The US Fish and Wildlife Service has initiated a re-envisioned approach for providing decision makers with the best available science and synthesis of that information, called the Species Status Assessment (SSA), for endangered species decision making. The SSA report is a descriptive document that provides decision makers with an assessment of a species’ current status and predicted future status. These analyses support all manner of decisions under the US Endangered Species Act, such as listing, reclassification, recovery planning, etc. Novel scientific analysis and predictive modeling in SSAs could be an important part of rooting species conservation decisions in current data and cutting edge analytical and modeling techniques. Here we describe a novel analysis of available data to assess current condition of eastern black rail across its range in a dynamic occupancy analysis. We used the results of the analysis to develop a site occupancy projection model where the model parameters (initial occupancy, site persistence, colonization) were linked to environmental covariates, such as land management and land cover change (sea-level rise, development, etc.). We used the projection model to predict future conditions under multiple sea-level rise and habitat management scenarios. Occupancy probability and site colonization were low in all analysis units and site persistence was also low, suggesting low resiliency and redundancy currently. Extinction probability was high for all analysis units in all simulated scenarios except one with significant effort to preserve existing habitat, suggesting low future resiliency and redundancy. With results of these data analyses and predictive modeling, the US Fish and Wildlife Service concluded that protections of the Endangered Species Act were warranted for this subspecies.
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TW exposures and potential human-health concerns of 540 organics and 35 inorganics were assessed in 45 Chicago area United States (US) homes in 2017. No US Environmental Protection Agency (EPA) enforceable Maximum Contaminant Level(s) (MCL) were exceeded in any residential or water treatment plant (WTP) pre-distribution TW sample. Ninety percent (90%) of organic analytes were not detected in treated TW, emphasizing the high quality of the Lake Michigan drinking-water source and the efficacy of the drinking-water treatment and monitoring. Sixteen (16) organics were detected in >25% of TW samples, with about 50 detected at least once. Low-level TW exposures to unregulated disinfection byproducts (DBP) of emerging concern, per/polyfluoroalkyl substances (PFAS), and three pesticides were ubiquitous. Common exceedances of non-enforceable EPA MCL Goal(s) (MCLG) of zero for arsenic [As], lead [Pb], uranium [U]), bromodichloromethane, and tribromomethane suggest potential human health concerns and emphasize the continuing need for improved understanding of cumulative effects of low-concentration mixtures on vulnerable sub-populations. Because DBP dominated TW organics, residential TW concentrations are potentially predictable with expanded pre-distribution DBP monitoring. However, several TW chemicals, notably Pb and several infrequently detected organic compounds, were not readily explained by pre distribution samples, illustrating the need for continued broad inorganic/organic TW characterization to support consumer assessment of acceptable risk and point-of-use treatment options.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.137236","usgsCitation":"Bradley, P., Argos, M., Kolpin, D., Meppelink, S., Romanok, K., Smalling, K., Focazio, M.J., Allen, J.M., Dietze, J., Devito, M.J., Donovan, A., Evans, N., Givens, C.E., Gray, J., Higgins, C.P., Hladik, M.L., Iwanowicz, L., Journey, C., Lane, R.F., Laughrey, Z.R., Loftin, K., McCleskey, R.B., McDonough, C.A., Medlock Kakaley, E.K., Meyer, M.T., Holthouse-Putz, A., Richardson, S.D., Stark, A., Weis, C.P., Wilson, V.S., and Zehraoui, A., 2020, Mixed organic and inorganic tapwater exposures and potential effects in greater Chicago area, USA: Science of the Total Environment, v. 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Blaine 0000-0002-2521-8052 rbmccles@usgs.gov","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":147399,"corporation":false,"usgs":true,"family":"McCleskey","given":"R.","email":"rbmccles@usgs.gov","middleInitial":"Blaine","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":782266,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"McDonough, Carrie A. 0000-0001-5152-8495","orcid":"https://orcid.org/0000-0001-5152-8495","contributorId":205664,"corporation":false,"usgs":false,"family":"McDonough","given":"Carrie","email":"","middleInitial":"A.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":782267,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Medlock Kakaley, Elizabeth K","contributorId":220449,"corporation":false,"usgs":false,"family":"Medlock Kakaley","given":"Elizabeth","email":"","middleInitial":"K","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":782268,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Meyer, Michael T. 0000-0001-6006-7985 mmeyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6006-7985","contributorId":866,"corporation":false,"usgs":true,"family":"Meyer","given":"Michael","email":"mmeyer@usgs.gov","middleInitial":"T.