The North American (NOAM) plate converges with the Caribbean (CARIB) plate at a rate of 20.0 ± 0.4 mm/yr. towards 254 ± 1°. Plate convergence is highly oblique (20–10°), resulting in a complex crustal boundary with along-strike segmentation, strain partitioning and microplate tectonics. We study the oblique convergence of the NOAM and CARIB plates between southeastern Cuba to northern Puerto Rico using new swath multibeam bathymetry data and 2D multi-channel seismic profiles. The combined interpretation of marine geophysical data with the seismicity and geodetic data from public databases allow us to perform a regional scale analysis of the shallower structure, the seismotectonics and the slab geometry along the plate boundary. Due to differential rollback between the NOAM oceanic crust north of Puerto Rico and the relative thicker Bahamas Carbonate Province crust north of Hispaniola a slab tear is created at 68.5°W. The northern margin of Puerto Rico records the oblique high-dip subduction and rollback of the NOAM plate below the island arc. Those processes have resulted in a forearc transpressive tectonics (without strain partitioning), controlled by the Septentrional-Oriente Fault Zone (SOFZ) and the Bunce Fault Zone (BFZ). Meanwhile, in the northern margin of Hispaniola, the collision of the Bahamas Carbonate Province results in high plate coupling with strain partitioning: SOFZ and Northern Hispaniola Deformed Belt (NHDB). In the northern Haitian margin, compression is still relevant since seismicity is mostly associated with the deformation front, whereas strike slip earthquakes are hardly anecdotal. Although in Hispaniola intermediate-depth seismicity should disappear, diffuse intermediate-depth hypocenter remains evidencing the presence of remnant NOAM subducted slab below central and western Hispaniola. Results of this study improve our understanding of the active tectonics in the NE Caribbean that it is the base for future assessment studies on seismic and tsunamigenic hazard.
Ecosystem-scale examination of fish communities typically involves creating spatio-temporally explicit relative abundance distribution maps using data from multiple fishery-independent surveys. However, sampling performance varies by vessel and sampling gear, which may influence estimated species distribution patterns. Using GAMMs, the effect of different gear–vessel combinations on relative abundance estimates at length was investigated using European fisheries-independent groundfish survey data. We constructed a modelling framework for evaluating relative efficiency of multiple gear–vessel combinations. 19 northeast Atlantic surveys for 254 species-length combinations were examined. Space-time variables explained most of the variation in catches for 181/254 species-length cases, indicating that for many species, models successfully characterized distribution patterns when combining data from disparate surveys. Variables controlling for gear efficiency explained substantial variation in catches for 127/254 species-length data sets. Models that fail to control for gear efficiencies across surveys can mask changes in the spatial distribution of species. Estimated relative differences in catch efficiencies grouped strongly by gear type, but did not exhibit a clear pattern across species’ functional forms, suggesting difficulty in predicting the potential impact of gear efficiency differences when combining survey data to assess species’ distributions and highlighting the importance of modelling approaches that can control for gear differences.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/icesjms/fsz254","usgsCitation":"Moriarty, M., Pedreschi, D., Smeltz, T., Sethi, S., Harris, B., McGonigle, C., Wolf, N., and Greenstreet, S.P., 2020, Combining fisheries surveys to inform marine species distribution modelling: ICES Journal of Marine Science, v. 77, no. 2, p. 539-552, https://doi.org/10.1093/icesjms/fsz254.","productDescription":"14 p.","startPage":"539","endPage":"552","ipdsId":"IP-105454","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":458088,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icesjms/fsz254","text":"Publisher Index Page"},{"id":388943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ireland, United Kingdom","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -11.601562500000007,\n 48.66194284607008\n ],\n [\n 9.140624999999993,\n 48.66194284607008\n ],\n [\n 9.140624999999993,\n 60.21799073323445\n ],\n [\n -11.601562500000007,\n 60.21799073323445\n ],\n [\n -11.601562500000007,\n 48.66194284607008\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"77","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-01-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Moriarty, Meadhbh","contributorId":265399,"corporation":false,"usgs":false,"family":"Moriarty","given":"Meadhbh","email":"","affiliations":[{"id":54679,"text":"Ulster University","active":true,"usgs":false}],"preferred":false,"id":822702,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pedreschi, Debbi","contributorId":265400,"corporation":false,"usgs":false,"family":"Pedreschi","given":"Debbi","email":"","affiliations":[{"id":54680,"text":"Marine Institute, Galway, Ireland","active":true,"usgs":false}],"preferred":false,"id":822703,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smeltz, T. Scott","contributorId":265401,"corporation":false,"usgs":false,"family":"Smeltz","given":"T. Scott","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":822704,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":822705,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harris, Bradley P.","contributorId":265402,"corporation":false,"usgs":false,"family":"Harris","given":"Bradley P.","affiliations":[{"id":12915,"text":"Alaska Pacific University","active":true,"usgs":false}],"preferred":false,"id":822706,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McGonigle, Chris","contributorId":265403,"corporation":false,"usgs":false,"family":"McGonigle","given":"Chris","affiliations":[{"id":54679,"text":"Ulster University","active":true,"usgs":false}],"preferred":false,"id":822707,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wolf, Nathan","contributorId":265404,"corporation":false,"usgs":false,"family":"Wolf","given":"Nathan","affiliations":[{"id":54682,"text":"Alaska Pacific Unversity","active":true,"usgs":false}],"preferred":false,"id":822708,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Greenstreet, Simon P.R.","contributorId":265405,"corporation":false,"usgs":false,"family":"Greenstreet","given":"Simon","email":"","middleInitial":"P.R.","affiliations":[{"id":54683,"text":"Marine Scotland Science","active":true,"usgs":false}],"preferred":false,"id":822709,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70220395,"text":"70220395 - 2020 - Marine latitudinal diversity gradients, niche conservatism and out of the tropics and Arctic: Climatic sensitivity of small organisms","interactions":[],"lastModifiedDate":"2021-05-11T12:12:03.04564","indexId":"70220395","displayToPublicDate":"2020-01-20T07:03:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2193,"text":"Journal of Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Marine latitudinal diversity gradients, niche conservatism and out of the tropics and Arctic: Climatic sensitivity of small organisms","docAbstract":"The latitudinal diversity gradient (LDG) is a consequence of evolutionary and ecological mechanisms acting over long history, and thus is best investigated with organisms that have rich fossil records. However, combined neontological‐palaeontological investigations are mostly limited to large, shelled invertebrates, which keeps our mechanistic understanding of LDGs in its infancy. This paper aims to describe the modern meiobenthic ostracod LDG and to explore the possible controlling factors and the evolutionary mechanisms of this large‐scale biodiversity pattern.
Present‐day Western North Atlantic.
Ostracoda.
We compiled ostracod census data from shallow‐marine environments of the western North Atlantic Ocean. Using these data, we documented the marine LDG with multiple metrics of alpha, beta (nestedness and turnover) and gamma diversity, and we tested whether macroecological patterns could be governed by different environmental factors, including temperature, salinity, dissolved oxygen, pH and primary productivity. We also explored the geologic age distribution of ostracod genera to investigate the evolutionary mechanisms underpinning the LDG.
Our results show that temperature and climatic niche conservatism are important in setting LDGs of these small, poorly dispersing organisms. We also found evidence for some dispersal‐driven spatial dynamics in the ostracod LDG. Compared to patterns observed in marine bivalves, however, dispersal dynamics were weaker and they were bi‐directional, rather than following the ‘out‐of‐the‐tropics’ model.
Our detailed analyses revealed that meiobenthic organisms, which comprise two‐thirds of marine diversity, do not always follow the same rules as larger, better‐studied organisms. Our findings suggest that the understudied majority of biodiversity may be more sensitive to climate than well‐studied, large organisms. This implies that the impacts of ongoing Anthropocene climatic change on marine ecosystems may be much more serious than presently thought.
