74609375,\n 41.50857729743935\n ],\n [\n -92.548828125,\n 41.77131167976407\n ],\n [\n -92.900390625,\n 43.32517767999296\n ],\n [\n -94.04296874999999,\n 45.460130637921004\n ],\n [\n -95.537109375,\n 48.45835188280866\n ],\n [\n -96.85546875,\n 49.61070993807422\n ],\n [\n -96.767578125,\n 50.401515322782366\n ],\n [\n -97.55859375,\n 51.069016659603896\n ],\n [\n -97.998046875,\n 50.51342652633956\n ],\n [\n -99.140625,\n 51.12421275782688\n ],\n [\n -99.931640625,\n 51.45400691005982\n ],\n [\n -102.216796875,\n 52.696361078274485\n ],\n [\n -104.501953125,\n 54.213861000644926\n ],\n [\n -107.22656249999999,\n 54.41892996865827\n ],\n [\n -111.533203125,\n 55.57834467218206\n ],\n [\n -114.78515624999999,\n 54.97761367069628\n ],\n [\n -115.13671875,\n 52.74959372674114\n ],\n [\n -115.13671875,\n 50.90303283111257\n ],\n [\n -113.90625,\n 49.095452162534826\n ],\n [\n -111.796875,\n 48.22467264956519\n ],\n [\n -110.56640625,\n 48.69096039092549\n ],\n [\n -109.16015624999999,\n 48.45835188280866\n ],\n [\n -107.40234375,\n 48.80686346108517\n ],\n [\n -105.380859375,\n 48.980216985374994\n ],\n [\n -101.77734374999999,\n 47.989921667414194\n ],\n [\n -100.986328125,\n 47.754097979680026\n ],\n [\n -100.37109375,\n 46.800059446787316\n ],\n [\n -100.546875,\n 45.460130637921004\n ],\n [\n -99.66796875,\n 43.96119063892024\n ],\n [\n -98.96484375,\n 43.45291889355465\n ],\n [\n -96.85546875,\n 42.8115217450979\n ],\n [\n -95.712890625,\n 43.58039085560784\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"19","noUsgsAuthors":false,"publicationDate":"2022-10-02","publicationStatus":"PW","contributors":{"authors":[{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248468,"corporation":false,"usgs":true,"family":"Mushet","given":"David M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sweetman, Jon","contributorId":298028,"corporation":false,"usgs":false,"family":"Sweetman","given":"Jon","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":854917,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239733,"text":"70239733 - 2022 - Chemical geodynamics insights from a machine learning approach","interactions":[],"lastModifiedDate":"2023-01-16T19:24:54.719451","indexId":"70239733","displayToPublicDate":"2022-10-01T13:22:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Chemical geodynamics insights from a machine learning approach","docAbstract":"The radiogenic isotope heterogeneity of oceanic basalts is often assessed using 2D isotope ratio diagrams. But because the underlying data are at least six dimensional (87Sr/86Sr, 143Nd/144Nd, 176Hf/177Hf, and 208,207,206Pb/204Pb), it is important to examine isotopic affinities in multi-dimensional data space. Here, we apply t-distributed stochastic neighbor embedding (t-SNE), a multi-variate statistical data analysis technique, to a recent compilation of radiogenic isotope data of mid ocean ridge (MORB) and ocean island basalts (OIB). The t-SNE results show that the apparent overlap of MORB-OIB data trends in 2-3D isotope ratios diagrams does not exist in multi-dimensional isotope data space, revealing that there is no discrete “component” that is common to most MORB-OIB mantle sources on a global scale. Rather, MORB-OIB sample stochastically distributed small-scale isotopic heterogeneities. Yet, oceanic basalts with the same isotopic affinity, as identified by t-SNE, delineate several globally distributed regional domains. In the regional geodynamic context, the isotopic affinity of MORB and OIB is caused by capturing of actively upwelling mantle by adjacent ridges, and thus melting of mantle with similar origin in on, near, and off-ridge settings. Moreover, within a given isotopic domain, subsidiary upwellings rising from a common deep mantle root often feed OIB volcanism over large surface areas. Overall, the t-SNE results define a fundamentally new basis for relating isotopic variations in oceanic basalts to mantle geodynamics, and may launch a 21st century era of “chemical geodynamics.”