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":782269,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Holthouse-Putz, Andrea","contributorId":222472,"corporation":false,"usgs":false,"family":"Holthouse-Putz","given":"Andrea","email":"","affiliations":[{"id":40543,"text":"City of Chicago, Department of Water Management","active":true,"usgs":false}],"preferred":false,"id":782237,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Richardson, Susan D 0000-0001-6207-4513","orcid":"https://orcid.org/0000-0001-6207-4513","contributorId":222473,"corporation":false,"usgs":false,"family":"Richardson","given":"Susan","email":"","middleInitial":"D","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":782238,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Stark, Alan","contributorId":210215,"corporation":false,"usgs":false,"family":"Stark","given":"Alan","email":"","affiliations":[],"preferred":false,"id":782239,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Weis, Christopher P. 0000-0002-7678-1080","orcid":"https://orcid.org/0000-0002-7678-1080","contributorId":205667,"corporation":false,"usgs":false,"family":"Weis","given":"Christopher","email":"","middleInitial":"P.","affiliations":[{"id":37136,"text":"NIH/NIEHS","active":true,"usgs":false}],"preferred":false,"id":782240,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Wilson, Vickie S. 0000-0003-1661-8481","orcid":"https://orcid.org/0000-0003-1661-8481","contributorId":184092,"corporation":false,"usgs":false,"family":"Wilson","given":"Vickie","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":782241,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Zehraoui, Abderrahman","contributorId":210218,"corporation":false,"usgs":false,"family":"Zehraoui","given":"Abderrahman","email":"","affiliations":[],"preferred":false,"id":782242,"contributorType":{"id":1,"text":"Authors"},"rank":31}]}} ,{"id":70208313,"text":"pp1824H - 2020 - Geology and assessment of undiscovered oil and gas resources of the Franklinian Shelf Province, Arctic Canada and North Greenland, 2008","interactions":[{"subject":{"id":70208313,"text":"pp1824H - 2020 - Geology and assessment of undiscovered oil and gas resources of the Franklinian Shelf Province, Arctic Canada and North Greenland, 2008","indexId":"pp1824H","publicationYear":"2020","noYear":false,"chapter":"H","displayTitle":"Geology and Assessment of Undiscovered Oil and Gas Resources of the Franklinian Shelf Province, Arctic Canada and North Greenland, 2008","title":"Geology and assessment of undiscovered oil and gas resources of the Franklinian Shelf Province, Arctic Canada and North Greenland, 2008"},"predicate":"IS_PART_OF","object":{"id":70193865,"text":"pp1824 - 2017 - The 2008 Circum-Arctic Resource Appraisal ","indexId":"pp1824","publicationYear":"2017","noYear":false,"title":"The 2008 Circum-Arctic Resource Appraisal "},"id":1}],"isPartOf":{"id":70193865,"text":"pp1824 - 2017 - The 2008 Circum-Arctic Resource Appraisal ","indexId":"pp1824","publicationYear":"2017","noYear":false,"title":"The 2008 Circum-Arctic Resource Appraisal "},"lastModifiedDate":"2024-06-26T14:18:50.316641","indexId":"pp1824H","displayToPublicDate":"2020-02-11T07:45:40","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1824","chapter":"H","displayTitle":"Geology and Assessment of Undiscovered Oil and Gas Resources of the Franklinian Shelf Province, Arctic Canada and North Greenland, 2008","title":"Geology and assessment of undiscovered oil and gas resources of the Franklinian Shelf Province, Arctic Canada and North Greenland, 2008","docAbstract":"In 2008, the U.S. Geological Survey assessed the potential for undiscovered oil and gas resources of the Franklinian Shelf Province of northern Canada and Greenland as part of the U.S. Geological Survey Circum-Arctic Resource Appraisal Program. The Franklinian Shelf Province lies along the northernmost edge of the North American craton in Greenland and Canada. It encompasses a Cambrian through Middle Devonian passive margin sequence deposited on the margin of an ocean formed by rifting and seafloor spreading that began in latest Precambrian time and continued into Ordovician time. In the Canadian part of the province, the passive margin sequence is overlain by a thick succession of Devonian clastic strata shed from uplifts produced by the Caledonian collision between Laurentia and Baltica that closed Iapetus Ocean. The late Silurian to Early Devonian Boothia-Cornwallis uplifts within the region, apparently a distal effect of earlier phases of the Caledonian collision, were local sources of clastic wedges within the predominantly carbonate shelf sequence. Much of the northern part of the province was subjected to folding and thrusting during Late Devonian to earliest Carboniferous Ellesmerian deformation, followed by a prolonged period of erosion. The eastern part of the province again experienced transpressive and compressive deformation as Greenland converged with North America during the early Tertiary Eurekan orogeny.
Potential source rocks include Ordovician to Lower Devonian shales that contain abundant oil-prone organic matter, deposited on the outer continental shelf and slope. The most likely source rocks are Silurian strata, deposited as the continental shelf was drowned by a marine transgression caused by regional subsidence most likely associated with thrust loading. Potential source rocks in Greenland also may include organic-rich Cambrian shales deposited on the continental shelf. Rapid burial by thick Caledonian-derived strata in Late Devonian time abruptly matured the source rocks and generated oil; continued rapid burial may have cracked much of the accumulated oil to gas. In North Greenland, oil generation may have resulted from burial by now-eroded Devonian strata or from burial by Ellesmerian thrust sheets. Widespread bitumen in outcrops and in exploration wells appears to confirm that oil was indeed generated.
Potential reservoirs include Cambrian nearshore clastic strata, Cambrian to Silurian carbonate bank strata, and Silurian to Middle Devonian reef buildups on the drowned shelf. Because Ellesmerian deformation postdated migration, only stratigraphic traps are likely, except in the area of the Boothia-Cornwallis uplift, a north-trending, structurally elevated zone, where structural traps formed by late Silurian to Early Devonian deformation are possible. It is unlikely that any large accumulations survived subsequent deformation or uplift and erosion.