Effective resource management depends on our ability to partition diversity into biologically meaningful units. Recent evolutionary divergence, however, can often lead to ambiguity in morphological and genetic differentiation, complicating the delineation of valid conservation units. Such is the case with the \"coregonine problem,\" where recent postglacial radiations of coregonines into lacustrine habitats resulted in the evolution of numerous species flocks, often with ambiguous taxonomy. The application of genomics methods is beginning to shed light on this problem and the evolutionary mechanisms underlying divergence in these ecologically and economically important fishes. Here, we used restriction site-associated DNA (RAD) sequencing to examine genetic diversity and differentiation among sympatric forms in the Coregonus artedi complex in the Apostle Islands of Lake Superior, the largest lake in the Laurentian Great Lakes. Using 29,068 SNPs, we were able to clearly distinguish among the three most common forms for the first time, as well as identify putative hybrids and potentially misidentified specimens. Population assignment rates for these forms using our RAD data were 93%-100% with the only mis-assignments arising from putative hybrids, an improvement from 62% to 77% using microsatellites. Estimates of pairwise differentiation (F ST: 0.045-0.056) were large given the detection of hybrids, suggesting that reduced fitness of hybrid individuals may be a potential mechanism for the maintenance of differentiation. We also used a newly built C. artedi linkage map to look for islands of genetic divergence among forms and found widespread differentiation across the genome, a pattern indicative of long-term drift, suggesting that these forms have been reproductively isolated for a substantial amount of time. The results of this study provide valuable information that can be applied to develop well-informed management strategies and stress the importance of re-evaluating conservation units with genomic tools to ensure they accurately reflect species diversity.
","language":"English","publisher":"National Library of Medicine","doi":"10.1111/eva.12919","usgsCitation":"Ackiss, A., Larson, W., and Stott, W., 2020, Genotyping-by-sequencing illuminates high levels of divergence among sympatric forms of coregonines in the Laurentian Great Lakes: Evolutionary Applications, v. 13, no. 5, p. 1037-1054, https://doi.org/10.1111/eva.12919.","productDescription":"18 p.","startPage":"1037","endPage":"1054","ipdsId":"IP-111482","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":487600,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eva.12919","text":"Publisher Index Page"},{"id":481457,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Laurentian Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.95944256375668,\n 47.144330692090534\n ],\n [\n -90.95944256375668,\n 46.58094569283301\n ],\n [\n -90.38815350125653,\n 46.58094569283301\n ],\n [\n -90.38815350125653,\n 47.144330692090534\n ],\n [\n -90.95944256375668,\n 47.144330692090534\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Ackiss, Amanda S.","contributorId":350148,"corporation":false,"usgs":false,"family":"Ackiss","given":"Amanda S.","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":925444,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larson, Wesley 0000-0003-4473-3401 wlarson@usgs.gov","orcid":"https://orcid.org/0000-0003-4473-3401","contributorId":199509,"corporation":false,"usgs":true,"family":"Larson","given":"Wesley","email":"wlarson@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":925443,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stott, Wendylee wstott@usgs.gov","contributorId":3763,"corporation":false,"usgs":true,"family":"Stott","given":"Wendylee","email":"wstott@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":925445,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217377,"text":"70217377 - 2020 - The transformative impact of genomics on sage-grouse conservation and management","interactions":[],"lastModifiedDate":"2021-01-20T16:21:34.219524","indexId":"70217377","displayToPublicDate":"2020-01-18T10:16:17","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"The transformative impact of genomics on sage-grouse conservation and management","docAbstract":"For over two decades, genetic studies have been used to assist in the conservation and management of both Greater Sage-grouse (Centrocercus urophasianus) and Gunnison Sage-grouse (C. minimus), addressing a wide variety of topics including taxonomy, parentage, population connectivity, and demography. The field of conservation genetics has been transformed by dramatic improvements in sequencing technology, facilitating genomic studies in many wildlife species. The quality and amount of data generated by genomic methods vastly exceed that of traditional genetic studies, allowing for increased precision in estimating genetic parameters of interest. Perhaps more importantly, genomic methods can provide insight into non-neutral evolution such as adaptive divergence. Here we recount the shift from genetic to genomic methods using two wildlife species of substantial conservation interest, focusing on the improved capabilities and advantages of genomic methods. For instance, reassessment of divergence in sage-grouse using genomic methods confirmed strong differentiation between the two species and revealed that a small population in the state of Washington was more genetically distinct than previously recognized. Further, new genomic resources and approaches have been used to identify a family of genes linked to local dietary adaptation suggesting that sage-grouse may possess digestive and metabolic adaptations that mitigate the effects of consuming plant secondary metabolites like those found in sagebrush. Genetic variation among populations in these gene regions is thought to be involved with local dietary adaptations, and therefore maintaining the tie between sage-grouse and the chemistry of local sagebrush may be an important management consideration. We posit that the integration of newly developed genomic resources combined with the vast wealth of ecological and behavioral data for sage-grouse has the potential to shed light on mechanistic relationships that ultimately are vital to the conservation and management of these species.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Population genomics: Wildlife","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/13836_2019_65","usgsCitation":"Oyler-McCance, S.J., Oh, K., Zimmerman, S., and Aldridge, C., 2020, The transformative impact of genomics on sage-grouse conservation and management, chap. of Population genomics: Wildlife, p. 523-546, https://doi.org/10.1007/13836_2019_65.","productDescription":"24 p.","startPage":"523","endPage":"546","ipdsId":"IP-094749","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":382323,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-01-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":808550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oh, Kevin P","contributorId":223092,"corporation":false,"usgs":false,"family":"Oh","given":"Kevin P","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":808551,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zimmerman, Shawna J","contributorId":139402,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Shawna J","affiliations":[{"id":6737,"text":"Colorado State University, Department of Ecosystem Science and Sustainability, and Natural Resource Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":808552,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":213471,"corporation":false,"usgs":false,"family":"Aldridge","given":"Cameron L.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":808553,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207892,"text":"sim3449 - 2020 - High-resolution airborne geophysical survey of the Shellmound, Mississippi area","interactions":[],"lastModifiedDate":"2022-04-22T20:07:01.788312","indexId":"sim3449","displayToPublicDate":"2020-01-17T16:20:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3449","displayTitle":"High-Resolution Airborne Geophysical Survey of the Shellmound, Mississippi Area","title":"High-resolution airborne geophysical survey of the Shellmound, Mississippi area","docAbstract":"In late February to early March 2018, the U.S. Geological Survey acquired 2,364 line-kilometers (km) of airborne electromagnetic, magnetic, and radiometric data in the Shellmound, Mississippi study area. The purpose of this survey is to contribute high-resolution information about subsurface geologic structure to inform groundwater models, water resource infrastructure studies, and local decision making. The Shellmound region hosts a managed aquifer recharge (MAR) pilot project, developed by the Agricultural Research Service of the U.S. Department of Agriculture. The MAR pilot project is investigating the use of bank filtration along the Tallahatchie River as a source for recharge in areas of significant groundwater decline. Direct injection into the Mississippi River Valley Alluvial aquifer (MRVA) occurs about 3 km from the extraction gallery. Understanding the structure of the aquifer, including both shallow and deep confining units, is important for the success of this pilot MAR study and may be even more important for potential future large-scale MAR projects and groundwater model development efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3449","usgsCitation":"Burton, B.L., Minsley, B.J., Bloss, B.R., Kress, W.H., Rigby, J.R., and Smith, B.D., 2020, High-resolution airborne geophysical survey of the Shellmound, Mississippi area: U.S. Geological Survey Scientific Investigations Map 3449, 2 sheets, https://doi.org/10.3133/sim3449.","productDescription":"2 Sheets: 28.09 x 21.01 inches and 29.96 x 24.19 inches; Data Release; ReadMe","onlineOnly":"Y","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":399521,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109607.htm"},{"id":371340,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D4EA9W","text":"USGS data release","linkHelpText":"Airborne electromagnetic, magnetic, and radiometric survey, Shellmound, Mississippi, March 2018"},{"id":371339,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3449/sim3449_ReadMe.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3449 Read Me"},{"id":371338,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3449/sim3449_sheet2.pdf","text":"Sheet 2—","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3449 Sheet 2","linkHelpText":"High-Resolution Airborne Geophysical Survey of the Shellmound, Mississippi Area"},{"id":371337,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3449/sim3449_sheet1.pdf","text":"Sheet 1—","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3449 Sheet 1","linkHelpText":"High-Resolution Airborne Geophysical Survey of the Shellmound, Mississippi Area"},{"id":371336,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3449/coverthb.jpg"}],"country":"United States","state":"Mississippi","county":"Leflore County","city":"Shellmound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.5333,\n 33.5242\n ],\n [\n -90.1628,\n 33.5242\n ],\n [\n -90.1628,\n 33.8\n ],\n [\n -90.5333,\n 33.8\n ],\n [\n -90.5333,\n 33.5242\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046
The U.S. Geological Survey (USGS), in cooperation with the U.S. Army Corps of Engineers (USACE), analyzed streamflow trends and streamflow-related variables through 2017 in seven important water-supply basins to provide information that can help water managers with the USACE and river authorities make future water management decisions. The primary purpose of this report is to document trends in long-term streamflow data at 114 selected USGS streamflow-gaging stations and 36 simulated reservoir-inflow stations in 7 river basins primarily in Texas: Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity. In this report, trends were considered statistically significant if their p-values were less than or equal to 0.05 (p-value ≤0.05). Streamflow data selected for temporal trend analyses included annual minimum streamflow, annual peak streamflow, and streamflow volume. Precipitation, air temperature, and groundwater-level-elevation data were analyzed for trends that may help to explain changes observed in the streamflow statistics. Basins were divided into sections along county lines for precipitation analyses. Streamflow volumes were analyzed for associations with potential flood storage. The potential flood storage, defined as the difference between maximum storage and normal storage, was computed for each dam from the National Inventory of Dams database and accumulated over time based on the completion date of the dam.