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GC010606","usgsCitation":"Stracke, A., Willig, M., Genske, F., Béguelin, P., and Todd, E., 2022, Chemical geodynamics insights from a machine learning approach: Geochemistry, Geophysics, Geosystems, v. 23, no. 10, e2022GC010606, 41 p., https://doi.org/10.1029/2022GC010606.","productDescription":"e2022GC010606, 41 p.","ipdsId":"IP-142586","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":446253,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gc010606","text":"Publisher Index Page"},{"id":411964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-10-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Stracke, Andreas","contributorId":216038,"corporation":false,"usgs":false,"family":"Stracke","given":"Andreas","email":"","affiliations":[{"id":39353,"text":"Westfälische Wilhelms Universität, Institüt für Mineralogie, Münster, Germany","active":true,"usgs":false}],"preferred":false,"id":861679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willig, M.","contributorId":300990,"corporation":false,"usgs":false,"family":"Willig","given":"M.","affiliations":[{"id":65268,"text":"Institut für Mineralogie, Westfälische Wilhelms-Universität Münster","active":true,"usgs":false}],"preferred":false,"id":861680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Genske, F.","contributorId":300991,"corporation":false,"usgs":false,"family":"Genske","given":"F.","email":"","affiliations":[{"id":65268,"text":"Institut für Mineralogie, Westfälische Wilhelms-Universität Münster","active":true,"usgs":false}],"preferred":false,"id":861681,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Béguelin, P.","contributorId":300992,"corporation":false,"usgs":false,"family":"Béguelin","given":"P.","affiliations":[{"id":65268,"text":"Institut für Mineralogie, Westfälische Wilhelms-Universität Münster","active":true,"usgs":false}],"preferred":false,"id":861682,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Todd, Erin 0000-0002-4871-9730 etodd@usgs.gov","orcid":"https://orcid.org/0000-0002-4871-9730","contributorId":202811,"corporation":false,"usgs":true,"family":"Todd","given":"Erin","email":"etodd@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":861683,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240242,"text":"70240242 - 2022 - Storeria occipitomaculata (Red-bellied Snake)","interactions":[],"lastModifiedDate":"2023-02-02T16:28:20.557922","indexId":"70240242","displayToPublicDate":"2022-10-01T10:27:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1898,"text":"Herpetological Review","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Storeria occipitomaculata (Red-bellied Snake)","title":"Storeria occipitomaculata (Red-bellied Snake)","docAbstract":"STORERIA OCCIPITOMACULATA (Red-bellied Snake). USA: LOUISIANA: St. Mary Parish: Bayou Teche National Wildlife Refuge (29.69425N, 91.46701W; WGS 84). 18 August 2022. William C. Carroll and Aidan G. Phillips. Verified by Coleman M. Sheehy III. Florida Museum of Natural History, University of Florida (UF 193423; photo voucher). Adult photographed in leaf litter in a wet bottomland hardwood forest with a mixed composition of hardwood trees and Dwarf Palmetto (Sabal minor). New parish record (Dundee and Rossman 1989. The Amphibians and Reptiles of Louisiana. Louisiana State University Press, Baton Rouge, Louisiana. 300 pp.). The snake was found 68.5 km to the east-southeast from the nearest other documented specimen in Vermilion Parish (UF 177730; Muse et al. 2016. Herpetol. Rev. 47:266). This record is the second documentation of S. occipitomaculata in a coastal Louisiana parish (Muse et al. 2016, op. cit.). These two recent findings challenge our previous understanding that this species is absent from coastal parishes (Boundy and Carr 2017. Amphibians & Reptiles of Louisiana: An Identification and Reference Guide. Louisiana State University Press, Baton Rouge, Louisiana. 282 pp.). Storeria occipitomaculata is fossorial and can be difficult to locate, but these two recent records suggest additional populations may yet be discovered where suitable forested habitat exists along the coast.","language":"English","publisher":"Society for the Study of Amphibians and Reptiles (SSAR)","usgsCitation":"Phillips, A.G., Carroll, W.C., and Glorioso, B., 2022, Storeria occipitomaculata (Red-bellied Snake): Herpetological Review, v. 53, no. 4.","productDescription":"1 p.","startPage":"632","ipdsId":"IP-145481","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":412624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":412591,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://ssarherps.org/herpetological-review-pdfs/"}],"country":"United States","state":"Louisiana","county":"St. Mary Parish","otherGeospatial":"Bayou Teche National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.47,\n 29.7\n ],\n [\n -91.47,\n 29.69\n ],\n [\n -91.46,\n 29.69\n ],\n [\n -91.46,\n 29.7\n ],\n [\n -91.47,\n 29.7\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"53","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Phillips, Aidan G. 0000-0003-4814-1921","orcid":"https://orcid.org/0000-0003-4814-1921","contributorId":301920,"corporation":false,"usgs":false,"family":"Phillips","given":"Aidan","email":"","middleInitial":"G.","affiliations":[{"id":65363,"text":"Student Services Contractor, Wetland and Aquatic Research Center","active":true,"usgs":false}],"preferred":false,"id":863061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carroll, William C. 0000-0002-9586-3153","orcid":"https://orcid.org/0000-0002-9586-3153","contributorId":301921,"corporation":false,"usgs":false,"family":"Carroll","given":"William","email":"","middleInitial":"C.","affiliations":[{"id":65363,"text":"Student Services Contractor, Wetland and Aquatic Research Center","active":true,"usgs":false}],"preferred":false,"id":863062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glorioso, Brad 0000-0002-5400-7414","orcid":"https://orcid.org/0000-0002-5400-7414","contributorId":219360,"corporation":false,"usgs":true,"family":"Glorioso","given":"Brad","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":863063,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237649,"text":"70237649 - 2022 - Hydrologic restoration decreases greenhouse gas emissions from shrub bog peatlands in southeastern