Oceanic transform faults display a unique combination of seismic and aseismic slip behavior, including a large globally averaged seismic deficit, and the local occurrence of repeating magnitude (M)
The U.S. Geological Survey Western Ecological Research Center’s Santa Cruz Field Station, Santa Cruz, California, has been funded by the U.S. Navy to continue monitoring a suite of intertidal black abalone sites at San Nicolas Island, California. The nine rocky intertidal sites were established in 1980 by Glenn VanBlaricom (then of the U.S. Fish and Wildlife Service) to study the potential impact of translocated sea otters on the intertidal black abalone population at the island. The sites were monitored from 1981 to 1997, usually annually or semi-annually. Monitoring resumed in 2001, and regular annual monitoring cycles have been conducted at the sites since then. The study sites became particularly important, from a management perspective, after a virulent disease decimated black abalone populations throughout southern California beginning in the mid-1980s. The disease, withering syndrome, was first observed on San Nicolas Island in 1992 and during the next few years reduced the population there by approximately 99 percent. The species was subsequently listed as endangered under the Endangered Species Act in 2009.
The subject of this report is the 2019 monitoring cycle of the sites and how the current status fits into the long-term data at San Nicolas Island. Since 2001, the monitored population has increased nearly tenfold to approximately 8.7 percent of the pre-disease level. This increase has resulted from generally higher levels of recruitment than seen in the first two decades of monitoring, punctuated by a few high recruitment events. Most of the population growth has been at two of the nine sites (sites 7 and 8). This pattern continued in 2019 with increasing numbers at sites 7 and 8 and the highest number of abalone counted and measured island-wide since 1996. However, counts declined at six of the sites during the last year and the increases in counts at sites 7 and 8 barely offset these losses. Recruitment rates have fallen since a peak in 2017 but 2019 continued to show some additional recruitment. The distance between adjacent black abalone, a metric relevant to potential reproduction, has decreased substantially since it was first consistently measured in 2005. Although sand burial can have devastating localized consequences to black abalone, the sand cover data we collected was not sufficient to suggest an obvious temporal or site-based pattern to sedimentation, and there is no indication that this was a factor in any of the declines recorded in 2019. Continued monitoring of these sites can provide island biologists with species trends to aid in adaptive management of the resource.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201047","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Kenner, M.C., 2020, Black abalone surveys at Naval Base Ventura County, San Nicolas Island, California: 2019, annual report: U.S. Geological Survey Open-File Report 2020–1047, 41 p., https://doi.org/10.3133/ofr20201047.","productDescription":"iv, 41 p.","numberOfPages":"41","onlineOnly":"Y","ipdsId":"IP-111797","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":374605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1047/ofr20201047.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1047/coverthb.jpg"}],"country":"United States","state":"California","county":"Ventura County","otherGeospatial":"San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.59922790527342,\n 33.203647816301306\n ],\n [\n -119.42481994628906,\n 33.203647816301306\n ],\n [\n -119.42481994628906,\n 33.293229612321824\n ],\n [\n -119.59922790527342,\n 33.293229612321824\n ],\n [\n -119.59922790527342,\n 33.203647816301306\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Conservation and restoration of gar (Lepisosteidae) populations in North America are increasingly of interest to fisheries managers. Alligator Gar Atractosteus spatula are being stocked as age-0 fish in efforts to re-establish extirpated populations. However, gars are known to be highly cannibalistic in hatcheries, suggesting that age-0 Alligator Gar introduced into natural habitats may face predation pressures from other gar species, limiting the likelihood of released fish recruiting to the population. Furthermore, introduced age-0 gar may not have the proper prey resources for rapid growth that would facilitate recruitment to adulthood. Texoma Reservoir, located on the Oklahoma–Texas border, hosts four native gar species, including the Alligator Gar, whose population is supported by supplemental stocking of age-0 fingerlings. We investigated feeding by age-0 gar to obtain a baseline level of trophic ecology for this group of fishes, including the potential for poststocking cannibalism. Food webs were reconstructed via analysis of carbon (δ13C) and nitrogen (δ15N) isotopes using Bayesian mixing model approaches. Isotopic values for age-0 fish of all four gar species were similar. Invertebrates represented a large fraction of the diet for all gars, followed by Common Carp Cyprinus carpio and Grass Carp Ctenopharyngodon idella. Competitive interactions among Bluegill Lepomis macrochirus, White Bass Morone chrysops, White Crappie Pomoxis annularis, and young gar may occur for invertebrates, shads Dorosoma spp., and Western Mosquitofish Gambusia affinis but are likely short-lived due to the rapid growth of age-0 gar after their transition to piscivory. Trophic position of age-0 Alligator Gar, Longnose Gar Lepisosteus osseus, and Spotted Gar Lepisosteus oculatus was positively related to TL, but this relationship was not observed for Shortnose Gar Lepisosteus platostomus. In Texoma Reservoir, the four native gar species, including stocked Alligator Gar, appear to have adequate food resources for recruitment, with little indication of within-family or interspecific predation. This suggests that stocked Alligator Gar quickly acclimated to their new environment.