Precipitation and air temperature trends were analyzed for each of the eight climate divisions (High Plains, Trans-Pecos, Low Rolling Hills, Edwards Plateau, North Central Texas, South Central Texas, East Texas, and Upper Coast). Results of precipitation trend analyses indicated moderate upward trends in the Upper Coast and East Texas Climate Divisions analyzed on an annual time step from 1900 through 2017. These two climate divisions are in the eastern and southeastern parts of the State, and they receive more mean annual precipitation (45.88 and 46.09 inches, respectively) than the other climate divisions. The results of air temperature analyses indicated upward trends in annual mean air temperature within all climate divisions, with a mean slope of 0.02 degree Fahrenheit per year, or 1 degree every 50 years.
Within the Brazos River Basin, results of precipitation trend analyses on an annual time step indicated that precipitation amounts are most likely increasing in the lower and middle sections of the basin. Downward trends in annual streamflow and in the ratio of streamflow volume to precipitation volume were indicated at 7 of the 15 stations in the upper sections of the basin. The lower sections of the basin had mostly downward trends in annual minimum streamflow, whereas upward trends in annual minimum streamflow were indicated in the upper sections of the basin. Downward trends in annual peak streamflow were indicated at many of the stations in the upper sections of the basin. At the same seven stations in the upper sections of the basin where there were downward trends in annual streamflow, there were also downward trends in the ratio of streamflow volume to precipitation volume. The data from the same seven stations indicated negative associations between potential flood storage volume and annual streamflow volume and downward trends in the ratio of annual streamflow volume to potential flood storage volume. With the known addition of 13,006,394 acre-feet of potential flood storage between 1900 and 2010 in the subbasins analyzed, streamflow volumes have decreased in the upper sections of the Brazos River Basin.
Within the Colorado River Basin, results of precipitation trend analyses on an annual time step indicated no trends in the basin. Downward trends in annual streamflow were indicated at 16 stations in the upper sections of the basin, whereas no trends in annual streamflow were indicated in the lower section of the basin. In the lower section of the basin, one station that was operated as a continuous streamflow-gaging station through 2017 had a downward trend in annual minimum streamflow, and another station (operated through 2007) had an upward trend in annual minimum streamflow. In the upper sections of the basin, data from seven stations indicated upward trends in annual minimum streamflow, and data from six stations indicated downward trends. Data from 18 stations in the upper sections of the basin indicated downward trends in annual peak streamflow. Thirteen of the 16 stations in the upper sections of the basin with data that indicated downward trends in annual streamflow also have data that indicated downward trends in the ratio of streamflow volume to precipitation volume. Data from the same 13 stations indicated negative associations between potential flood storage volume and annual streamflow volume and downward trends in the ratio of annual streamflow volume to potential flood storage volume. With the known addition of 7,193,147 acre-feet of potential flood storage between 1891 and 2014 in the subbasins analyzed, streamflow volumes have decreased in the upper sections of the Colorado River Basin.
Within the Big Cypress Basin, results of precipitation trend analyses on annual, seasonal, and monthly time steps indicated almost no trends in the basin as defined in this report. However, the annual precipitation p-value only slightly exceeded the p-value threshold for a statistically significant trend. Given the upward trend in precipitation in the East Texas Climate Division, which includes the Big Cypress Basin, and the low p-value for annual precipitation within the basin, precipitation in the basin may be increasing over time. Two annual streamflow trends, one upward and one downward, were in the upper parts of the basin. Data from USGS streamflow-gaging station 07346000 Big Cypress Bayou near Jefferson, Texas, indicated an upward trend in annual minimum streamflow and a downward trend in annual peak streamflow. The station is immediately downstream from Lake O’ the Pines; presumably, minimums have increased because of regulated releases, and annual peaks have decreased because of storage from the lake for flood control. Despite the known addition of 2,737,154 acre-feet of potential flood storage between 1898 and 2011 in the subbasins analyzed, there have not been widespread reductions in streamflow volumes in the Big Cypress Basin, except for within the drainage area for the farthest upstream station on the main stem downstream from Mount Pleasant, Texas.
Within the Guadalupe River Basin, results of precipitation trend analyses on an annual time step indicated an upward trend in the lower section of the basin, but no trends in annual streamflow were indicated in the lower section of the basin. In the upper section of the basin, data from 1 of the 13 stations indicated an upward trend in annual streamflow. Data from 6 of the 13 stations in the upper section of the basin indicated a trend in annual minimum streamflow with 4 upward and 2 downward trends. Data from 2 of the 13 stations in the upper section of the basin indicated downward trends in annual peak streamflow. Despite the known addition of 2,016,534 acre-feet of potential flood storage between 1849 and 2013 in the subbasins analyzed, streamflow volumes have not decreased in the Guadalupe River Basin.
Within the Neches River Basin, results of precipitation trend analyses on an annual time step indicated upward trends in the basin. None of the data from stations analyzed in the Neches River Basin indicated annual trends in streamflow despite upward trends in annual precipitation within the basin. Data from 9 of the 19 stations analyzed in the basin indicated upward trends in annual minimum streamflow. Data from one of the simulated-inflow stations indicated a downward trend in annual minimum streamflow into Sam Rayburn Reservoir. Data from two stations indicated downward trends in annual peak streamflow, and data from one small subbasin indicated an upward trend in annual peak streamflow. Despite the known addition of 4,839,609 acre-feet of potential flood storage between 1888 and 2008 in the subbasins analyzed, there have not been widespread reductions in streamflow volumes in the Neches River Basin.
Within the Sulphur River Basin, results of precipitation trend analyses on an annual time step indicated a moderate upward trend within the basin. Data from only one of the stations, the simulated inflow to Jim Chapman Lake, indicated an annual upward trend in streamflow despite an upward trend in annual precipitation throughout the basin. Data from three of the six stations in the Sulphur River Basin indicated upward trends in annual minimum streamflow, and data from one of the six stations indicated a downward trend in annual peak streamflow. Despite the known addition of 6,933,361 acre-feet of potential flood storage between 1904 and 2006 in the subbasins analyzed, streamflow volumes have not decreased in the Sulphur River Basin.