US","interactions":[],"lastModifiedDate":"2022-10-18T15:27:57.063434","indexId":"70237649","displayToPublicDate":"2022-10-01T10:24:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic restoration decreases greenhouse gas emissions from shrub bog peatlands in southeastern US","docAbstract":"Peatlands play a disproportionate role in the global carbon cycle. However, many peatlands have been ditched to lower the water table and converted into agriculture, which contributes to anthropogenic greenhouse gas emissions. Hydrologic restoration of drained peatlands could offset greenhouse gas emissions from these actions, but field examples that consider various greenhouse gases are still rare. Here, we examined emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from soils in drained shrub bogs in North Carolina, USA, before and after hydrologic restoration. We used static chamber methods and a before-and-after, control-impact (BACI) experimental design. We found that hydrologic manipulation (akin to restoration) increased water table levels by 65%, even with the impact of two hurricanes before and one after hydrologic manipulation. Increased water table levels led to a 58% decrease in CO2 fluxes, and an increase in CH4 (251%) and N2O fluxes (85%). Water table depth and soil temperature explained 43% of variation in CO2, while water table depth explained 25% and 18% of variation in CH4 and N2O fluxes, respectively. Despite the increases in CH4 and N2O, the higher magnitude of fluxes and large decline in CO2 lead to an overall lowering of greenhouse gas emissions after hydrologic restoration. Our results suggest that raising the water table in this shrub bog peatland decreased overall greenhouse gas emissions, illustrating that hydrologic restoration of peatlands can be a valuable climate mitigation practice.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-022-01605-y","usgsCitation":"Armstrong, L., Peralta, A., Krauss, K., Cormier, N., Moss, R., Soderholm, E., McCall, A., Pickens, C., and Ardon, M., 2022, Hydrologic restoration decreases greenhouse gas emissions from shrub bog peatlands in southeastern US: Wetlands, v. 42, 81, 10 p., https://doi.org/10.1007/s13157-022-01605-y.","productDescription":"81, 10 p.","ipdsId":"IP-135745","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":408490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Pocosin Lakes National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.61453247070312,\n 35.60930140634475\n ],\n [\n -76.16958618164062,\n 35.60930140634475\n ],\n [\n -76.16958618164062,\n 35.862343734896484\n ],\n [\n -76.61453247070312,\n 35.862343734896484\n ],\n [\n -76.61453247070312,\n 35.60930140634475\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","noUsgsAuthors":false,"publicationDate":"2022-10-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Armstrong, Luise","contributorId":298009,"corporation":false,"usgs":false,"family":"Armstrong","given":"Luise","email":"","affiliations":[{"id":36317,"text":"East Carolina University","active":true,"usgs":false}],"preferred":false,"id":854835,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peralta, Ariane","contributorId":298010,"corporation":false,"usgs":false,"family":"Peralta","given":"Ariane","email":"","affiliations":[{"id":36317,"text":"East Carolina University","active":true,"usgs":false}],"preferred":false,"id":854836,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":854837,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cormier, N. 0000-0003-2453-9900","orcid":"https://orcid.org/0000-0003-2453-9900","contributorId":221147,"corporation":false,"usgs":false,"family":"Cormier","given":"N.","affiliations":[{"id":16788,"text":"Macquarie University","active":true,"usgs":false}],"preferred":false,"id":854838,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moss, Rebecca 0000-0002-7599-9758 mossr@usgs.gov","orcid":"https://orcid.org/0000-0002-7599-9758","contributorId":169722,"corporation":false,"usgs":true,"family":"Moss","given":"Rebecca","email":"mossr@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":854839,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Soderholm, Eric","contributorId":298011,"corporation":false,"usgs":false,"family":"Soderholm","given":"Eric","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":854840,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McCall, Aaron","contributorId":298012,"corporation":false,"usgs":false,"family":"McCall","given":"Aaron","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":854841,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pickens, Christine","contributorId":298013,"corporation":false,"usgs":false,"family":"Pickens","given":"Christine","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":854842,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ardon, Marcelo","contributorId":298014,"corporation":false,"usgs":false,"family":"Ardon","given":"Marcelo","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":854843,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70240286,"text":"70240286 - 2022 - Effects of shady environments on fish collective behavior","interactions":[],"lastModifiedDate":"2023-02-03T16:20:33.544985","indexId":"70240286","displayToPublicDate":"2022-10-01T10:13:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Effects of shady environments on fish collective behavior","docAbstract":"Despite significant efforts devoted to understanding the underlying complexity and emergence of collective movement in animal groups, the role of different external settings on this type of movement remains largely unexplored. Here, by combining time series analysis and complex network tools, we present an extensive investigation of the effects of shady environments on the behavior of a fish species (Silver Carp Hypophthalmichthys molitrix) within earthen ponds. We find that shade encourages fish residence during daylight hours, but the degree of preference for shade varies substantially among trials and ponds. Silver Carp are much slower and exhibit lower persistence in their speeds when under shade than out of it during daytime and nighttime, with fish displaying the highest persistence degree and speeds at night. Furthermore, our research shows that shade affects fish schooling behavior by reducing their polarization, number of interactions among individuals, and the stability among local neighbors; however, fish keep a higher local degree of order when under shade compared to nighttime positions.