Three assessment units were defined: the Western Franklinian Shelf, the Boothia-Cornwallis Uplift, and the Eastern Franklinian Shelf Assessment Units. These assessment units were not quantitatively assessed, mostly because of the high risk to timing and preservation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1824H","usgsCitation":"Tennyson, M.E., and Pitman, J.K., 2020, Geology and assessment of undiscovered oil and gas resources of the Franklinian Shelf Province, Arctic Canada and North Greenland, 2008, chap. H of Moore, T.E., and Gautier, D.L., eds., The 2008 Circum-Arctic Resource Appraisal: U.S. Geological Survey Professional Paper 1824, 19 p., https://doi.org/10.3133/pp1824H.","productDescription":"Report: vi, 19 p.; 3 appendixes","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-062464","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":372224,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/h/pp1824h_appendix3.xls","text":"Appendix","size":"50 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Assessment input data for the Eastern Franklinian Shelf Assessment Unit."},{"id":372223,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/h/pp1824h_appendix2.xls","text":"Appendix 2","size":"50 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Assessment input data for the Boothia-Cornwallis Uplift Assessment Unit."},{"id":372222,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/h/pp1824h_appendix1.xls","text":"Appendix 1","size":"50 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Assessment input data for the Western Franklinian Shelf Assessment Unit."},{"id":372221,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1824/h/pp1824h.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":372145,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1824/h/coverthb.jpg"}],"country":"Canada, Greenland","otherGeospatial":"Franklinian Shelf Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -26.54296875,\n 82.2379940231732\n ],\n [\n -22.32421875,\n 83.42021497175465\n ],\n [\n -34.98046875,\n 83.92369331796976\n ],\n [\n -80.15625,\n 83.31873282163234\n ],\n [\n -127.96875,\n 75.80211845876491\n ],\n [\n -139.21874999999997,\n 68.72044056989829\n ],\n [\n -132.890625,\n 62.75472592723178\n ],\n [\n -89.47265625,\n 63.39152174400882\n ],\n [\n -69.08203125,\n 71.41317683396566\n ],\n [\n -26.54296875,\n 82.2379940231732\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information,
Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
Sedimentary basins in the Puget Sound region, Washington State, increase ground‐motion intensity and duration of shaking during local earthquakes. We analyze Pacific Northwest Seismic Network and U.S. Geological Survey strong‐motion recordings of five local earthquakes (M 3.9–6.8), including the 2001 Nisqually earthquake, to characterize sedimentary basin effects within the Seattle and Tacoma basins. We observe basin‐edge generated surface waves at sites within the Seattle basin for most ray paths that cross the Seattle fault zone. We also note previously undocumented basin‐edge surface waves in the Tacoma basin during one of the local earthquakes. To place quantitative constraints on basin amplification, we determine amplification factors by computing the spectral ratios of inside‐basin sites to outside‐basin sites at 1, 2, 3, and 5 s periods. Ground shaking is amplified in the Seattle basin for all the earthquakes analyzed and for a subset of events in the Tacoma basin. We find that the largest amplification factors in the Seattle basin are produced by a shallow earthquake located to the southwest of the basin. Our observation suggests that future shallow crustal and megathrust earthquakes rupturing west of the Puget Lowland will produce greater amplification within the Seattle basin than has been seen for intraslab events. We also perform ground‐motion simulations using a finite‐difference method to validate a 3D Cascadia velocity model (CVM) by comparing properties of observed and synthetic waveforms up to a frequency of 1 Hz. Basin‐edge effects are well reproduced in the Seattle basin, but are less well resolved in the Tacoma basin. Continued study of basin effects in the Tacoma basin would improve the CVM.
Climate variation and trends affect species distribution and abundance across large spatial extents. However, most studies that predict species response to climate are implemented at small spatial scales or are based on occurrence‐environment relationships that lack mechanistic detail. Here, we develop an integrated population model (IPM) for multi‐site count and capture‐recapture data for a declining migratory songbird, Wilson's warbler (Cardellina pusilla), in three genetically distinct breeding populations in western North America. We include climate covariates of vital rates, including spring temperatures on the breeding grounds, drought on the wintering range in northwest Mexico, and wind conditions during spring migration. Spring temperatures were positively related to productivity in Sierra Nevada and Pacific Northwest genetic groups, and annual changes in productivity were important predictors of changes in growth rate in these populations. Drought condition on the wintering grounds was a strong predictor of adult survival for coastal California and Sierra Nevada populations; however, adult survival played a relatively minor role in explaining annual variation in population change. A latent parameter representing a mixture of first‐year survival and immigration was the largest contributor to variation in population change; however, this parameter was estimated imprecisely, and its importance likely reflects, in part, differences in spatio‐temporal distribution of samples between count and capture‐recapture data sets. Our modeling approach represents a novel and flexible framework for linking broad‐scale multi‐site monitoring data sets. Our results highlight both the potential of the approach for extension to additional species and systems, as well as needs for additional data and/or model development.