Within the Trinity River Basin, results of precipitation trend analyses on an annual time step indicated upward trends in most sections of the basin. Data from 8 of the 36 stations analyzed for trends in annual streamflow indicated upward trends, and all 8 stations are in the upper sections of the basin. None of the data from stations in the lower sections of the basin indicated trends in annual streamflow. Data from 16 of the 36 stations indicated upward trends in annual minimum streamflow. Upward trends in annual minimum streamflow could be the result of managed reservoir releases in combination with wastewater treatment plant releases in the large Dallas-Fort Worth metroplex in the upper sections of the basin. All the trends in annual peak streamflow were in the sections of the basin that include the Dallas-Fort Worth metroplex. Data from two stations, one USGS streamflow-gaging station and one simulated-inflow station, indicated upward trends in annual peak streamflow, and data from one streamflow-gaging station indicated a downward trend in annual peak streamflow. Of the basins included in this study, the Trinity River Basin has the second largest amount of potential flood storage of 8,947,349 acre-feet from dams added between 1890 and 2013. Eleven stations in the Trinity River Basin had positive associations between potential flood storage volume and annual streamflow volume, indicating that annual streamflow increases as potential flood storage increases. Data from 7 of the 11 stations also indicated upward trends in annual streamflow. The positive associations may be the result of increases in minimum streamflow, which could be the result of any combination of managed reservoir releases, wastewater treatment plant releases, or increased runoff from urbanized areas, particularly in the urbanized area of the Dallas-Fort Worth metroplex.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195137","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Fort Worth District","usgsCitation":"Harwell, G.R., McDowell, J.S., Gunn, C.L., and Garrett, B.S., 2020, Precipitation, temperature, groundwater-level elevation, streamflow, and potential flood storage trends within the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River basins in Texas through 2017 (ver. 1.1, April 2020): U.S. Geological Survey Scientific Investigations Report 2019–5137, 94 p., https://doi.org/10.3133/sir20195137.","productDescription":"Report: x, 94 p.; 5 Tables; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102896","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":399613,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109606.htm"},{"id":374071,"rank":9,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5137/coverthb2.jpg"},{"id":373986,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5137/versionHist.txt","text":"Version History","size":"1.35 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2019–5137 Version History"},{"id":371261,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5137/sir20195137_table9.xlsx","text":"Table 9—","size":"120 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 9","linkHelpText":"Summary of annual, seasonal, and monthly trends in the ratio of streamflow volume to precipitation volume in the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River Basins"},{"id":371258,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5137/sir20195137_table7.xlsx","text":"Table 7—","size":"64 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 7","linkHelpText":"Summary of precipitation temporal trends around the time of annual peak streamflow in the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River Basins"},{"id":371255,"rank":2,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5137/sir20195137_table5.xlsx","text":"Table 5—","size":"80 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 5","linkHelpText":"Summary of annual, seasonal, and monthly associations between precipitation volume and streamflow volume in the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River Basins"},{"id":371252,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L1F7PT","text":"USGS data release","description":"USGS data release","linkHelpText":"Data used to assess precipitation, temperature, groundwater-level elevation, streamflow, and potential flood storage trends within the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River Basins in Texas through 2017"},{"id":373985,"rank":7,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5137/sir20195137_v1.1.pdf","text":"Report","size":"20.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5137"},{"id":371259,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5137/sir20195137_table8.xlsx","text":"Table 8—","size":"144 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 8","linkHelpText":"Summary of annual, seasonal, and monthly streamflow volume trends in the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River Basins"},{"id":371262,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5137/sir20195137_table10.xlsx","text":"Table 10—","size":"48 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 10","linkHelpText":"Summary of trends in annual minimum streamflow and annual peak streamflow and relations between streamflow volume and potential flood storage volume in the Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River Basins"}],"country":"United States","state":"Texas","otherGeospatial":"Brazos, Colorado, Big Cypress, Guadalupe, Neches, Sulphur, and Trinity River basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.4667,\n 28.4167\n ],\n [\n -93.0619,\n 28.4167\n ],\n [\n -93.0619,\n 33.6667\n ],\n [\n -101.4667,\n 33.6667\n ],\n [\n -101.4667,\n 28.4167\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: January 2020; Version 1.1: April 2020","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
To assess and track changes to the rocky subtidal communities surrounding San Nicolas Island, the U.S. Navy entered into an agreement with the U.S. Geological Survey (USGS) in 2014 to conduct an ecological monitoring program at several sites around the island. Four permanent sites—Nav Fac 100, West End, Dutch Harbor, and Daytona 100—were established. The sites were based on ones that had been monitored since 1980 by USGS and were combined or expanded for better comparability with monitoring programs conducted at the other California Channel Islands. At the sites, scientists from USGS and our cooperator, the University of California, Santa Cruz, measured bottom cover of algae and sessile invertebrate species in quadrats, counted and sized fish on swimming transects, and counted a suite of kelps and invertebrates on benthic band transects. Holdfast diameter and number of stipes of giant kelp (Macrocystis pyrifera) were recorded on these transects, and size data were collected for urchins, sea stars, and shelled mollusks. Bottom temperatures were recorded at hourly intervals by archival data loggers that were deployed at the sites. This report focuses primarily on data collected in fall 2017 and spring 2018 and makes comparisons with data collected in previous years, beginning in fall 2014.
Nav Fac 100 is a site with a relatively low benthic profile, situated on the north side of San Nicolas Island. It was previously urchin dominated but underwent a dramatic decline in purple sea urchins in 2015 and 2016. Since then, macroalgae has become more prevalent as both annual brown algae, such as Dictyota, and perennials (for example, Cystoseira) have become established. The invasive brown alga Sargassum horneri has also become established. West End, on the southwest side of the island, also lacks much bottom relief but has more crevice habitat associated with boulders. It remains dominated by kelps and red algae, but red algae have decreased recently. Dutch Harbor, on the south side, has many high relief rocky reefs and had the greatest fish and non-motile invertebrate densities. It remains the most stable of the sites. Daytona 100, on the southeast side, has moderate relief and has remained a patchwork of kelp and urchin dominated areas with moderate fish density.
The main change at the sites during the last 4 years was the decline in urchin numbers at Nav Fac 100. There was storm-related mortality and subsequent recruitment in the M. pyrifera population at several of the sites in both 2016 and 2017. The winter of 2018, however, was relatively mild, with less destructive storm-related disturbance. The invasive brown alga S. horneri, first seen at San Nicolas Island at Nav Fac 100 in fall 2015, has become firmly established there during the last 2 sampling years. Finally, moderate increases were observed in purple urchin densities at all sites this spring. Long-term data are presented to illustrate trends and changes over the past three decades. Results indicate continued monitoring to evaluate ecosystem effects from perturbations owing to natural processes and anthropomorphic factors, including recovery of the sea otter population, changes in fisheries, invasive species and changing environmental conditions, could be valuable to inform managers’ decision-making.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191147","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Kenner, M.C., and Tomoleoni, J., 2020, Kelp forest monitoring at Naval Base Ventura County, San Nicolas Island, California: Fall 2017 and Spring 2018, Fourth Annual Report: U.S. Geological Survey Open-File Report 2019–1147, 76 p., https://doi.org/10.3133/ofr20191147.","productDescription":"vi, 76 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-111796","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":399434,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109596.htm"},{"id":371253,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1147/coverthb.jpg"},{"id":371254,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1147/ofr20191147.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1147"}],"country":"United States","state":"California","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 -119.64111328125,\n 33.18353672893615\n ],\n [\n -119.37744140625,\n 33.18353672893615\n ],\n [\n -119.37744140625,\n 33.32134852669881\n ],\n [\n -119.64111328125,\n 33.32134852669881\n ],\n [\n -119.64111328125,\n 33.18353672893615\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
We surveyed for Least Bell’s Vireos (Vireo bellii pusillus; vireo) along the San Luis Rey River, between College Boulevard in Oceanside and Interstate 15 in Fallbrook, California (middle San Luis Rey River), in 2019, and we surveyed and conducted nest monitoring for Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) in a survey area where breeding had historically been documented on the middle San Luis Rey River, in 2019. Surveys were conducted from April 11 to June 24 (vireo) and from May 16 to July 15 (flycatcher). We found 179 vireo territories, at least 124 of which were occupied by pairs. Vireo territories increased by 100 percent within the portion of the middle San Luis Rey River that burned as a result of a wildfire in 2017. In contrast, vireo territories increased by 5 percent within the unburned portion of the middle San Luis Rey River.
Vireos used five different habitat types in the survey area: mixed willow riparian, willow-cottonwood, riparian scrub, willow-sycamore, and upland scrub. Fifty-two percent of the vireos were detected in habitat characterized as mixed willow, and 92 percent of the vireos were detected in habitat with greater than 50 percent native plant cover. Of the 12 banded vireos detected in the survey area, 5 were resighted with a full color-band combination. One adult female with a unique color-band combination immigrated to the middle San Luis Rey River from Marine Corps Base Camp Pendleton (MCBCP). Five other vireos with single (natal) federal bands were recaptured, identified, and color banded in 2019. Two vireos with a single dark blue federal band, indicating that they were banded as nestlings on the lower San Luis Rey River (LSLR), could not be recaptured for identification. The five natal vireos that were recaptured on the middle San Luis Rey River dispersed from 1.4 to 8.3 kilometers (km) from their natal territories. Banded vireos with a known age ranged from 1 to 11 years old.