","language":"English","publisher":"Springer","doi":"10.1038/s41598-022-22515-3","usgsCitation":"Ribeiro, H.V., Acre, M.R., Faulkner, J., da Cunha, L.R., Lawson, K., Wamboldt, J.J., Brey, M.K., Woodley, C., and Calfee, R.D., 2022, Effects of shady environments on fish collective behavior: Scientific Reports, v. 12, no. 1, 17873, 12 p., https://doi.org/10.1038/s41598-022-22515-3.","productDescription":"17873, 12 p.","ipdsId":"IP-142457","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":446255,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-022-22515-3","text":"Publisher Index Page"},{"id":435670,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XURDHS","text":"USGS data release","linkHelpText":"Silver Carp (Hypophthalmichthys molitrix) locations in earthen ponds with overhead structure"},{"id":412687,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Ribeiro, Haroldo V.","contributorId":301984,"corporation":false,"usgs":false,"family":"Ribeiro","given":"Haroldo","email":"","middleInitial":"V.","affiliations":[{"id":65378,"text":"Departamento de F'isica, Universidade Estadual de Maring","active":true,"usgs":false}],"preferred":false,"id":863237,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acre, Matthew Ross 0000-0002-5417-9523","orcid":"https://orcid.org/0000-0002-5417-9523","contributorId":268034,"corporation":false,"usgs":true,"family":"Acre","given":"Matthew","email":"","middleInitial":"Ross","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863238,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faulkner, Jacob 0000-0002-8109-9107","orcid":"https://orcid.org/0000-0002-8109-9107","contributorId":238279,"corporation":false,"usgs":true,"family":"Faulkner","given":"Jacob","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863239,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"da Cunha, Leonardo R.","contributorId":301985,"corporation":false,"usgs":false,"family":"da Cunha","given":"Leonardo","email":"","middleInitial":"R.","affiliations":[{"id":65378,"text":"Departamento de F'isica, Universidade Estadual de Maring","active":true,"usgs":false}],"preferred":false,"id":863240,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lawson, Katelyn M.","contributorId":201981,"corporation":false,"usgs":false,"family":"Lawson","given":"Katelyn M.","affiliations":[{"id":36314,"text":"University of Florida/IFAS","active":true,"usgs":false}],"preferred":false,"id":863241,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wamboldt, James J. 0000-0003-3043-5198","orcid":"https://orcid.org/0000-0003-3043-5198","contributorId":219060,"corporation":false,"usgs":true,"family":"Wamboldt","given":"James","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":863242,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":863243,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Woodley, Christa M.","contributorId":301986,"corporation":false,"usgs":false,"family":"Woodley","given":"Christa M.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":863244,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Calfee, Robin D. 0000-0001-6056-7023 rcalfee@usgs.gov","orcid":"https://orcid.org/0000-0001-6056-7023","contributorId":1841,"corporation":false,"usgs":true,"family":"Calfee","given":"Robin","email":"rcalfee@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863245,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70237223,"text":"70237223 - 2022 - Puget Sound Spatially Referenced Regression on Watershed Attributes (SPARROW)","interactions":[],"lastModifiedDate":"2026-03-18T15:16:45.794674","indexId":"70237223","displayToPublicDate":"2022-10-01T10:07:50","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":23618,"text":"Quality Assurance Project Plan","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"22-03-109","title":"Puget Sound Spatially Referenced Regression on Watershed Attributes (SPARROW)","docAbstract":"The United States Geological Survey (USGS) and the Washington State Department of Ecology (Ecology) are collaborating on the development of refined, seasonal load estimates of total nitrogen and total phosphorus within watersheds draining to Washington waters of the Salish Sea for the period 2005-2020. The modeling approach for this work is based on SPARROW (Spatially Referenced Regression on Watershed Attributes), a watershed modeling technique developed by the USGS. SPARROW is typically used to estimate stream loads throughout a stream network.
The estimated loads will be used within the context of the Puget Sound Nutrient Source Reduction Project to evaluate the influence of watershed contributions of nutrients throughout the stream network and to marine waters. This quality assurance project plan (QAPP) contains details about the technical approach, observational data, spatial and temporal source data, limitations, and quality assurance procedures that will be employed to develop the SPARROW models so that they can be used to inform additional actions to address excess nutrients.
","language":"English","publisher":"Washington State Department of Ecology (Ecology)","usgsCitation":"Figueroa-Kaminsky, C., Wasielewski, J., McCarthy, S., Schmadel, N., Wise, D., Johnson, Z., and Black, R.W., 2022, Puget Sound Spatially Referenced Regression on Watershed Attributes (SPARROW): Quality Assurance Project Plan 22-03-109, 76 p.","productDescription":"76 p.","ipdsId":"IP-143204","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":501247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501246,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://apps.ecology.wa.gov/publications/SummaryPages/2203109.html"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.52217372194855,\n 48.967370797762754\n ],\n [\n -123.88225129810027,\n 48.967370797762754\n ],\n [\n -123.88225129810027,\n 45.85254256141661\n ],\n [\n -120.52217372194855,\n 45.85254256141661\n ],\n [\n -120.52217372194855,\n 48.967370797762754\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Figueroa-Kaminsky, Cristiana","contributorId":350514,"corporation":false,"usgs":false,"family":"Figueroa-Kaminsky","given":"Cristiana","affiliations":[{"id":25353,"text":"Washington State Department of Ecology","active":true,"usgs":false}],"preferred":false,"id":957136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wasielewski, Jamie K. 0009-0005-7497-3344","orcid":"https://orcid.org/0009-0005-7497-3344","contributorId":344993,"corporation":false,"usgs":false,"family":"Wasielewski","given":"Jamie K.","affiliations":[{"id":82458,"text":"Washington Dept. of Ecology","active":true,"usgs":false}],"preferred":false,"id":957137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCarthy, Sheelagh","contributorId":367235,"corporation":false,"usgs":false,"family":"McCarthy","given":"Sheelagh","affiliations":[],"preferred":false,"id":957138,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmadel, Noah M. 