The U.S. Geological Survey assessed undiscovered unconventional hydrocarbon resources reservoired in the Upper Cretaceous Tuscaloosa marine shale (TMS) of southern Mississippi and adjacent Louisiana in 2018. As part of the assessment, oil-source rock correlations were examined in the TMS play area where operators produce light (38–45° API), sweet oil from horizontal, hydraulically-fractured wells in an overpressured ‘high-resistivity’ (>5 Ω-m) zone at the base of the TMS. Geochemical data from 39 oil samples and 17 source rock solvent extracts collected from the TMS play area indicate close correspondence for Tuscaloosa Group oils [from lower Tuscaloosa, middle Tuscaloosa (the TMS) and upper Tuscaloosa reservoirs] in thermal maturity (computed from MPI), SARA proportions, n-alkane distributions, isoprenoid and DBT/P ratios, monoaromatic steroids, and δ13C isotopic compositions (from whole oils, saturate and aromatic fractions). Other parameters (normal steranes, extended homohopanes, C31R/C30 hopane, norhopane/hopane and tricyclic terpane ratios, gammacerane/hopane) show most oil samples have similar values, suggesting all Tuscaloosa Group oils are from a common mixed marine-terrigenous source rock. Tighter distributions for triaromatic steroid (TAS) and δ13C isotopic composition for conventional oils in lower and upper Tuscaloosa reservoirs may indicate charge occurred in a single or shorter pulse relative to TMS oils which show broader TAS and δ13C properties, possibly from their generation over an extended period of burial maturation. Dissimilarity in geochemical properties between lower Tuscaloosa source rock solvent extracts and Tuscaloosa Group oils indicates lower Tuscaloosa source rocks did not contribute significantly to conventional and unconventional Tuscaloosa Group hydrocarbon accumulations. Whereas, TMS solvent extracts are similar to Tuscaloosa Group oils, suggesting an oil-source rock correlation. Excluding the possibility for long-distance lateral migration from a similar source downdip (which is unnecessary given thermal maturity considerations), the observations indicate 1. the TMS is a self-sourced reservoir, 2. the TMS is the source of oils accumulated in nearby conventional Tuscaloosa Group reservoirs, and 3. thin organic-rich shales in the lower Tuscaloosa did not contribute substantially to any oil accumulations in the Tuscaloosa Group.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.petrol.2020.107015","usgsCitation":"Hackley, P.C., Dennen, K.O., Garza, D., Lohr, C., Valentine, B., Hatcherian, J.J., Enomoto, C., and Dulong, F.T., 2020, Oil-source rock correlation studies in the unconventional Upper Cretaceous Tuscaloosa marine shale (TMS) petroleum system, Mississippi and Louisiana, USA: Journal of Petroleum Science and Engineering, v. 190, 107015, 16 p., https://doi.org/10.1016/j.petrol.2020.107015.","productDescription":"107015, 16 p.","ipdsId":"IP-110731","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":457773,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.petrol.2020.107015","text":"Publisher Index Page"},{"id":384492,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana, Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.15283203125,\n 29.19053283229458\n ],\n [\n -88.08837890625,\n 29.19053283229458\n ],\n [\n -88.08837890625,\n 33.063924198120645\n ],\n [\n -94.15283203125,\n 33.063924198120645\n ],\n [\n -94.15283203125,\n 29.19053283229458\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"190","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dennen, Kristin Opferkuch","contributorId":255529,"corporation":false,"usgs":true,"family":"Dennen","given":"Kristin","email":"","middleInitial":"Opferkuch","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garza, Daniel","contributorId":255532,"corporation":false,"usgs":false,"family":"Garza","given":"Daniel","email":"","affiliations":[{"id":51576,"text":"Sanchez Oil & Gas Corporation","active":true,"usgs":false}],"preferred":false,"id":812501,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lohr, Celeste 0000-0001-6287-9047 clohr@usgs.gov","orcid":"https://orcid.org/0000-0001-6287-9047","contributorId":209992,"corporation":false,"usgs":true,"family":"Lohr","given":"Celeste","email":"clohr@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812520,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Valentine, Brett 0000-0002-8678-2431 bvalentine@usgs.gov","orcid":"https://orcid.org/0000-0002-8678-2431","contributorId":209829,"corporation":false,"usgs":true,"family":"Valentine","given":"Brett","email":"bvalentine@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812521,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":812522,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Enomoto, Catherine B. 0000-0002-4119-1953","orcid":"https://orcid.org/0000-0002-4119-1953","contributorId":211802,"corporation":false,"usgs":true,"family":"Enomoto","given":"Catherine B.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812523,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dulong, Frank T. 0000-0001-7388-647X fdulong@usgs.gov","orcid":"https://orcid.org/0000-0001-7388-647X","contributorId":650,"corporation":false,"usgs":true,"family":"Dulong","given":"Frank","email":"fdulong@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812524,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70206441,"text":"sir20195122 - 2020 - Hydrogeologic characterization, groundwater chemistry, and vulnerability assessment, Ute Mountain Ute Reservation, Colorado and Utah","interactions":[],"lastModifiedDate":"2022-04-25T19:05:32.137207","indexId":"sir20195122","displayToPublicDate":"2020-02-10T14:00:00","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":"2019-5122","displayTitle":"Hydrogeologic Characterization, Groundwater Chemistry, and Vulnerability Assessment, Ute Mountain Ute Reservation, Colorado and Utah","title":"Hydrogeologic characterization, groundwater chemistry, and vulnerability assessment, Ute Mountain Ute Reservation, Colorado and Utah","docAbstract":"The U.S. Geological Survey, in cooperation with the Ute Mountain Ute Tribe (UMUT), initiated a study in 2016 to increase understanding of the hydrogeology and chemistry of groundwater within select areas of the Ute Mountain Ute Reservation (UMUR) in Colorado and Utah, identify vulnerabilities to the system and other natural resources, and outline information needs to aid in the understanding and protection of groundwater resources. The results presented for this study can be used to support the UMUT’s goal of protecting their vital groundwater resources on the UMUR.