The U.S. Geological Survey, in partnership with The Nature Conservancy, analyzed existing water-quality and streamflow data from three locations in the Upper White River Basin, Indiana, to estimate annual mean concentrations and fluxes and to identify and quantify changes in water quality and streamflow over time. Water-quality data used in the analyses were collected between water years 1992 and 2017. Annual mean-daily concentrations and fluxes of total suspended solids, total phosphorus as phosphorus, nitrate plus nitrite as nitrogen, and total Kjeldahl nitrogen as nitrogen were estimated for U.S. Geological Survey streamgage locations in Indiana on the Upper White River at Muncie, near Nora, and near Centerton. In addition, flow-normalized annual mean-daily concentrations and fluxes of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen were estimated and used to assess changes in these constituents between water years 1997 and 2017. Flow normalization is a process that attempts to remove the effects of year-to-year variation in streamflow on concentrations and fluxes without removing the effects associated with seasonal and long-term (multiyear) trends in streamflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203030","collaboration":"Prepared in cooperation with The Nature Conservancy with generous support from the Nina Mason Pulliam Charitable Trust","usgsCitation":"Koltun, G.F., and Hauswald, C., 2020, Trends in streamflow, nutrients, and total suspended solids in the Upper White River Basin, Indiana: U.S. Geological Survey Fact Sheet 2020–3030, 6 p., https://doi.org/10.3133/fs20203030.","productDescription":"6 p.","onlineOnly":"Y","ipdsId":"IP-114324","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":374596,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3030/coverthb.jpg"},{"id":374597,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3030/fs20203030.pdf","text":"Report","size":"9.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3030"}],"country":"United States","state":"Indiana","otherGeospatial":"Upper White River Basin","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-86.6546,39.6001],[-86.6522,39.6087],[-86.6463,39.6128],[-86.6403,39.6201],[-86.6404,39.6305],[-86.6654,39.6305],[-86.6858,39.63],[-86.6853,39.6884],[-86.6849,39.7773],[-86.6845,39.8648],[-86.6929,39.8643],[-86.6937,39.9228],[-86.6938,39.9528],[-86.6946,40.0402],[-86.6961,40.1282],[-86.6962,40.1785],[-86.2424,40.1807],[-86.2435,40.2152],[-86.242,40.3013],[-86.2423,40.3734],[-86.2429,40.3884],[-86.2422,40.4029],[-85.8624,40.407],[-85.8621,40.3784],[-85.5784,40.3794],[-85.4451,40.3792],[-85.2205,40.379],[-85.2182,40.3073],[-85.1302,40.3082],[-85.0186,40.3092],[-84.901,40.3096],[-84.8064,40.3102],[-84.8079,40.1741],[-84.8106,40.1351],[-84.8112,40.1265],[-84.8131,40.006],[-84.8603,40.0066],[-84.8952,40.0061],[-85.2014,40.0042],[-85.2013,39.875],[-85.2133,39.8751],[-85.2205,39.8748],[-85.2214,39.7895],[-85.243,39.7902],[-85.3017,39.789],[-85.3519,39.7894],[-85.4651,39.7886],[-85.5765,39.7858],[-85.5968,39.786],[-85.6333,39.7862],[-85.6338,39.6987],[-85.6876,39.6987],[-85.7993,39.6993],[-85.913,39.6976],[-85.9518,39.6969],[-85.9523,39.638],[-85.9521,39.347],[-85.9812,39.3466],[-85.9902,39.3467],[-86.0247,39.3464],[-86.0854,39.3452],[-86.0919,39.3452],[-86.1008,39.3453],[-86.1377,39.3445],[-86.249,39.342],[-86.3566,39.3404],[-86.3816,39.3399],[-86.4631,39.3391],[-86.5732,39.3395],[-86.6309,39.3413],[-86.6309,39.3481],[-86.6323,39.4696],[-86.6859,39.47],[-86.686,39.5144],[-86.6861,39.5262],[-86.6706,39.5339],[-86.6533,39.5475],[-86.6491,39.5552],[-86.6528,39.5666],[-86.6546,39.5865],[-86.6552,39.5965],[-86.6546,39.6001]]]},\"properties\":{\"name\":\"Boone\",\"state\":\"IN\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd., Suite 100
Columbus, OH 43229