One resident flycatcher was observed in the survey area in 2019. The resident flycatcher (male) was detected in a territory of mixed willow habitat with greater than 50 percent native plant cover. He was detected as a single male from May 16 to July 17, 2019, and no evidence of pairing or nesting was observed. The male flycatcher with a unique color-band combination occupied the same territory in 2018 and 2019.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1122","usgsCitation":"Allen, L.D. and Kus, B.E., 2020, Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) on the Middle San Luis Rey River, San Diego County, southern California—2019 data summary: U.S. Geological Survey Data Series 1122, 11 p., https://doi.org/10.3133/ds1122.","productDescription":"iv, 11 p.","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-114314","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":371284,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1122/coverthb.jpg"},{"id":371285,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1122/ds1122.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Data Series 1122"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.55920410156249,\n 33.1398510418607\n ],\n [\n -116.5264892578125,\n 33.1398510418607\n ],\n [\n -116.5264892578125,\n 33.61919376817004\n ],\n [\n -117.55920410156249,\n 33.61919376817004\n ],\n [\n -117.55920410156249,\n 33.1398510418607\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
Rapid seismic deployments following large earthquakes capture ephemeral near‐field recordings of aftershocks and ambient noise that can provide valuable data for seismological studies. The U.S. Geological Survey installed 19 temporary seismic stations following the 4 July 2019 Mw 6.4 and 6 July 2019 (UTC) Mw 7.1 earthquakes near the city of Ridgecrest, California. The stations record the aftershock sequence beginning two days after the mainshock and are expected to remain in the field through approximately January 2020. The deployment augments the permanent seismic network in the area to improve azimuthal coverage and provide additional near‐field observations. This article summarizes the motivation and goals of the deployment; details of station installation, instrumentation, and configurations; and initial data quality and observations from the network. We expect these data to be useful for a range of studies including detailing near‐field variability in strong ground motions, determining stress drops and rupture directivity of small events, imaging the fault zone, documenting the evolution of crustal properties within and outside of the fault zone, and others.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220190296","usgsCitation":"Cochran, E.S., Wolin, E., McNamara, D.E., Yong, A., Wilson, D.C., Alvarez, M., van der Elst, N., McClain, A.R., and Steidl, J.H., 2020, The U.S. Geological Survey’s Rapid Seismic Array Deployment for the 2019 Ridgecrest Earthquake Sequence: Seismological Research Letters, v. 91, p. 1952-1960, https://doi.org/10.1785/0220190296.","productDescription":"9 p.","startPage":"1952","endPage":"1960","ipdsId":"IP-113314","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":378081,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.91900634765624,\n 35.302797817405796\n ],\n [\n -117.32711791992186,\n 35.302797817405796\n ],\n [\n -117.32711791992186,\n 35.9157474194997\n ],\n [\n -117.91900634765624,\n 35.9157474194997\n ],\n [\n -117.91900634765624,\n 35.302797817405796\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"91","noUsgsAuthors":false,"publicationDate":"2020-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolin, Emily 0000-0003-1610-1191","orcid":"https://orcid.org/0000-0003-1610-1191","contributorId":221834,"corporation":false,"usgs":true,"family":"Wolin","given":"Emily","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":797744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yong, Alan 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":204730,"corporation":false,"usgs":true,"family":"Yong","given":"Alan","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, David C. 0000-0001-9667-9449 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0001-9667-9449","contributorId":239707,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":797746,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alvarez, Mark 0000-0002-1361-5616","orcid":"https://orcid.org/0000-0002-1361-5616","contributorId":222021,"corporation":false,"usgs":true,"family":"Alvarez","given":"Mark","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797747,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"van der Elst, Nicholas 0000-0002-3812-1153 nvanderelst@usgs.gov","orcid":"https://orcid.org/0000-0002-3812-1153","contributorId":147858,"corporation":false,"usgs":true,"family":"van der Elst","given":"Nicholas","email":"nvanderelst@usgs.gov","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797748,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McClain, Adria Ruth 0000-0001-9371-9994","orcid":"https://orcid.org/0000-0001-9371-9994","contributorId":239708,"corporation":false,"usgs":true,"family":"McClain","given":"Adria","email":"","middleInitial":"Ruth","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797749,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Steidl, Jamison Haase 0000-0003-0612-7654","orcid":"https://orcid.org/0000-0003-0612-7654","contributorId":239709,"corporation":false,"usgs":true,"family":"Steidl","given":"Jamison","email":"","middleInitial":"Haase","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797750,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70215322,"text":"70215322 - 2020 - RAPTURE (RAD capture) panel facilitates analyses characterizing sea lamprey reproductive ecology and movement dynamics","interactions":[],"lastModifiedDate":"2020-10-16T14:45:24.319061","indexId":"70215322","displayToPublicDate":"2020-01-15T09:15:57","publicationYear":"2020","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":"RAPTURE (RAD capture) panel facilitates analyses characterizing sea lamprey reproductive ecology and movement dynamics","docAbstract":"Genomic tools are lacking for invasive and native populations of sea lamprey (Petromyzon marinus). Our objective was to discover single nucleotide polymorphism (SNP) loci to conduct pedigree analyses to quantify reproductive contributions of adult sea lampreys and dispersion of sibling larval sea lampreys of different ages in Great Lakes tributaries. Additional applications of data were explored using additional geographically expansive samples. We used restriction site‐associated DNA sequencing (RAD‐Seq) to discover genetic variation in Duffins Creek (DC), Ontario, Canada, and the St. Clair River (SCR), Michigan, USA. We subsequently developed RAD capture baits to genotype 3,446 RAD loci that contained 11,970 SNPs. Based on RAD capture assays, estimates of variance in SNP allele frequency among five Great Lakes tributary populations (mean FST 0.008; range 0.00–0.018) were concordant with previous microsatellite‐based studies; however, outlier loci were identified that contributed substantially to spatial population genetic structure. At finer scales within streams, simulations indicated that accuracy in genetic pedigree reconstruction was high when 200 or 500 independent loci were used, even in situations of high spawner abundance (e.g., 1,000 adults). Based on empirical collections of larval sea lamprey genotypes, we found that age‐1 and age‐2 families of full and half‐siblings were widely but nonrandomly distributed within stream reaches sampled. Using the genomic scale set of SNP loci developed in this study, biologists can rapidly genotype sea lamprey in non‐native and native ranges to investigate questions pertaining to population structuring and reproductive ecology at previously unattainable scales.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.6001","usgsCitation":"Sard, N., Smith, S., Homola, J., Kanefsky, J., Bravener, G., Adams, J.V., Holbrook, C., Hrodey, P.J., Tallon, K., and Scribner, K.T., 2020, RAPTURE (RAD capture) panel facilitates analyses characterizing sea lamprey reproductive ecology and movement dynamics: Ecology and Evolution, v. 10, no. 3, p. 1469-1488, https://doi.org/10.1002/ece3.6001.","productDescription":"20 p.","startPage":"1469","endPage":"1488","ipdsId":"IP-111320","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":458125,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.6001","text":"Publisher Index Page"},{"id":379470,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario, Wisconsin","otherGeospatial":"Brule River, Carp Lake Outlet, Duffins Creek, St Clair River, St Mary's River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.69189453125,\n 46.43217825300941\n ],\n [\n -91.49826049804688,\n 46.43217825300941\n ],\n [\n -91.49826049804688,\n 46.75679832604253\n ],\n [\n -91.69189453125,\n 46.75679832604253\n ],\n [\n -91.69189453125,\n 46.43217825300941\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.3807373046875,\n 41.95131994679697\n ],\n [\n -82.298583984375,\n 41.95131994679697\n ],\n [\n -82.298583984375,\n 43.01268088642034\n ],\n [\n -83.3807373046875,\n 43.01268088642034\n ],\n [\n -83.3807373046875,\n 41.95131994679697\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.80758666992188,\n 45.65964739507404\n ],\n [\n -84.69532012939452,\n 45.65964739507404\n ],\n [\n -84.69532012939452,\n 45.71217284793297\n ],\n [\n -84.80758666992188,\n 45.71217284793297\n ],\n [\n -84.80758666992188,\n 45.65964739507404\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.34066772460938,\n 46.2501492379416\n ],\n [\n -83.87374877929688,\n 46.2501492379416\n ],\n [\n -83.87374877929688,\n 46.60228013300284\n ],\n [\n -84.34066772460938,\n 46.60228013300284\n ],\n [\n -84.34066772460938,\n 46.2501492379416\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.10774230957031,\n 43.81842713569542\n ],\n [\n -79.00989532470703,\n 43.81842713569542\n ],\n [\n -79.00989532470703,\n 43.89331549391385\n ],\n [\n -79.10774230957031,\n 43.89331549391385\n 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University","active":true,"usgs":false}],"preferred":false,"id":801710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kanefsky, Jeannette","contributorId":243198,"corporation":false,"usgs":false,"family":"Kanefsky","given":"Jeannette","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":801711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bravener, Gale","contributorId":150995,"corporation":false,"usgs":false,"family":"Bravener","given":"Gale","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":801712,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adams, Jean V. 0000-0002-9101-068X jvadams@usgs.gov","orcid":"https://orcid.org/0000-0002-9101-068X","contributorId":3140,"corporation":false,"usgs":true,"family":"Adams","given":"Jean","email":"jvadams@usgs.gov","middleInitial":"V.