Hydrogeologic conditions were characterized for the surficial aquifer contained in Quaternary-age unconsolidated surficial deposits and the Dakota aquifer contained in the Cretaceous-age Dakota Sandstone. In the surficial aquifer, median depth to water ranges from about 5.4 to 17.2 feet below land surface in the Farm and Ranch Enterprise area and 11 to 34 feet below land surface in the Towaoc area, and the water table slopes generally southwest or south. A map of depth to the top of the Dakota Sandstone was constructed from existing well data. Depths range from zero in outcrop areas to more than 3,000 feet below land surface on mesas in the southeastern part of the UMUR.
Groundwater-chemistry data were collected by the UMUT from 13 springs and 31 wells from 1996 through 2017. Specific conductance was much lower for samples from springs than from wells; median values were 512 and 6,024 microsiemens per centimeter at 25 degrees Celsius, respectively. Spring samples were well oxygenated. A few well samples were anoxic (dissolved oxygen concentrations less than 0.5 milligrams per liter [mg/L]), indicating reducing conditions in the aquifer. About 75 percent of spring samples had fresh water (total dissolved solids concentrations less than 1,000 mg/L), and about 85 percent of well samples had brackish or highly saline water (total dissolved solids concentrations greater than 1,000 mg/L). Water type for springs on the Ute Mountains was calcium bicarbonate. Lower-altitude springs had a calcium-sulfate water type. Most well samples had sodium as the dominant cation, and sulfate, bicarbonate, and chloride as the dominant anions. Fluoride concentrations in about 45 percent of well samples were greater than an agricultural-use standard of 2 mg/L.
Nitrate plus nitrite concentrations in most spring and well samples were less than about 1.6 mg/L per liter. Concentrations in samples from wells in the irrigated agricultural area were elevated; the maximum concentration was 78.5 mg/L. About one-half of the trace-element samples had concentrations that were less than laboratory reporting limits. Only aluminum, arsenic, and selenium in spring samples, and boron and selenium in well samples, were detected at concentrations greater than surface-water standards or water-quality standards for agricultural use of groundwater.
Only three organic compounds, the pesticides alachlor and atrazine and the volatile organic compound di(2-ethylhexyl) phthalate, were detected in well samples. The Escherichia coli bacteria was detected in 47 and 23 percent of samples from wells and springs, respectively. The E. coli detections included samples from three culturally significant springs, which did not meet the UMUT cultural-use standard of total absence of E. coli.
Tritium and carbon-14 were the primary environmental tracers used for interpreting groundwater ages for Lopez 2 Spring and five wells (AP–1, 5000 Block, Cottonwood Spring, Goodknight, and SE Toe). Water from the AP–1 well contained a mixture of pre- and post-1950s recharge. Tritium and carbon-14 recharge ages for Lopez 2 Spring (post-1950s in age), Goodknight and SE Toe wells (pre-1950s in age), and Cottonwood Spring well (primarily pre-1950s in age) are supported by helium-4 data. The helium-4 data for the 5000 Block well are inconsistent with the tritium and carbon-14 age of pre-1950s recharge because of interference caused by high methane concentrations in the water.
Springs and surficial deposits are more vulnerable to contamination from anthropogenic chemicals than deeper bedrock wells. Bedrock aquifers are vulnerable in areas where the geologic formations containing the aquifers are exposed at the land surface. Groundwater in deep bedrock aquifers is likely thousands of years old and is not currently affected by present-day land uses. Both shallow and deep groundwater are vulnerable to naturally occurring salts and minerals, such as of total dissolved solids, major ions, nitrate, and trace elements.
Effects of a changing climate on water resources and other ecological characteristics of the UMUR could include changes in evapotranspiration, a decrease in snowpack, decreased aquifer recharge and flow of springs, a decrease in soil moisture, and increased occurrence of wildfires and forest mortality. Of particular interest for the UMUT are possible effects of a changing climate on medicinal and culturally important plants and springs
Several information needs were identified during this study that would aid in the understanding and protection of groundwater resources on the UMUR. These include well-completion information for bedrock wells, the collection of environmental tracer data at additional wells, the addition of methane and hydrocarbon analysis to well sampling plans, and the resampling of springs and wells that were last sampled in 2002 or earlier.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20195122","collaboration":"Prepared in cooperation with the Ute Mountain Ute Tribe","usgsCitation":"Bauch, N.J., and Arnold, L.R., 2020, Hydrogeologic characterization, groundwater chemistry, and vulnerability assessment, Ute Mountain Ute Reservation, Colorado and Utah: U.S. Geological Survey Scientific Investigations Report 2019–5122, 76 p., https://doi.org/10.3133/sir20195122.","productDescription":"Report: ix, 76 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-095027","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":399604,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109676.htm"},{"id":372110,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S4MOB6","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial datasets for estimating depth to the top of the Dakota Sandstone, Ute Mountain Ute Reservation, Colorado, 2017"},{"id":372108,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5122/coverthb.jpg"},{"id":372109,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5122/sir20195122.pdf","text":"Report","size":"8.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5122"}],"country":"United States","state":"Colorado","otherGeospatial":"Ute Mountain Ute Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.0333,\n 37\n ],\n [\n -108.2667,\n 37\n ],\n [\n -108.2667,\n 37.3564\n ],\n [\n -109.0333,\n 37.3564\n ],\n [\n -109.0333,\n 37\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