Indirect defence, the adaptive top‐down control of herbivores by plant traits that enhance predation, is a central component of plant–herbivore interactions. However, the scope of interactions that comprise indirect defence and associated ecological and evolutionary processes has not been clearly defined. We argue that the range of plant traits that mediate indirect defence is much greater than previously thought, and we further organise major concepts surrounding their ecological functioning. Despite the wide range of plant traits and interacting organisms involved, indirect defences show commonalities when grouped. These categories are based on whether indirect defences boost natural enemy abundance via food or shelter resources, or, alternatively, increase natural enemy foraging efficiency via information or alteration of habitat complexity. The benefits of indirect defences to natural enemies should be further explored to establish the conditions in which indirect defence generates a plant–natural enemy mutualism. By considering the broader scope of plant–herbivore–natural enemy interactions that comprise indirect defence, we can better understand plant‐based food webs, as well as the evolutionary processes that have shaped them.
","language":"English","publisher":"Wiley","doi":"10.1111/ele.13512","usgsCitation":"Pearse, I.S., LoPresti, E., Schaeffer, R.N., Wetzel, W.C., Mooney, K.A., Ali, J.G., Ode, P.J., Eubanks, M., Bronstein, J., and Weber, M.G., 2020, Generalizing indirect defense and resistance of plants: Ecology Letters, v. 23, no. 7, p. 1137-1152, https://doi.org/10.1111/ele.13512.","productDescription":"16 p.","startPage":"1137","endPage":"1152","ipdsId":"IP-109178","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":456805,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ele.13512","text":"Publisher Index Page"},{"id":376472,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-05-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":216680,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":793180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LoPresti, Eric","contributorId":208296,"corporation":false,"usgs":false,"family":"LoPresti","given":"Eric","email":"","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":793181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schaeffer, Robert N.","contributorId":229436,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Robert","email":"","middleInitial":"N.","affiliations":[{"id":41641,"text":"Utah State U.","active":true,"usgs":false}],"preferred":false,"id":793182,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wetzel, William C.","contributorId":229437,"corporation":false,"usgs":false,"family":"Wetzel","given":"William","email":"","middleInitial":"C.","affiliations":[{"id":41642,"text":"Michigan State U","active":true,"usgs":false}],"preferred":false,"id":793183,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mooney, Kailen A.","contributorId":229438,"corporation":false,"usgs":false,"family":"Mooney","given":"Kailen","email":"","middleInitial":"A.","affiliations":[{"id":34134,"text":"UC Irvine","active":true,"usgs":false}],"preferred":false,"id":793184,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ali, Jared G.","contributorId":229439,"corporation":false,"usgs":false,"family":"Ali","given":"Jared","email":"","middleInitial":"G.","affiliations":[{"id":41643,"text":"Penn State U.","active":true,"usgs":false}],"preferred":false,"id":793185,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ode, Paul J.","contributorId":197314,"corporation":false,"usgs":false,"family":"Ode","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":793186,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Eubanks, Micky D.","contributorId":229440,"corporation":false,"usgs":false,"family":"Eubanks","given":"Micky D.","affiliations":[{"id":36313,"text":"Texas A&M","active":true,"usgs":false}],"preferred":false,"id":793187,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bronstein, Judith L.","contributorId":229441,"corporation":false,"usgs":false,"family":"Bronstein","given":"Judith L.","affiliations":[{"id":35523,"text":"U Arizona","active":true,"usgs":false}],"preferred":false,"id":793188,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weber, Marjorie G.","contributorId":229442,"corporation":false,"usgs":false,"family":"Weber","given":"Marjorie","email":"","middleInitial":"G.","affiliations":[{"id":41642,"text":"Michigan