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":957139,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wise, Daniel R. 0000-0002-1215-9612","orcid":"https://orcid.org/0000-0002-1215-9612","contributorId":217259,"corporation":false,"usgs":true,"family":"Wise","given":"Daniel","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":957140,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":957141,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Black, Robert W. 0000-0002-4748-8213 rwblack@usgs.gov","orcid":"https://orcid.org/0000-0002-4748-8213","contributorId":1820,"corporation":false,"usgs":true,"family":"Black","given":"Robert","email":"rwblack@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853672,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237874,"text":"70237874 - 2022 - Getting ahead of flash drought: From early warning to early action","interactions":[],"lastModifiedDate":"2022-10-28T14:41:10.795385","indexId":"70237874","displayToPublicDate":"2022-10-01T09:34:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12798,"text":"Bulletin of American Meteorological Society (BAMS)","active":true,"publicationSubtype":{"id":10}},"title":"Getting ahead of flash drought: From early warning to early action","docAbstract":"Flash droughts, characterized by their unusually rapid intensification, have garnered increasing attention within the weather, climate, agriculture, and ecological communities in recent years due to their large environmental and socioeconomic impacts. Because flash droughts intensify quickly, they require different early warning capabilities and management approaches than are typically used for slower-developing “conventional” droughts. In this essay, we describe an integrated research-and-applications agenda that emphasizes the need to reconceptualize our understanding of flash drought within existing drought early warning systems by focusing on opportunities to improve monitoring and prediction. We illustrate the need for engagement among physical scientists, social scientists, operational monitoring and forecast centers, practitioners, and policy-makers to inform how they view, monitor, predict, plan for, and respond to flash drought. We discuss five related topics that together constitute the pillars of a robust flash drought early warning system, including the development of 1) a physically based identification framework, 2) comprehensive drought monitoring capabilities, and 3) improved prediction over various time scales that together 4) aid impact assessments and 5) guide decision-making and policy. We provide specific recommendations to illustrate how this fivefold approach could be used to enhance decision-making capabilities of practitioners, develop new areas of research, and provide guidance to policy-makers attempting to account for flash drought in drought preparedness and response plans.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-21-0288.1","usgsCitation":"Otkin, J.A., Woloszyn, M., Wang, H., Svoboda, M., Skumanich, M., Pulwarty, R., Lisonbee, J., Hoell, A., Hobbins, M., Haigh, T., and Cravens, A.E., 2022, Getting ahead of flash drought: From early warning to early action: Bulletin of American Meteorological Society (BAMS), v. 103, no. 10, p. E2188-E2202, https://doi.org/10.1175/BAMS-D-21-0288.1.","productDescription":"15 p.","startPage":"E2188","endPage":"E2202","ipdsId":"IP-137074","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467160,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1175/bams-d-21-0288.1","text":"External Repository"},{"id":408858,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Otkin, Jason A.","contributorId":192635,"corporation":false,"usgs":false,"family":"Otkin","given":"Jason","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":856044,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woloszyn, Molly","contributorId":260136,"corporation":false,"usgs":false,"family":"Woloszyn","given":"Molly","email":"","affiliations":[{"id":52519,"text":"NOAA National Integrated Drought Information System","active":true,"usgs":false}],"preferred":false,"id":856045,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Hailan","contributorId":298623,"corporation":false,"usgs":false,"family":"Wang","given":"Hailan","email":"","affiliations":[{"id":64628,"text":"NOAA Climate Prediction Center","active":true,"usgs":false}],"preferred":false,"id":856046,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Svoboda, Mark","contributorId":192357,"corporation":false,"usgs":false,"family":"Svoboda","given":"Mark","email":"","affiliations":[],"preferred":false,"id":856047,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Skumanich, Marina","contributorId":260137,"corporation":false,"usgs":false,"family":"Skumanich","given":"Marina","email":"","affiliations":[{"id":52519,"text":"NOAA National Integrated Drought Information System","active":true,"usgs":false}],"preferred":false,"id":856048,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pulwarty, Roger","contributorId":212144,"corporation":false,"usgs":false,"family":"Pulwarty","given":"Roger","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":856049,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lisonbee, Joel","contributorId":298624,"corporation":false,"usgs":false,"family":"Lisonbee","given":"Joel","email":"","affiliations":[{"id":64629,"text":"NOAA-NIDIS","active":true,"usgs":false}],"preferred":false,"id":856050,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hoell, Andrew","contributorId":145803,"corporation":false,"usgs":false,"family":"Hoell","given":"Andrew","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":856051,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hobbins, Mike 0000-0001-5540-8466","orcid":"https://orcid.org/0000-0001-5540-8466","contributorId":292343,"corporation":false,"usgs":false,"family":"Hobbins","given":"Mike","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":856052,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Haigh, Tonya","contributorId":204248,"corporation":false,"usgs":false,"family":"Haigh","given":"Tonya","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":856053,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cravens, Amanda E. 0000-0002-0271-7967 aecravens@usgs.gov","orcid":"https://orcid.org/0000-0002-0271-7967","contributorId":196752,"corporation":false,"usgs":true,"family":"Cravens","given":"Amanda","email":"aecravens@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":856054,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70239208,"text":"70239208 - 2022 - Survival and growth of four floodplain forest species in an Upper Mississippi River underplanting","interactions":[],"lastModifiedDate":"2023-01-04T15:30:54.592533","indexId":"70239208","displayToPublicDate":"2022-10-01T09:30:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12993,"text":"Tree Planters Notes","active":true,"publicationSubtype":{"id":10}},"title":"Survival and growth of four floodplain forest species in an Upper Mississippi River underplanting","docAbstract":"Forest restoration efforts commonly occur in degraded ecosystems. For the floodplain forests of the Upper Mississippi River, the combination of aging canopy trees and expansion of invasive species such as reed canary grass (Phalaris arundinacea L.) can shift forested ecosystems to open meadows. Before this shift occurs, there may be opportunities to proactively underplant. Our study reports 2-year survival and growth of four tree species (swamp white oak (Quercus bicolor Wild.), silver maple (Acer saccharinum L.), hackberry (Celtis occidentalis L.), and sycamore (Platanus occidentalis L.) planted under a moderate canopy of silver maple (approximately 60 percent overstory cover) across three elevational gradients. Swamp white oak had high survival across all three of the elevational zones and showed limited effects by herbivory or insects. Growth and survival of sycamore and hackberry depended on the elevational zone; sycamore performed better on lower elevational sites and hackberry did better on higher elevational sites. Our results highlight the potential for underplanting in floodplain forests as a proactive restoration strategy, with consideration given to local site conditions.