The Near and InterMediate Range Order Diffractometer (NIMROD) was used to examine the potential impact of shale mineralogy on CO2 behavior within micropores. Two samples with varying mineral compositions were obtained from producing intervals in the dry gas window in the Middle Devonian Marcellus Shale. One of the samples contained relatively high amounts of quartz and clay and low carbonate, the other contained relatively equal amounts of quartz, carbonate, and clay. The samples were probed with CO2 at subcritical pressures (20–50 bar) and temperature (22 °C) and characterized over a neutron scattering vector (Q) range of 0.02 < Q < 50 Å–1. This Q range provides information from the atomistic length-scale up to pore radii of 10 nm. Mineralogy variations between the samples did not affect scattering ratios over the entire Q range accessible with the NIMROD. Q values for the minimum scattering ratios of both samples at similar pressures are remarkably similar, particularly for Q < ∼0.09 Å–1, and maximum scattering ratios are similar in both samples suggesting that mineral pores are so uncommon in the pore sizes examined that they cannot be resolved due to the overwhelming amounts of organic pores in these samples. Overall, these findings suggest that mineralogical variations have little effect on CO2 behavior within organic matter-hosted shale micropores at high thermal maturities and they lend support to the assertion that CO2 cannot be stored in the vast surface areas of micropores in organic material in shale formations. In addition, CO2 enhanced oil recovery (EOR) is unlikely to displace petroleum from some of the smaller mesopores (2.5 to ∼3.5 nm) and all of the micropores because they are effectively closed to CO2.
amples were probed with CO2 at subcritical pressures (20 50 bar) and temperature (22 oC) and characterized over a neutron scattering vector (Q) range of 0.02 < Q < 50 -1. This Q range provides information on nominal pore size radii of around 10 0.5 nm. Mineralogy variations between the samples did not affect scattering ratios over the entire Q range accessible with the NIMROD. Q values for the minimum scattering ratios of both samples at similar pressures are statistically indistinguishable and maximum scattering ratios are similar in both samples suggesting that mineral pores are either absent or are so uncommon that they cannot be resolved due to the overwhelming amounts of organic pores in these samples. Overall, these findings suggest that mineralogical variations have little effect on CO2 behavior within organic matter-hosted shale micropores at high thermal maturities and they lend support to the assertion that CO2 cannot be stored in the vast surface areas of micropores (<2.5 nm) in shale formations. In addition, CO2 enhanced oil recovery (EOR) is unlikely to displace petroleum from some of the smaller mesopores (2.5 10 nm) and all of the micropores because they are effectively closed to CO2.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.energyfuels.9b03744","usgsCitation":"Ruppert, L., Jubb, A., Headen, T.F., Youngs, T.G., and Bandli, B., 2020, Impacts of mineralogical variation on CO2 behavior in small pores from producing intervals of the Marcellus Shale: Results from neutron scattering: Energy & Fuels, v. 34, no. 3, p. 2765-2771, https://doi.org/10.1021/acs.energyfuels.9b03744.","productDescription":"7 p.","startPage":"2765","endPage":"2771","ipdsId":"IP-112608","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":379024,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruppert, Leslie F. 0000-0002-7453-1061","orcid":"https://orcid.org/0000-0002-7453-1061","contributorId":242600,"corporation":false,"usgs":true,"family":"Ruppert","given":"Leslie F.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":800461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":800462,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Headen, Thomas F 0000-0003-0095-5731","orcid":"https://orcid.org/0000-0003-0095-5731","contributorId":242601,"corporation":false,"usgs":false,"family":"Headen","given":"Thomas","email":"","middleInitial":"F","affiliations":[],"preferred":false,"id":800463,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Youngs, Tristan G. A.","contributorId":202502,"corporation":false,"usgs":false,"family":"Youngs","given":"Tristan","email":"","middleInitial":"G. A.","affiliations":[{"id":36465,"text":"Disordered Materials Group (ISIS), STFC Rutherford Appleton Laboratory, U.K.","active":true,"usgs":false}],"preferred":false,"id":800464,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bandli, Bryan","contributorId":242602,"corporation":false,"usgs":false,"family":"Bandli","given":"Bryan","email":"","affiliations":[],"preferred":false,"id":800465,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211572,"text":"70211572 - 2020 - Quantifying 40 years of rockfall activity in Yosemite Valley with historical Structure-from-Motion photogrammetry and terrestrial laser scanning","interactions":[],"lastModifiedDate":"2020-07-31T14:56:04.113807","indexId":"70211572","displayToPublicDate":"2020-02-10T09:42:39","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying 40 years of rockfall activity in Yosemite Valley with historical Structure-from-Motion photogrammetry and terrestrial laser scanning","docAbstract":"Rockfalls and rockslides are often dominant geomorphic processes in steep bedrock landscapes, but documenting their occurrence can be challenging, requiring frequent monitoring and well resolved spatial data. Repeat application of remote sensing methods such as Terrestrial Laser Scanning (TLS) and Structure-from-Motion (SfM) photogrammetry can detect even very small rockfalls, but typically these acquisitions span only years and may not record rockfall activity representative of longer-term rates of cliff erosion. Inventory databases can extend rockfall records, but are commonly incomplete and prone to observation bias. We employed TLS and SfM on two adjacent cliffs (El Capitan and Middle Brother) in Yosemite Valley, integrating semi-annual data collections from 2010 to 2017 with “historical” (archival) SfM models derived from oblique photographs taken in 1976. Comparing the 1976 SfM models against more recent data allows for more accurate and precise rockfall detection and volume measurement over a 40-year period. Change detection indicates that 235 rockfalls occurred from the two cliffs, more than twice as many events as are recorded in Yosemite's inventory database. Although individual rockfall volumes reported in the inventory database vary from those measured by SfM-TLS, reported cumulative volumes are similar to measured volumes, likely because the large-volume events that account for most of the cumulative volume tend to be widely observed and well-documented. Volume-frequency relationships indicate that the cliffs erode predominantly by less frequent, larger-volume rockfalls, at rates of 0.9 to 1.7 mm/yr. Our study demonstrates how integrated SfM and TLS measurements, especially utilizing SfM models derived from historical imagery, allow detection and quantification of rockfalls spanning several decades, complementing and improving inventory databases, informing rockfall hazard assessment, and providing longer-term rates of cliff erosion.