State U","active":true,"usgs":false}],"preferred":false,"id":793189,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70209985,"text":"sir20205035 - 2020 - Ecological status of aquatic communities in selected streams in the Milwaukee Metropolitan Sewerage District planning area of Wisconsin, 2004–13","interactions":[],"lastModifiedDate":"2020-05-12T11:44:31.472549","indexId":"sir20205035","displayToPublicDate":"2020-05-11T11:54:36","publicationYear":"2020","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":"2020-5035","displayTitle":"Ecological Status of Aquatic Communities in Selected Streams in the Milwaukee Metropolitan Sewerage District Planning Area of Wisconsin, 2004–13","title":"Ecological status of aquatic communities in selected streams in the Milwaukee Metropolitan Sewerage District planning area of Wisconsin, 2004–13","docAbstract":"A total of 14 wadable streams in urban or urbanizing watersheds near Milwaukee, Wisconsin, were sampled in 2004, 2007, 2010, and 2013 to assess the ecological status of aquatic communities (biota), including benthic algae and invertebrates, and fish. To assess temporal variation, additional community sampling was also done at a subset of three sites in 2011 and 2012. Relative abundances of each type of organism were used to calculate biological metrics, such as richness and diversity, percentages of intolerant and tolerant organisms, and indexes of biotic integrity for invertebrates and fish. Selected environmental (physical and chemical) data in the streams were collected to evaluate potential relations to the biota and the ecological health of the stream. Physical and chemical data included land use/land cover, stream discharge from U.S. Geological Survey (USGS) streamgages (except at 2 creeks that were not gaged), stream habitat, microhabitat at invertebrate collection locations, water quality (except at 2 creeks that were not gaged), field measurements of several water-quality constituents, measures of benthic algal biomass, and toxicity and chemical tests on extracts from passive samplers deployed at a subset of 6 sites. Relative abundances of organisms and biological metrics were compared among sampling years and with environmental metrics to evaluate the ecological status of these streams and determine primary stressors on the aquatic communities, with the aim of helping resource managers understand and work toward improving the ecological health of these and other urban and urbanizing rivers in the study area.
Biological metrics for most sites indicated some level of diminished ecological status when compared across all sampled sites and when compared with rating scales for selected metrics. The least degraded sites among all those sampled—indicated by aggregate bioassessments for algae, invertebrates, and fish metrics and in order starting with the best overall condition—were the Milwaukee River near Cedarburg, Menomonee River at Menomonee Falls, Jewel Creek, and Milwaukee River at Milwaukee. The most degraded sites were Menomonee River at Wauwatosa, Root River at Greenfield, Lincoln Creek, and the Kinnickinnic River. Differences in aggregate bioassessments indicate that aquatic communities at the Menomonee River at Wauwatosa site and the Root River at Greenfield site were worse in 2013 than in 2004; however, Oak Creek and Honey Creek sites were better. In 2013, several sites had less than 30-percent pollution-sensitive diatoms indicating degraded algal assemblages. Invertebrate metrics for most of the 14 sites in 2013 were lower than in 2004 and indicate that invertebrate assemblages at most sampled sites were more degraded in 2013. Tolerant fish taxa made up more than 40 percent of assemblages at most sites and nearly 100 percent of assemblages at four sites. At times, in some smaller streams, too few fish were captured to compute an Index of Biotic Integrity with confidence, and invertebrates provided a better means for assessing the ecological status and water quality. With these few exceptions, the use of all three groups of biota provided the most robust assessments at the 14 sites in 2004–13.