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","language":"English","publisher":"Nevada Department of Transportation","collaboration":"in cooperation with USDOT Federal Highway Administration","usgsCitation":"Brehme, C.S., Tracey, J.A., Gould, P.R., Rochester, C.J., and Fisher, R., 2022, Internal structural cover and ledges facilitate the use of large underpasses by multiple wildlife species and groups, ii, 52 p.","productDescription":"ii, 52 p.","ipdsId":"IP-145925","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":417088,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417081,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.pooledfund.org/details/study/610"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":221,\"properties\":{\"name\":\"San 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N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":872810,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238656,"text":"70238656 - 2022 - Estuarine Geomorphology, Circulation, and Mixing","interactions":[],"lastModifiedDate":"2022-12-02T13:28:35.628529","indexId":"70238656","displayToPublicDate":"2022-10-01T07:24:39","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"2","title":"Estuarine Geomorphology, Circulation, and Mixing","docAbstract":"To understand the processes affecting the distribution and cycles of particulates, pollutants, nutrients, and organisms in estuaries, it is insufficient to focus solely on the biological and chemical aspects of the processes. Water sources and movements (e.g. evaporation, precipitation, riverine discharge, submarine ground water discharge, wetland hydrology, and tidal exchange) as well as other hydrodynamic aspects of coastal systems, including circulation patterns, stratification, mixing and flushing, must also be considered. When hydrodynamic changes occur quickly relative to biological, geological, and chemical transformations, they become the dominant controlling factors of many ecological processes in estuaries (Officer 1980), and it is now widely recognized that a thorough understanding of the marine estuarine ecology requires comprehensive knowledge and integration of physical processes affecting the system. Using the terminology of a shallow-water oceanographer, this chapter aims to organize, classify, and describe some of these important physical characteristics and processes.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Estuarine Ecology","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Wiley","usgsCitation":"Snedden, G., Cable, J., and Kjerfve, B., 2022, Estuarine Geomorphology, Circulation, and Mixing, chap. 2 of Estuarine Ecology, p. 16-35.","productDescription":"20 p.","startPage":"16","endPage":"35","ipdsId":"IP-128034","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":409988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"3rd edition","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Snedden, Gregg 0000-0001-7821-3709","orcid":"https://orcid.org/0000-0001-7821-3709","contributorId":213411,"corporation":false,"usgs":true,"family":"Snedden","given":"Gregg","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":858212,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cable, Jaye E.","contributorId":299602,"corporation":false,"usgs":false,"family":"Cable","given":"Jaye E.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":false,"id":858213,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kjerfve, Bjorn","contributorId":299603,"corporation":false,"usgs":false,"family":"Kjerfve","given":"Bjorn","email":"","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":858214,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237944,"text":"70237944 - 2022 - Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆","interactions":[],"lastModifiedDate":"2022-11-01T12:04:00.80791","indexId":"70237944","displayToPublicDate":"2022-10-01T06:59:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆","docAbstract":"The state of California, United States of America produces many crop products that are both utilized domestically and exported throughout the world. With nearly 39,000 km2 of croplands, monitoring unintentional and intentional surface water inundation is important for water resource management and flood hazard readiness. We examine inundation dynamics in California croplands from 2003 to 2020 by intersecting monthly surface water maps (n = 216 months) derived using two satellite remote sensing platforms (Landsat and Moderate Resolution Imaging Spectroradiometer [MODIS]) with a high-quality cropland map generated by the California Department of Water Resources. Surface water maps were produced using the Dynamic Surface Water Extent model, in which satellite image pixels are classified into different levels of detection confidence. Our analysis focused on calculating monthly and annual occurrence of “high confidence” water for each satellite collection across eight cropland types and 58 counties. Results indicate that 49.9% (MODIS) to 56.4% (Landsat) of croplands were inundated at least once during the 18-year timespan. Rice crops, due to their unique need of consistent surface water and dominance as a crop type in CA, had the highest proportion of and mean annual inundation area, while citrus crops had the lowest. Mean monthly inundation patterns in most croplands followed California's precipitation patterns with high inundation during the winter and spring rainy season. At the county level, croplands in the southern Central Valley typically had high occurrences of inundation in conjunction with large crop areas. Exposure and sensitivity of inundation for three crop types (citrus, deciduous, and vineyards) that are typically less associated with intentional inundation were geographically variable, but overall were generally highest in counties in the southern Central Valley, California's primary agricultural region. Flood and precipitation related crop insurance claims indicated that rice had the highest mean indemnity payout for any month with damages typically occurring in March and April. Insurance claims were also high in deciduous fruit and nut crops, which had peak damages in February. A comparison between inundation results and insurance claims suggests that the inundation mapped by our process coincides with claim activity. These data elucidate water inundation patterns across the state that can serve to inform farmers, insurers, decision makers, resource managers, and flood mitigation professionals.