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":801713,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":801714,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hrodey, Peter J.","contributorId":205578,"corporation":false,"usgs":false,"family":"Hrodey","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":6599,"text":"U.S. Fish and Wildlife Service, Marquette Biological Station","active":true,"usgs":false}],"preferred":false,"id":801715,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tallon, Kevin","contributorId":173286,"corporation":false,"usgs":false,"family":"Tallon","given":"Kevin","email":"","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":801716,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Scribner, Kim T.","contributorId":95434,"corporation":false,"usgs":false,"family":"Scribner","given":"Kim","email":"","middleInitial":"T.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":801717,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70236131,"text":"70236131 - 2020 - Peak ground motions and site response at Anza and Imperial Valley, California","interactions":[],"lastModifiedDate":"2022-08-30T14:00:58.547116","indexId":"70236131","displayToPublicDate":"2020-01-15T08:55:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3208,"text":"Pure and Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Peak ground motions and site response at Anza and Imperial Valley, California","docAbstract":"Power spectra of shear-waves for eighteen earthquakes from the Anza-Imperial Valley region were inverted for source, mid-path Q, site attenuation and site response. The motivation was whether differences in site attenuation (parameterized as t*, r/cQ, where r is distance along ray path near the site, c is shear velocity and Q is the quality factor that parameterizes attenuation) and site response could be correlated with residuals in peak values of velocity or acceleration after removing the affect of distance-dependent attenuation. We decomposed spectra of S-waves from horizontal components of 18 earthquakes from 2010 to 2018 into a common source for each event with ω−2 spectral fall-off at high frequencies and then projected the residuals onto path and site terms following the methodology of Boatwright et al. (Bull Seismol Soc Am 81:1754–1782, 1991). The site terms were constrained to have an amplification at a particular frequency governed by VS30 at two of the sites which had downhole shear-wave logs. The 18 events, 3 < M < 4, had moments between approximately 1020 and 1022 dyne-cm, and stress drops between 1 and 100 bars. Average mid-crust attenuation had a Q of 844 reflecting the average path through the crystalline rock of the San Jacinto Mountains. t* for each station corresponded to the geologic environment such that stations on hard rock had low t* (e.g. stations KNW, PFO and RDM) a station in the San Jacinto fault zone (station SND) had a moderate t* of 0.035 s and stations in the Imperial Valley usually had higher t*s. Generally t* correlated with average amplification suggesting that sites characterized by low surface velocities and higher attenuation also have more amplification in the 1–6 Hz band. Residuals of peak values were determined by subtracting the prediction of Boore and Atkinson (2008). There is a correlation between average amplification and peak velocity, but not peak acceleration. Interestingly, there is less scatter at high values of amplification although there is also less data. Scatter in values of peak velocity and peak acceleration are higher at shorter compared to longer durations. When using a frequency-dependent form for Q, variances are higher, sometimes much higher; the dataset does not support frequency-dependent Q, which is not similar to results from the Imperial Valley and northeastern North America.
","language":"English","publisher":"Springer","doi":"10.1007/s00024-019-02366-2","usgsCitation":"Fletcher, J.P., and Boatwright, J., 2020, Peak ground motions and site response at Anza and Imperial Valley, California: Pure and Applied Geophysics, v. 177, p. 2753-2769, https://doi.org/10.1007/s00024-019-02366-2.","productDescription":"17 p.","startPage":"2753","endPage":"2769","ipdsId":"IP-103872","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":458127,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00024-019-02366-2","text":"Publisher Index Page"},{"id":405902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Anza","otherGeospatial":"Imperial Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117,\n 32.7\n ],\n [\n -115.2,\n 32.7\n ],\n [\n -115.2,\n 33.8\n ],\n [\n -117,\n 33.8\n ],\n [\n -117,\n 32.7\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"177","noUsgsAuthors":false,"publicationDate":"2020-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Fletcher, Jon Peter B. 0000-0001-8885-6177 jfletcher@usgs.gov","orcid":"https://orcid.org/0000-0001-8885-6177","contributorId":1216,"corporation":false,"usgs":true,"family":"Fletcher","given":"Jon","email":"jfletcher@usgs.gov","middleInitial":"Peter B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":850200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boatwright, John","contributorId":219666,"corporation":false,"usgs":false,"family":"Boatwright","given":"John","affiliations":[{"id":40044,"text":"USGS, deceased","active":true,"usgs":false}],"preferred":false,"id":850201,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70207810,"text":"sir20195151 - 2020 - Storage capacity and sedimentation characteristics of the San Antonio Reservoir, California, 2018","interactions":[],"lastModifiedDate":"2022-04-25T20:37:40.39933","indexId":"sir20195151","displayToPublicDate":"2020-01-15T08:04:46","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":"2019-5151","displayTitle":"Storage Capacity and Sedimentation Characteristics of the San Antonio Reservoir, California, 2018","title":"Storage capacity and sedimentation characteristics of the San Antonio Reservoir, California, 2018","docAbstract":"The San Antonio Reservoir is a large water storage facility in Alameda County, California, and is a major component of the Hetch Hetchy Regional Water System (RWS). The RWS is a water-supply system owned and operated by the San Francisco Public Utilities Commission (SFPUC) and provides water for about 2.7 million people in the San Francisco, Santa Clara, Alameda, and San Mateo Counties. The San Antonio Reservoir is one of two RWS reservoirs in Alameda County and the third largest of the RWS reservoirs in the San Francisco Bay Area. The reservoir was formed by the James H. Turner Dam, which was completed in 1965. At the time of construction, the reservoir was estimated to have 50,500 acre-feet (acre-ft) of storage capacity. That early estimate was based on a 1963 pre-construction topographic map, which was drawn from aerial photographs. The capacity of the reservoir was later surveyed in 1994 and 2000. These two later surveys did not include the upper 18 feet (ft) of the reservoir, which represents roughly 30 percent of the overall storage volume. To determine the storage capacity and provide updated stage-capacity curves up to the spillway, the U.S. Geological Survey, in cooperation with the SFPUC, surveyed the bathymetry and shoreline of the reservoir in April 2018.
The bathymetric survey was performed by making depth soundings using a boat-mounted, multibeam echosounder. At the time of the survey, the water level was between 13 and 14 ft below the spillway elevation. To measure capacity between the water line up to the spillway elevation, topography along most of the shoreline was surveyed from the boat using a terrestrial Light Detection and Ranging (LiDAR) scanner and in other areas by using ground-survey techniques. Location during bathymetric and topographic data collection was determined using a Global Navigation Satellite System-Real Time Network system. Vertical profiles of sound speed were collected periodically. The sound-speed profiles were used to spatially and temporally adjust the sound-speed calculations used to determine depth from the soundings. Approximately 125 kilometers (78 miles) of transects with a total of about 560 million depth soundings and topographic LiDAR points were collected (about 160 per square meter). In addition, approximately 500 topographic survey points were collected in shallow, wadable areas and on land near the upper reservoir area using a Global Navigation Satellite System receiver attached to a fixed length survey rod. Depth soundings, terrestrial LiDAR points, topographic survey points, and a digitized shoreline were merged and interpolated to generate a digital elevation model (DEM) of the reservoir. Gridded elevation data extracted from the DEM were then tabulated to determine total reservoir capacity and create reservoir stage-surface area and stage-storage capacity tables.