Physical and chemical stressors were correlated to adverse effects on aquatic biota at the sampled streams. Passive samplers were deployed at a subset of six sites in 2013. Microtox results indicated there was little or no toxicity at the Milwaukee River near Cedarburg site and at the Oak Creek site, slight toxicity at the Lincoln Creek and Honey Creek sites, and moderate toxicity at the Milwaukee River at Milwaukee site and the Little Menomonee River site; however, based on cytochrome-P450 reporter gene system toxicity tests, potential toxicity from hydrophobic organic contaminants was measured at all six sites. For all 14 sites, physical and chemical stressors related to urbanization correlated with biological metrics for algae, invertebrates, and fish. Most stressors for aquatic biota reflected an urban signature. Stressors related to ecological condition in our study were chemical and physical, such as developed land, impervious surface in the watershed, urban land in a buffer area around the stream (a 100-foot [30-meter]-wide area on each side of the stream, and maximum instantaneous discharge normalized by drainage area (a measure of flood and scour effects). Chemical stressors included low waterborne concentrations of dissolved oxygen and high concentrations of chloride, zinc and other metals, nutrients (nitrite and phosphorus), and fecal coliform bacteria.
Although algae, invertebrates, and fish did not always demonstrate a significant response to the same stressors, higher abundances of high total phosphorus-indicator diatoms, lower ratings for invertebrate biotic integrity indexes and percentages of mayflies-stoneflies-caddisflies, and lower values for fish biotic integrity indexes underscored possible adverse effects of even low levels of developed land. Developed land is typically associated with more rapid runoff, which washes chemicals from impervious surfaces into area waterways and degrades stream habitat for aquatic communities. However, with respect to at least chloride from road salt, diatoms tolerant to dissolved salts were significantly lower with as little as 1-percent mixed forest in the watershed. Lower percentages of urban land in the stream buffer correlated with healthier aquatic assemblages of algae, invertebrates, and fish. The assessment of algal, invertebrate, and fish assemblages coupled with physical and chemical data were highly useful in evaluating the ecological status of aquatic communities at the 14 sites and for determining environmental stressors that may be contributing to reduced stream condition. Some of the stressors could potentially be removed or lessened with stream rehabilitation or changes in watershed management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205035","collaboration":"Prepared in cooperation with the Milwaukee Metropolitan Sewerage District","usgsCitation":"Scudder Eikenberry, B.C., Nott, M.A., Stewart, J.S., Sullivan, D.J., Alvarez, D.A., Bell, A.H., and Fitzpatrick, F.A., 2020, Ecological status of aquatic communities in selected streams in the Milwaukee Metropolitan Sewerage District planning area of Wisconsin, 2004–13: U.S. Geological Survey Scientific Investigations Report 2020–5035, 84 p., https://doi.org/10.3133/sir20205035.","productDescription":"Report: viii, 84 p.; Data Release; Dataset","numberOfPages":"96","onlineOnly":"Y","ipdsId":"IP-106552","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":374557,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5035/coverthb.jpg"},{"id":374558,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5035/sir20205035.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5035"},{"id":374559,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FWMODL","text":"USGS data release","linkHelpText":"Aquatic community and environmental data for 14 rivers and streams in the Milwaukee Metropolitan Sewerage District Planning Area, 2004-13"},{"id":374560,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System—","linkHelpText":"USGS water data for the Nation"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Milwaukee Metropolitan Sewerage District Planning Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.187255859375,\n 42.512601715736665\n ],\n [\n -87.81372070312499,\n 42.512601715736665\n ],\n [\n -87.81372070312499,\n 43.15710884095329\n ],\n [\n -88.187255859375,\n 43.15710884095329\n ],\n [\n -88.187255859375,\n 42.512601715736665\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Environmental groundwater-quality data were collected from 648 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program and are included in this report. Most of the wells (514) were sampled from January through December 2016, and 60 of them were sampled in 2013 and 74 in 2014. The data were collected from seven types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public-water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply; enhanced trends networks, which are used to evaluate the time scales during which groundwater quality changes; vertical flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths; flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths over a horizontal distance; and modeling support studies, which are used to provide data to support groundwater modeling. Groundwater samples were analyzed for many water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, and some constituents of special interest (arsenic speciation, chromium [VI], and perchlorate). These groundwater-quality data, along with data from quality-control samples, are tabulated in this report and in an associated data release. Some data from environmental samples collected in 2013–14 and quality-control samples collected in 2012–15 also are included in the associated data release. Data from samples collected in 2016 are associated with networks described in this report and have not been published previously; data from samples collected between 2012 and 2015 are associated with networks described in previous reports in this data series.