Generating public interest in fish and their biology is often challenging. Many aquatic species are cryptic and largely invisible to the public. Therefore, increasing public awareness of cryptic fishes and elevating their visibility to broad audiences requires innovation. Inexpensive technological advancements now provide fisheries biologists, managers, and researchers with means never before possible for documenting fish in their natural habitat via underwater videography. We investigated cost efficient and simple methods for capturing and creating high quality, high definition, and informative underwater videos that could be used by people with little or no previous experience in videography. We tested 1) a variety of filming equipment including cameras and camera recording settings, lenses, batteries, and memory cards; 2) active and passive camera deployment techniques; and 3) a variety of free and paid postproduction software and compared them for ease of use, expense, and quality of output. Highest quality footage, i.e., highest resolution, clearest, and most stable, was obtained using a GoPro action camera deployed underwater in a stationary position mounted to a metal base plate using a combination of stock and macro lenses, and filming in 4K resolution at 30 frames per second. Final production videos were created using Adobe Premiere Pro.
Winter drawdowns in flood control reservoirs create expansive mudflats that lack the vegetation typical of littoral zones, which reduces the amount of structure available for fish habitat. This study investigated the feasibility of establishing agricultural plantings as a management action to ameliorate mudflats by providing structural cover following reservoir refilling. We tested cool-season annual grasses and clovers applied in several mixed and monoculture treatments that were sown on the mudflats of Enid Reservoir, Mississippi, during the winter drawdown in three consecutive years. Soil samples were taken for analysis of pH and macronutrients prior to planting. Plantings were monitored until the following spring to evaluate effectiveness of establishment through ground coverage, height, and stem density sampling. Plots were assigned a seeding treatment of either grasses (ryegrass Lolium spp. or triticale x Triticosecale sp.), clovers (balansa clover Trifolium michelianum or berseem clover Trifolium alexandrinum), or both (mixed plantings) or left as an unseeded control. Differences among plant treatments were assessed via repeated measures analysis of variance and differences among means evaluated with Tukey's honestly significant difference test. Soil productivity within the study area was poor all 3 years. Grasses germinated both when disked into the soil and when top sown, while clover only germinated when disked. Plots seeded with grasses performed better than control plots with respect to stem density, height, and ground coverage, while plots seeded with grass and clover mixtures performed better than control plots only with respect to height, and plots seeded with only clover did not perform significantly better than control plots. Results serve as an evaluation of the efficacy of agricultural plant establishment on the mudflats of a flood control reservoir, inform the direction of future research, and identify considerations regarding the application of agricultural plantings as a management tool to create fish habitat.
Pinnipeds are commonly monitored using aerial photographic surveys at land- or ice-based sites, where animals come ashore for resting, pupping, molting, and to avoid predators. Although these counts form the basis for monitoring population change over time, they do not provide information regarding where animals occur in the water, which is often of management and conservation interest. In this study, we developed a hierarchical model that links counts of pinnipeds at terrestrial sites to sightings-at-sea and estimates abundance, spatial distribution, and the proportion of time spent on land (attendance probability). The structure of the model also allows for the inclusion of predictors that may explain variation in ecological and observation processes. We applied the model to Steller sea lions (Eumetopias jubatus) in Glacier Bay, Alaska using counts of sea lions from aerial photographic surveys and opportunistic in-water sightings from vessel surveys. Glacier Bay provided an ideal test and application of the model because data are available on attendance probability based on long-term monitoring. We found that occurrence in the water was positively related to proximity to terrestrial sites, as would be expected for a species that engages in central-place foraging. The proportion of sea lions in attendance at terrestrial sites and overall abundance estimates were consistent with reports from the literature and monitoring programs. The model we describe has benefit and utility for park managers who wish to better understand the overlap between pinnipeds and visitors, and the framework that we present has potential for application across a variety of study systems and taxa.