The Paleocene-Eocene Thermal Maximum (PETM) is the most pronounced global warming event of the early Paleogene related to atmospheric CO2 increases. It is characterized by negative δ18O and δ13C excursions recorded in sedimentary archives and a transient disruption of the marine biosphere. Sites from the U.S. Atlantic Coastal Plain show an additional small, but distinct δ13C excursion below the onset of the PETM, coined the “pre-onset excursion” (POE), mimicking the PETM-forced environmental perturbations. This study focuses on the South Dover Bridge core in Maryland, where the Paleocene-Eocene transition is stratigraphically constrained by calcareous nannoplankton and stable isotope data, and in which the POE is well-expressed. The site was situated in a middle neritic marine shelf setting near a major outflow of the paleo-Potomac River system. We generated high-resolution benthic foraminiferal assemblage, stable isotope, trace-metal, grain-size and clay mineralogy data. The resulting stratigraphic subdivision of this Paleocene-Eocene transition is placed within a depth transect across the paleoshelf, highlighting that the PETM sequence is relatively expanded. The geochemical records provide detailed insights into the paleoenvironment, developing from a well-oxygenated water column in latest Paleocene to a PETM-ecosystem under severe biotic stress-conditions, with shifts in food supply and temperature, and under dysoxic bottom waters in a more river-dominated setting. Environmental changes started in the latest Paleocene and culminated atthe onset of the PETM, hinting to an intensifying trigger rather than to an instantaneous event at the Paleocene-Eocene boundary toppling the global system.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022PA004475","usgsCitation":"Doubrawa, M., Stassen, P., Robinson, M.M., Babila, T., Zachos, J., and Speijer, R.P., 2022, Shelf ecosystems along the U.S. Atlantic Coastal Plain prior to and during the Paleocene-Eocene Thermal Maximum: Insights into the stratigraphic architecture: Paleoceanography and Paleoclimatology, v. 37, no. 10, e2022PA004475, 21 p., https://doi.org/10.1029/2022PA004475.","productDescription":"e2022PA004475, 21 p.","ipdsId":"IP-138735","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":446262,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://doi.org/10.1029/2022PA004475>).","text":"External Repository"},{"id":423139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, New Jersey, Pennsylvania, Virginia","city":"Ancora, Millville","otherGeospatial":"Atlantic Ocean, Bass River, Mattawoman Creek, South Dover Bridge, Wilson Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.52149499904571,\n 38.01418678915982\n ],\n [\n -73.68652155562353,\n 37.988504538700454\n ],\n [\n -73.53442629157814,\n 40.51190899588033\n ],\n [\n -74.29490261180376,\n 40.51190899588033\n ],\n [\n -75.06624287946097,\n 40.06441158243243\n ],\n [\n -76.5654676250484,\n 39.61395531750057\n ],\n [\n -76.90224999543389,\n 39.10998841782785\n ],\n [\n -77.32594394527337,\n 38.77199315778719\n ],\n [\n -77.52149499904571,\n 38.01418678915982\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"37","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-10-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Doubrawa, Monika","contributorId":332061,"corporation":false,"usgs":false,"family":"Doubrawa","given":"Monika","email":"","affiliations":[{"id":49038,"text":"KU Leuven","active":true,"usgs":false}],"preferred":false,"id":889335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stassen, Peter","contributorId":290269,"corporation":false,"usgs":false,"family":"Stassen","given":"Peter","email":"","affiliations":[{"id":49038,"text":"KU Leuven","active":true,"usgs":false}],"preferred":false,"id":889336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robinson, Marci M. 0000-0002-9200-4097 mmrobinson@usgs.gov","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":332062,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci","email":"mmrobinson@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":889337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Babila, Tali","contributorId":211722,"corporation":false,"usgs":false,"family":"Babila","given":"Tali","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":889338,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zachos, James","contributorId":224075,"corporation":false,"usgs":false,"family":"Zachos","given":"James","affiliations":[],"preferred":false,"id":889339,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Speijer, Robert P.","contributorId":290266,"corporation":false,"usgs":false,"family":"Speijer","given":"Robert","email":"","middleInitial":"P.","affiliations":[{"id":49038,"text":"KU Leuven","active":true,"usgs":false}],"preferred":false,"id":889340,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254932,"text":"70254932 - 2022 - Scat as a source of DNA for population monitoring","interactions":[],"lastModifiedDate":"2024-06-11T21:36:21.796705","indexId":"70254932","displayToPublicDate":"2022-09-30T15:46:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Scat as a source of DNA for population monitoring","docAbstract":"Sampling fecal droppings (scat) to genetically identify individual animals is an established method for monitoring mammal populations and could be highly useful for monitoring reptile populations. Whereas existing protocols for obtaining DNA from reptile scat focus on analyses of whole, fresh scat deposited during animal handling, the collection of scat naturally deposited by reptiles in situ, as required for non-invasive population monitoring, requires protocols to extract highly degraded DNA. Using surface swabs from such scats can reduce PCR inhibition, ecological impacts of removing scat, and zoonotic risks. We report on three related but independently designed studies of DNA analyses from scat swabs of herbivorous reptiles under natural desert conditions: two free-ranging desert tortoise species (Agassiz's desert tortoise, Gopherus agassizii, California, US, and Morafka's desert tortoise, G. morafkai, Arizona, US) and the common chuckwalla (Sauromalus atar) (Arizona, US, and Sonora, MX). We analyzed samples from both tortoise species with the same set of 16 microsatellites and chuckwalla samples with four mtDNA markers; studies also varied in swab preservation medium and DNA extraction method. Microsatellite amplification success per sample, defined as ≥9 loci with amplification, varied between studies, with 15% for Agassiz's desert tortoise and 42% Morafka's desert tortoise. For chuckwallas, we successfully amplified and sequenced 50% of samples. We recovered fragments up to 400 bp for tortoises and 980 bp for chuckwallas from scat swab samples. This study demonstrates that genotypes can successfully be obtained from swabs of herbivorous reptile scat collected in the field under natural environmental conditions and emphasizes that repeat amplifications are necessary for genetic identification of individuals from non-invasive samples.