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1124","collaboration":"National Water-Quality Assessment Project","usgsCitation":"Arnold, T.L., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, J.T., and Belitz, K., 2020, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. Geological Survey Data Series 1124, 135 p., https://doi.org/10.3133/ds1124. ","productDescription":"Report: ix, 135 p.; Data Release; Dataset","numberOfPages":"150","onlineOnly":"Y","ipdsId":"IP-111772","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":374561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1124/coverthb.jpg"},{"id":374562,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1124/ds1124.pdf","text":"Report","size":"20.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1124"},{"id":374563,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W4RR74","text":"USGS data release","linkHelpText":"Datasets from groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015"},{"id":374564,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System—","linkHelpText":"USGS water data for the Nation"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Observation, theory, and intuition all suggest that larger earthquakes should trigger larger landslides. Many factors could contribute to this, including depth-dependent shear strength or non-linearity of ground motion in soils and rock, but we hypothesize that the key characteristics of large earthquakes causing this phenomenon are (in addition to magnitude) the frequency and duration of the strong ground motion. Because of the paucity of site-specific data for detailed analysis, we take a regional approach to this question by analyzing strong-motion records and earthquake-induced landslide (EQIL) inventories from six well-documented earthquakes. Ground motion is characterized using earthquake magnitude and the median durations and frequencies (mean periods) of subsets of strong-motion records relevant to landslide triggering. EQIL inventories are characterized using the median landslide area of the entire inventory as well as the median areas of the largest 1% of the landslides and the largest 10 landslides. We then compare ground-motion characteristics with landslide size statistics to determine possible correlations. Comparisons of all earthquake- and landslide-size statistics show strong positive correlations between landslide size and (1) magnitude, (2) ground-motion duration, and (3) mean period. Although all the ground-motion measures yield highly correlated regressions, mean period appears to be the best overall predictor of landslide size. Landslide modeling using Newmark's sliding-block method also shows that longer mean periods and durations and larger magnitudes correlate strongly with increases in modeled displacements. These results support our hypothesis that increasing period and duration of seismic ground motion are the physical factors driving increased landslide sizes for larger earthquakes. Additional studies including data from a much larger set of earthquakes is needed to confirm the results of this initial study.
We acquired multiple types of seismic data across the Hollywood Fault in Hollywood, Calif., and the Santa Monica Fault in Beverly Hills, Calif., in May and June 2018. On the basis of our data, we infer near-surface locations of various traces of these faults.
From two separate profiles across the Hollywood Fault, we evaluated multiple seismic datasets and models, including guided-wave data, tomographic VP data, tomographic VS data, VP/VS and Poisson’s ratio models derived from tomographic VP and VS data, Rayleigh-wave–based VS models, Love-wave–based VS models, VP/VS and Poisson’s ratio models (derived from combinations of tomographic-based VP and surface-wave–based VS models), P-wave reflection images, and S-wave reflection images. All of these data and models can be used to delineate near-surface faulting, and the data consistently infer near-surface fault traces of the Hollywood Fault in the same locations. Importantly, the combined data indicate more than one near-surface fault trace of the Hollywood Fault. Between North Bronson and North Gower Avenues, evidence exists for a near-surface trace of the Hollywood Fault slightly south of Carlos Avenue. Farther west, along Argyle Avenue, our data contain high levels of cultural noise, but we interpret near-surface faulting slightly south of the intersection of Carlos and Argyle Avenues and between Carlos Avenue and Yucca Street.