Results of the reservoir capacity analysis indicated that the reservoir has 53,266 (plus or minus 140) acre-ft of storage capacity, which is an increase of 2,766 acre-ft (or 5.5 percent) greater than the original 1965 estimate; the increase is likely due to improved survey methods. Also, at the time of this 2018 survey, Intake #1 (the lowest intake) was not in operation. Intake #1 is estimated to be buried approximately 10 ft below the bed, whereas Intake #2 is about 20 ft above the bed. There are five intakes at different elevation levels; however, when consecutive lower intakes become inoperable due to sedimentation, the live storage capacity (capacity available for use) is reduced. At the time of this survey, the remaining live storage (above Intake #2) was approximately 52,363 acre-ft.
The 2018 stage-capacity curve was compared to the original 1965 stage-capacity curve. Although overall, the changes indicate an increase in storage capacity, the change in volume at 372.7 ft North American Vertical Datum of 1988 (370 ft National Geodetic Vertical Datum of 1929, NGVD 29) shows a decrease of 733 acre-ft (the elevation of 370 ft NGVD 29 was used because it is the lowest elevation available for the 1965 stage-capacity curves). This finding agrees with the observed accumulation of sediment over Intake #1. That volume was converted to an annual sediment yield of 0.35 acre-ft per square mile (or 165 cubic meters per square kilometer), which is of the same order of magnitude as that found in other watersheds for the Coast Ranges in California. A decrease of 733 acre-ft between 1965 and 2018 thus represents a loss of 1.5 percent of the overall storage capacity in the reservoir. The updated stage-surface area and stage-capacity tables provided in this report and online (https://doi.org/10.5066/P9KC9DU8) can be used by the SFPUC to improve reservoir operations and serve as an accurate baseline to monitor bathymetric changes in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195151","collaboration":"Prepared in cooperation with the San Francisco Public Utilities Commission","usgsCitation":"Marineau, M.D., Wright, S.A, and Lopez, J.V., 2020, Storage capacity and sedimentation characteristics of the San Antonio Reservoir, California, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5151, 34 p., https://doi.org/10.3133/sir20195151.","productDescription":"Report: vi, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-105258","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":399623,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109595.htm"},{"id":371223,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KC9DU8","linkHelpText":"Bathymetry, Stage-Area, and Stage-Volume Tables for the San Antonio Reservoir, California, 2018"},{"id":371222,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5151/sir20195151.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5151"},{"id":371221,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5151/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Antonio Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.81846618652345,\n 37.596415965954684\n ],\n [\n -121.85348510742188,\n 37.57138553454929\n ],\n [\n -121.841983795166,\n 37.565262680889965\n ],\n [\n -121.8335723876953,\n 37.56186087804736\n ],\n [\n -121.82378768920898,\n 37.5711134184077\n ],\n [\n -121.81726455688477,\n 37.582541440297746\n ],\n [\n -121.80301666259766,\n 37.5814531328266\n ],\n [\n -121.80473327636719,\n 37.59083926161267\n ],\n [\n -121.81846618652345,\n 37.596415965954684\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Recent advances in high-frequency water-quality sensors have enabled direct measurements of physical and chemical attributes in rivers and streams nearly continuously. Water-quality trends can be used to identify important watershed-scale changes driven by natural and anthropogenic influences. Statistical methods to estimate trends using high-frequency data are lacking. To address this gap, an evaluation of the generalized additive model (GAM) approach to test for trends in high-frequency data was conducted. Our proposed framework includes methods for handling serial correlation, trend estimation and slope-change detection, and trend interpretation at arithmetic scale for log-transformed variables. Water-temperature and turbidity data, representing two analytes with different temporal patterns, collected from the James River at Cartersville, Virginia, USA, were chosen for this analysis. Results indicated that the model, including flow, season, time covariates, and interaction between flow and season performed well for both analytes. The same model structure was applied to specific conductance data, collected from a small highly urbanized watershed, with satisfactory model performance. The water temperature GAM results indicated that the significant decreasing-then-increasing patterns after 2012 were mainly driven by air temperature changes. The turbidity trend was not significant over time. The specific conductance results showed a consistently upward trend over the last decade due to ever-increasing urbanization in the small watershed. This study suggests that the GAM method has great potential as a useful tool for trend analysis on high-frequency data, and for informing watershed managers of hydro-climatic and human influences on water quality by detecting crucial signal variation over time.
Atmospheric dust can influence biogeochemical cycles, accelerate snowmelt, and affect air, water quality, and human health. Yet, the bulk of atmospherically transported material remains poorly quantified in terms of total mass fluxes and composition. This lack of information stems in part from the challenges associated with measuring dust deposition. Here we report on the design and efficacy of a new dry deposition sampler (Dry Deposition Sampling Unit (DSU)) and method that quantifies the gravitational flux of dust particles. The sampler can be used alone or within existing networks such as those employed by the National Atmospheric Deposition Program (NADP). Because the samplers are deployed sterile and the use of water to remove trapped dust is not required, this method allows for the recovery of unaltered dry material suitable for subsequent chemical and microbiological analyses. The samplers were tested in the laboratory and at 15 field sites in the western United States. With respect to material retention, sampler performance far exceeded commonly used methods. Retrieval efficiency was >97% in all trials and the sampler effectively preserved grain size distributions during wind exposure experiments. Field tests indicated favorable comparisons to dust-on-snow measurement across sites (r2 0.70, p < 0.05) and within sites to co-located aerosol data (r2 0.57–0.99, p < 0.05). The inclusion of dust deposition and composition monitoring into existing networks increases spatial and temporal understanding of the atmospheric transport on materials and substantively furthers knowledge of the effects of dust on terrestrial ecosystems and human exposure to dust and associated deleterious compounds.
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Network stakeholders often inquire about the importance of sites and their locations when discussing funding and monitoring design. Support vector machines (SVMs) can be useful by their assigning each monitoring site as either a support or nonsupport vector. A potentiometric surface was created from synthetic data and 800 random observation locations (sites) as an analog to a groundwater-level network. Using generalized additive models for potentiometric surface prediction, simulations show that a subsample of support vectors from the 800 sites will out perform random samples of sample size equaling the support vector count. Support vector percentages from simulation quantify the recurrence that SVMs assign each site as a support vector, and these percentages in turn measure site importance. An example application of support vector percentages identifies important monitoring sites needed to regionalize the 0.1 annual exceedance probability peak streamflow. The results indicate that 152 of 283 streamgages with support vector percentages equalling 100 percent have not operated since about 2000 and generally have much smaller drainage areas than the greater streamgage network in Texas. The drainage area disparity is an indication of historical imbalance in peak streamflow data acquisition from various stream sizes in Texas.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2019.124522","collaboration":"","usgsCitation":"Asquith, W.H., 2020, The use of support vectors from support vector machines for hydrometeorologic monitoring network analyses: Journal of Hydrology, v. 583, 124522, 10 p., https://doi.org/10.1016/j.jhydrol.2019.124522.","productDescription":"124522, 10 p.","ipdsId":"IP-104552","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":374045,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"583","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":787258,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70208025,"text":"70208025 - 2020 - A round-robin evaluation of the repeatability and reproducibility of environmental DNA assays for dreissenid mussels","interactions":[],"lastModifiedDate":"2020-10-28T15:09:08.954274","indexId":"70208025","displayToPublicDate":"2020-01-13T16:41:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal 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One way to reduce the uncertainty of eDNA is to identify laboratory‐based protocols that ensure repeatable and reproducible results. We conducted a double‐blind round‐robin analysis of probe‐based assays for DNA of dreissenid (Dreissena spp.) mussels, which are prolific aquatic invaders that can cause significant economic and ecological impacts. DNA extract from water samples spiked with known amounts of dreissenid DNA and from water samples collected from waters with and without dreissenids were analyzed by four independent research laboratories. We used results to calculate detection repeatability within laboratories and assays, detection reproducibility among laboratories and assays, and estimated dreissenid DNA copy number precision and accuracy. Laboratory and assay repeatability and reproducibility of detection results were high, 91% and 92%, respectively. The estimated copy numbers were neither precise nor accurate for samples spiked with <773 gene copies. These results suggest that eDNA surveillance of dreissenid mussels, using the protocols evaluated herein, can generate reliable detection data for decision‐making. However, managers should be cautious about using the quantitative information often associated with eDNA detections, especially when DNA is at lower abundance. Our results provide strong support that eDNA has the potential to provide repeatable and reproducible evidence under varying laboratory conditions and for different sample water chemistries. This is reassuring since the demand for eDNA surveillance is widespread and number of laboratories that process eDNA samples is growing steadily.