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9415","usgsCitation":"Manning, J.A., Edwards, T., Clemons, J., Leavitt, D.J., Goldberg, C., and Culver, M., 2022, Scat as a source of DNA for population monitoring: Ecology and Evolution, v. 12, ece3.9415, 7 p., https://doi.org/10.1002/ece3.9415.","productDescription":"ece3.9415, 7 p.","ipdsId":"IP-110967","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":446264,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9415","text":"Publisher Index Page"},{"id":429924,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California","otherGeospatial":"Anza-Borrego Desert State Park. Florence Military Reservation","volume":"12","noUsgsAuthors":false,"publicationDate":"2022-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Manning, Jeffrey A.","contributorId":338051,"corporation":false,"usgs":false,"family":"Manning","given":"Jeffrey","email":"","middleInitial":"A.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":902921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edwards, Taylor","contributorId":239460,"corporation":false,"usgs":false,"family":"Edwards","given":"Taylor","affiliations":[{"id":47864,"text":"Genetics Core, University of Arizona, Tucson, AR, USA","active":true,"usgs":false}],"preferred":false,"id":902922,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clemons, John","contributorId":338054,"corporation":false,"usgs":false,"family":"Clemons","given":"John","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":902923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leavitt, Daniel J.","contributorId":338057,"corporation":false,"usgs":false,"family":"Leavitt","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":81077,"text":"U.S. Fish and Wildlife Services","active":true,"usgs":false}],"preferred":false,"id":902924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goldberg, Caren S.","contributorId":289552,"corporation":false,"usgs":false,"family":"Goldberg","given":"Caren S.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":902925,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Culver, Melanie 0000-0001-5380-3059 mculver@usgs.gov","orcid":"https://orcid.org/0000-0001-5380-3059","contributorId":197693,"corporation":false,"usgs":true,"family":"Culver","given":"Melanie","email":"mculver@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902926,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237117,"text":"sir20225091 - 2022 - Assessing the impact of chloride deicer application in the Siskiyou Pass, southern Oregon","interactions":[],"lastModifiedDate":"2022-10-03T11:05:36.759623","indexId":"sir20225091","displayToPublicDate":"2022-09-30T12:22:27","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5091","displayTitle":"Assessing the Impact of Chloride Deicer Application in the Siskiyou Pass, Southern Oregon","title":"Assessing the impact of chloride deicer application in the Siskiyou Pass, southern Oregon","docAbstract":"Chloride deicers have been applied by the Oregon Department of Transportation (ODOT) to Interstate Route 5 (I–5) from the Oregon-California border north to mile marker 10 for several years in the high-elevation area known as the Siskiyou Pass. Magnesium chloride (MgCl2) and sodium chloride (NaCl) are applied to keep the interstate highway safe for drivers and allow for efficient transport of goods and people through adverse weather conditions, particularly snow and ice. The U.S. Geological Survey entered into a cooperative agreement with ODOT to research the effects of chloride deicers in the Carter and Wall Creek watersheds that drain the vicinity of the Siskiyou Pass.
The Stochastic Empirical Loading and Dilution Model (SELDM) was used to estimate combinations of prestorm-streamflow, stormflow, highway-runoff, and event mean constituent concentrations (EMCs), as well as stormwater-constituent loads at sites of interest. The study evaluated the effects of roadway application of chloride deicers on downstream and highway-runoff conditions (particularly EMCs), exceedance rates of criterion maximum concentrations, and concurrent runoff loads of stormwater constituents from a site of interest. SELDM was also used to evaluate the efficiency of hydrograph extension best management practices to reduce peak constituent concentrations. Several SELDM scenarios were developed as sensitivity analyses to evaluate the model benefit of collecting specific local sets of data, such as streamflow, precipitation, highway-runoff and riverine water-quality samples, and volumetric runoff coefficient statistics.
Results of the study showed that for SELDM modeling in the Siskiyou Pass area, (1) the inclusion of local streamflow data is important for obtaining accurate downstream EMCs, (2) the inclusion of precipitation data is important for highway and concurrent runoff load calculations, and (3) water-quality constituent EMC data from highway runoff and upstream stormflows are the most important data to collect for highway runoff and upstream water-quality constituent concentration statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225091","collaboration":"Prepared in cooperation with the Oregon Department of Transportation","usgsCitation":"Stonewall, A.J., Yates, M.C., and Granato, G.E., 2022, Assessing the impact of chloride deicer application in the Siskiyou Pass, southern Oregon: U.S. Geological Survey Scientific Investigations Report 2022–5091, 94 p., https://doi.org/10.3133/sir20225091.","productDescription":"Report: xii, 93 p.; Data Release; 4 Tables","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-107308","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":407662,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_table_3.csv","text":"Table 3","size":"13 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5091 Table 3"},{"id":407660,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5091/coverthb.jpg"},{"id":407661,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5091"},{"id":407666,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_table_17.csv","text":"Table 17","size":"2 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5091 Table 17"},{"id":407668,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_table_26.csv","text":"Table 26","size":"4 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5091 Table 26"},{"id":407670,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5091/images"},{"id":407671,"rank":10,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091.XML"},{"id":407664,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_table_10.csv","text":"Table 10","size":"2 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5091 Table 10"},{"id":407673,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q1PP61","text":"USGS data release","description":"USGS data release","linkHelpText":"Stochastic Empirical Loading and Dilution Model (SELDM) model archive and instructions for the Siskiyou Pass, Oregon"},{"id":407675,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_tables3_10_17_26_csv.zip","text":"Tables 3, 10, 17, and 26 CSV files","size":"7 KB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2022-5091 Tables 3, 10, 17, and 26"},{"id":407674,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5091/sir20225091_tables3_10_17_26.xlsx","text":"Tables 3, 10, 17, and 26 Excel file","size":"36 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5091 Tables 3, 10, 17, and 26"}],"country":"United States","state":"Oregon","otherGeospatial":"Siskiyou Pass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.32,\n 42.02\n ],\n [\n -122.32,\n 42.02\n ],\n [\n -122.32,\n 42.02\n ],\n [\n -122.40,\n 42.08\n ],\n [\n -122.40,\n 42.08\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201