For the Santa Monica Fault in Beverly Hills, we acquired guided-wave data only along Lasky Drive between Moreno Drive and South Santa Monica Boulevard, owing to limited access permissions. However, we used two separate source locations to generate the guided-wave data (SP1 and SP2). The data from more distant source location (relative to the recording array, SP1) were noisy, but on the basis of those data, we infer near-surface faulting at several locations along Lasky Drive, with concentrated near-surface faulting slightly south of the intersection of Lasky Drive and Charleville Boulevard. Guided-wave data generated at the closer source location (relative to recording array, SP2) more clearly show evidence for distributed near-surface faulting at several locations along Lasky Drive, with concentrated faulting near the intersection of Lasky Drive and Charleville Boulevard.
Although the seismic surveys across both faults provide strong evidence for the locations of near-surface fault traces, the seismic data provide little or no information about the rupture history of the fault traces.
Contact Information, Menlo Park, Calif.
Office—Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
Studies of naturally occurring subsurface carbon dioxide (CO2) accumulations can provide useful information for potential CO2 injection projects; however, the microbial communities and formation water geochemistry of most reservoirs are understudied. Formation water and microbial biomass were sampled at four CO2-rich reservoir sites: two within Bravo Dome, a commercial CO2 field in New Mexico; one northwest of Bravo Dome in Colorado (Oakdale Field); and one southwest of Bravo Dome in New Mexico (Rafter “K” Ranch). Aside from the Rafter “K” Ranch site, minor differences were observed in the geochemistry of formation water collected from these sites compared to historical data. No organisms were significantly associated with Oakdale Field compared to the other three sites, nor were any hydrogeochemical or gas geochemical parameters (for example, CO2 concentration) found to have significant associations with the microbial ecology of these four sites. Microorganisms from these sites were metabolically diverse and had the potential to (1) generate methane, (2) produce corrosive hydrogen sulfide (H2S), and (3) rapidly biofoul and (or) clog pore spaces by shifting microbial communities with changes in salinity or nutrient supply. This study demonstrates that high concentrations of CO2 in subsurface reservoirs apparently have not imparted a distinct geochemical or microbiological signature on the associated formation waters and that the microorganisms in these reservoirs are metabolically diverse and could adapt to geochemical changes in the subsurface.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205037","usgsCitation":"Shelton, J.L., Andrews, R.S., Akob, D.M., DeVera, C.A., Mumford, A.C., Engle, M., Plampin, M.R., and Brennan, S.T., 2020, Compositional analysis of formation water geochemistry and microbiology of commercial and carbon dioxide-rich wells in the southwestern United States: U.S. Geological Survey Scientific Investigations Report 2020–5037, 26 p., https://doi.org/10.3133/sir20205037.","productDescription":"viii, 26 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-098514","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":374365,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5037/sir20205037.pdf","text":"Report","size":"1.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5037"},{"id":374364,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5037/coverthb.jpg"}],"country":"United States","state":"Colorado, New Mexico, Texas, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.16064453125,\n 34.07086232376631\n ],\n [\n -102.919921875,\n 34.07086232376631\n ],\n [\n -102.919921875,\n 37.43997405227057\n ],\n [\n -107.16064453125,\n 37.43997405227057\n ],\n [\n -107.16064453125,\n 34.07086232376631\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Energy Resources Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
956 National Center
Reston, VA 20192
This study used acoustic telemetry and a multistate Cormack–Jolly–Seber model to determine the seasonal movement patterns of blue sucker Cycleptus elongatus from 2015 to 2017. Several hypotheses were ranked using AICc, and it was determined that the movement patterns of blue suckers in a mainstem reach below a hydropower dam (i.e., tailwater) differed from those of blue suckers tagged in the major tributaries (perennial with stream order >3). This study estimated a low probability (≤0.13) blue suckers would leave the tailwater reach at any time during the study. Conversely, blue suckers tagged in the major tributaries had a high probability (≥0.88) of leaving after the spawning season (February–May). Blue suckers tagged in the major tributaries displayed a high probability (0.83) of returning to the tributaries in the spawning season of 2016 when discharges were high. Blue suckers also had a higher probability of fidelity to the tributary where they were tagged (0.65) rather than straying to different tributaries (0.18). The majority of tagged blue suckers that strayed selected the only undammed tributary in the study area. In 2017, spring discharges were low, and the probability of blue suckers returning to any major tributary was low (0.19), with little difference in the probability of displaying site fidelity (0.10) vs. straying (0.09).