","language":"English","publisher":"Wiley","doi":"10.1002/edn3.68","usgsCitation":"Sepulveda, A.J., Hutchins, P.R., Jackson, C., Ostberg, C.O., Laramie, M., Amberg, J., Counihan, T., Hoegh, A.B., and Pilliod, D.S., 2020, A round-robin evaluation of the repeatability and reproducibility of environmental DNA assays for dreissenid mussels: Environmental DNA, v. 2, no. 4, p. 446-459, https://doi.org/10.1002/edn3.68.","productDescription":"14 p.","startPage":"446","endPage":"459","ipdsId":"IP-111602","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":458141,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/edn3.68","text":"Publisher Index Page"},{"id":437164,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NMZZNP","text":"USGS data release","linkHelpText":"PCR results from dreissenid mussel round robin assay analyses, 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0000-0003-1479-8458","orcid":"https://orcid.org/0000-0003-1479-8458","contributorId":220731,"corporation":false,"usgs":true,"family":"Ostberg","given":"Carl","middleInitial":"O.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":780183,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Laramie, Matthew 0000-0001-7820-2583 mlaramie@usgs.gov","orcid":"https://orcid.org/0000-0001-7820-2583","contributorId":152532,"corporation":false,"usgs":true,"family":"Laramie","given":"Matthew","email":"mlaramie@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":780184,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Amberg, Jon 0000-0002-8351-4861 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Documented impacts of invasive zebra mussels (Dreissena polymorpha) and spiny water fleas (Bythotrephes cederströmii; hereafter Bythotrephes) on native fishes are variable and context dependent across locations and time periods. Here, we use a hierarchical Bayesian analysis of a 35-year dataset on two fish species from 9 lakes to demonstrate that early life growth of ecologically important fishes are influenced by these aquatic invasive species. Walleye (Sander vitreus) in their first year of life grew more slowly in the presence of either invader after correcting for temperature (measured by degree days), and were on average 12 or 14% smaller at the end of their first summer following invasion by Bythotrephes or zebra mussels, respectively. Yellow perch (Perca flavescens) growth was less affected by invasion. Yellow perch on average grew more slowly in their first year of life following invasion by zebra mussels, although this effect was not statistically distinguishable from zero. Early life growth of both walleye and yellow perch was less tightly coupled to degree days in invaded systems, as demonstrated by increased variance surrounding the degree day-length relationship. Smaller first-year size is related to walleye survival and recruitment to later life stages and has important implications for lake food webs and fisheries management. Future research quantifying effects of zebra mussels and Bythotrephes on other population-level processes and across a wider gradient of lake types is needed to understand the mechanisms driving observed changes in walleye growth.
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Yet, until April 2018, no publicly-available, regularly updated data source existed to describe those turbines and their locations. Under a cooperative research and development agreement, analysts from three organizations collaborated to develop and release the United States Wind Turbine Database (USWTDB) - a publicly available, continuously updated, spatially rectified data source of locations and attributes of utility-scale wind turbines in the United States. Technical specifications and wind facility data, incorporated from five sources, undergo rigorous quality control. The location of each turbine is visually verified using high-resolution aerial imagery. The quarterly-updated data are available in a variety of formats, including an interactive web application, comma-separated values (CSV), shapefile, and application programming interface (API). 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Laboratory","active":true,"usgs":false}],"preferred":false,"id":793137,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225829,"text":"70225829 - 2020 - Using the Lomb-Scargle method for wave statistics from gappy time series","interactions":[],"lastModifiedDate":"2021-11-10T14:50:24.002282","indexId":"70225829","displayToPublicDate":"2020-01-13T08:43:38","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Using the Lomb-Scargle method for wave statistics from gappy time series","docAbstract":"Sandwich Town Neck Beach in Sandwich, MA, has experienced substantial erosion and has been the subject of efforts by the town and private landowners to limit the sand loss. Erosion has been particularly dramatic in the past five years with the loss of dwellings. Sandwich's nourishment efforts presented a unique opportunity for scientists at the U.S. Geological Survey Woods Hole Coastal and Marine Science Center to monitor beach morphology and to test new technologies and techniques such as geo-referenced drone imaging. Two bottom lander deployments were performed in Cape Cod Bay at a location that was key to model the fate of waves at Sandwich Town Neck Beach and to support the study of beach morphological evolution. The study period was after the town nourished the beach and during a time when several intense winter storms reshaped the beach and removed much of the nourished sand. A TRDI Workhorse Sentinel V ADCP was used for both deployments. For wave bursts, the instruments collected 2048 samples at 2 Hz every hour. The first deployment during the winter of 2016 returned good quality data. The second deployment during the following winter had gaps throughout the time series from a wiring problem in the external battery pack. The timing of the gaps was random, the duration approximately 100 s. While most of the bursts started at the top of each hour, many had 1-3 gaps within. Time series data with random gaps are problematic for computing spectral density, and thus, wave statistics. This kind of situation is familiar in other scientific disciplines such as astrophysics [1], where techniques exist to find stationary signals in sparse data. One of these methods is the Lomb-Scargle technique for computing periodograms. The most useful feature of the Lomb-Scargle (LS) method is that it allows the spectral analysis of incomplete records, without having to manipulate the record to extrapolate from or replace missing data. We compared the effectiveness of LS against common methods of averaging Fourier transforms such as a simple un-windowed Fast Fourier transform (FFT), Welch's method, and TRDI's Wavesmon software; methods that are commonly used in oceanography for non-gappy data. Synthetic data series that have been artificially modified to introduce gaps were used to evaluate the performance of each method. The LS approach was able to recover spectral density even with several 100-s gaps present. The method was applied here to the gappy and non-gappy data from both Sandwich deployments, and wave statistics were obtained and compared to the wave-buoy data. LS was used to process data that contains gaps that was rejected by Wavesmon, which was approximately 39% of the dataset. Significant wave height and peak period from LS compared well with buoy data. Mean period computed on gappy data using LS produced values biased low, compared with other methods when gaps were filled with the mean value. The LS technique has potential to uncover low-frequency signals such as infragravity waves from gappy records where the non-gappy segments are not long enough to resolve them. It has potential to unlock new information from older data sets.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2019 IEEE/OES twelfth current, waves and turbulence measurement (CWTM)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"IEEE Oceanic Engineering Society - Current, Waves, Turbulence and Measurement Applications Workshop","conferenceDate":"Mar 10-13, 2019","language":"English","publisher":"IEEE","doi":"10.1109/CWTM43797.2019.8955285","usgsCitation":"Martini, M.A., Aretxabaleta, A., and Sherwood, C.R., 2020, Using the Lomb-Scargle method for wave statistics from gappy time series, in 2019 IEEE/OES twelfth current, waves and turbulence measurement (CWTM), Mar 10-13, 2019, 9 p., https://doi.org/10.1109/CWTM43797.2019.8955285.","productDescription":"9 p.","ipdsId":"IP-105269","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":391573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","city":"Sandwich","otherGeospatial":"Sandwich Town Neck Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.48888206481934,\n 41.762413206292656\n ],\n [\n -70.47154426574707,\n 41.762413206292656\n ],\n [\n -70.47154426574707,\n 41.77297600540535\n ],\n [\n -70.48888206481934,\n 41.77297600540535\n ],\n [\n -70.48888206481934,\n 41.762413206292656\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Martini, Marinna A. 0000-0002-7757-5158 mmartini@usgs.gov","orcid":"https://orcid.org/0000-0002-7757-5158","contributorId":2456,"corporation":false,"usgs":true,"family":"Martini","given":"Marinna","email":"mmartini@usgs.gov","middleInitial":"A.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826576,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}