","language":"English","publisher":"Wiley","doi":"10.1111/jfb.14374","usgsCitation":"Dyer, J.J., and Brewer, S.K., 2020, Seasonal movements and tributary-specific fidelity of blue sucker Cycleptus elongatus in a Southern Plains riverscape: Journal of Fish Biology, v. 97, no. 1, p. 279-292, https://doi.org/10.1111/jfb.14374.","productDescription":"14 p.","startPage":"279","endPage":"292","ipdsId":"IP-102994","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":388551,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"lower Red River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -456.6522216796875,\n 33.687781758439364\n ],\n [\n -455.1470947265625,\n 33.687781758439364\n ],\n [\n -455.1470947265625,\n 34.334364487026306\n ],\n [\n -456.6522216796875,\n 34.334364487026306\n ],\n [\n -456.6522216796875,\n 33.687781758439364\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"97","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-06-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Dyer, J. J.","contributorId":264808,"corporation":false,"usgs":false,"family":"Dyer","given":"J.","email":"","middleInitial":"J.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":822007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":822008,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70214538,"text":"70214538 - 2020 - Applications and utility of the surface elevation table–marker horizon method for measuring wetland elevation and shallow soil subsidence-expansion: Discussion/reply to: Byrnes M., Britsch L., Berlinghoff J., Johnson R., and Khalil S. 2019. Recent subsidence rates for Barataria Basin, Louisiana. Geo-Marine Letters 39:265–278","interactions":[],"lastModifiedDate":"2020-09-30T14:49:02.792091","indexId":"70214538","displayToPublicDate":"2020-05-08T09:47:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1742,"text":"Geo-Marine Letters","active":true,"publicationSubtype":{"id":10}},"title":"Applications and utility of the surface elevation table–marker horizon method for measuring wetland elevation and shallow soil subsidence-expansion: Discussion/reply to: Byrnes M., Britsch L., Berlinghoff J., Johnson R., and Khalil S. 2019. Recent subsidence rates for Barataria Basin, Louisiana. Geo-Marine Letters 39:265–278","docAbstract":"Byrnes et al. (Geo-Marine Letters 39:265–278, Byrnes et al. 2019) present subsidence data for Barataria Basin located south and west of New Orleans in coastal Louisiana to better inform wetland protection and restoration planning by the Louisiana Coastal Protection and Restoration Authority. They measured subsidence using geodetic GPS elevation surveys of rod benchmarks, similar to the rod benchmarks of the surface elevation table–marker horizon (SET-MH) method used to measure surface biophysical processes influencing elevation dynamics and shallow subsidence (i.e., subsidence occurring above the base of the rod) in coastal wetlands. Byrnes et al. (Geo-Marine Letters 39:265–278, Byrnes et al. 2019) argue that (1) SET-MH measures should not be included in subsidence measures because subsidence is a purely geologic process, separate from biophysical processes occurring in the active marsh zone, (2) shallow subsidence measured by the SET-MH method in deep Holocene sediments are not valid because of downdrag on the rod, and (3) high spatial variability of wetland surface processes precludes the ability to make meaningful estimates of subsidence using the SET-MH method. This reply paper presents an extensive summary of the peer-reviewed literature that refutes all three of these claims and demonstrates that it is not only reasonable but also essential to apply the SET-MH method to obtain a complete as possible assessment of surface elevation dynamics to inform coastal wetland restoration and management planning in Barataria Basin and other coastal wetlands worldwide.