Submerged aquatic vegetation (SAV) thrives across the estuarine salinity gradient providing valuable ecosystem services. Within the saline portion of estuaries, seagrass areas are frequently cited as hotspots for their role in capturing and retaining organic carbon (Corg). Non-seagrass SAV, located in the fresh to brackish estuarine areas, may also retain significant soil Corg, yet their role remains unquantified. Given rapidly occurring landscape and salinity changes due to human and natural disturbances, landscape level carbon pool estimates from estuarine SAV habitat blue carbon estimates are needed. We assessed Corg stocks in SAV habitat soils from estuarine freshwater to saline habitats (interior deltaic) to saline barrier islands (Chandeleur Island) within the Mississippi River Delta Plain (MRDP), Louisiana, USA. SAV habitats contain Corg stocks equivalent to those reported for other estuarine vegetation types (seagrass, salt marsh, mangrove). Interior deltaic SAV Corg stocks (231.6 ± 19.5 Mg Corg ha−1) were similar across the salinity gradient, and significantly higher than at barrier island sites (56.6 ± 10.4 Mg Corg ha−1). Within the MRDP, shallow water SAV habitat covers up to an estimated 28,000 ha, indicating that soil Corg storage is potentially 6.4 ± 0.1 Tg representing an unaccounted Corg pool. Extrapolated across Louisiana, and the Gulf of Mexico, this represents a major unaccounted pool of soil Corg. As marshes continue to erode, the ability of coastal SAV habitat to offset some of the lost carbon sequestration may be valuable. Our estimates of Corg sequestration rates indicated that conversion of eroding marsh to potential SAV habitat may help to offset the reduction of Corg sequestration rates. Across Louisiana, we estimated SAV to offset this loss by as much as 79,000 Mg C yr−1 between the 1960s and 2000s.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.137217","usgsCitation":"Hillman, E.R., Rivera-Monroy, V., Nyman, A.J., and La Peyre, M., 2020, Estuarine submerged aquatic vegetation habitat provides organic carbon storage across a shifting landscape: Science of the Total Environment, v. 717, 137217, 12 p., https://doi.org/10.1016/j.scitotenv.2020.137217.","productDescription":"137217, 12 p.","ipdsId":"IP-090252","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457780,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://repository.lsu.edu/agrnr_pubs/603","text":"Publisher Index Page"},{"id":395067,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.6431884765625,\n 29.776297851831366\n ],\n [\n -88.81072998046875,\n 30.21398171687066\n ],\n [\n -89.56878662109375,\n 30.161751648356894\n ],\n [\n -89.86541748046875,\n 30.401306519203583\n ],\n [\n -90.318603515625,\n 30.557530797259172\n ],\n [\n -91.01074218749999,\n 30.57408532473883\n ],\n [\n -91.15631103515625,\n 30.28990324883237\n ],\n [\n -91.834716796875,\n 29.935895213372444\n ],\n [\n -91.878662109375,\n 29.76437737516313\n ],\n [\n -91.285400390625,\n 29.008140362978157\n ],\n [\n -90.428466796875,\n 28.738763971370293\n ],\n [\n -89.05517578125,\n 28.9120147012556\n ],\n [\n -88.6431884765625,\n 29.776297851831366\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"717","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hillman, E. R.","contributorId":264718,"corporation":false,"usgs":false,"family":"Hillman","given":"E.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":831996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rivera-Monroy, V. H.","contributorId":272502,"corporation":false,"usgs":false,"family":"Rivera-Monroy","given":"V. H.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":831997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nyman, A. J.","contributorId":265337,"corporation":false,"usgs":false,"family":"Nyman","given":"A.","email":"","middleInitial":"J.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":831998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":831999,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208829,"text":"70208829 - 2020 - Genes in space: What Mojave desert tortoise genetics can tell us about landscape connectivity","interactions":[],"lastModifiedDate":"2020-04-06T23:15:41.220637","indexId":"70208829","displayToPublicDate":"2020-02-08T08:46:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Genes in space: What Mojave desert tortoise genetics can tell us about landscape connectivity","docAbstract":"Habitat loss and fragmentation in the Mojave Desert have been increasing, which can create barriers to movement and gene flow leading to decreased populations of native species. Disturbance and degradation of Mojave desert tortoise habitat includes linear features (e.g. highways, railways, and a network of dirt roads), urbanized areas, and their associated infrastructure, mining activities, energy distribution systems, and most recently, utility-scale solar facilities. To evaluate the spatial genetic structure of tortoises in an area experiencing rapid habitat loss, we conducted field surveys from 2015-2017 and genotyped 299 tortoises at 20 microsatellite loci within and around Ivanpah Valley along the California/Nevada border. We used a Bayesian clustering analysis to examine population genetic structure across valley and mountain pass habitat. Spatial principal components analysis was included to further investigate population genetic structure with isolation-by-distance. To explicitly incorporate landscape features (e.g. habitat and anthropogenic linear barriers) we used maximum likelihood population effects. We assessed recent gene flow on the landscape through maximum likelihood pedigree analyses of relatedness. We detected three to four genetic clusters with high levels of admixture that generally corresponded to three valleys separated by mountain ranges, and a genetically distinguishable population in one mountain pass. Pedigree analyses showed second order relationships up to 60 km apart suggesting a greater range of interactions and inter-relatedness between individuals than previously suspected. Our results support historical gene flow with isolation-by-resistance, and reveal a genetic signal indicative of reduction in genetic connectivity across two parallel linear features (a railway and a highway). This work demonstrates the value of protecting connected tracts of functional habitat and the importance of connectivity research in conservation.","language":"English","publisher":"Springer","doi":"10.1007/s10592-020-01251-z","usgsCitation":"Dutcher, K.E., Vandergast, A.G., Esque, T., Mitelberg, A., Matocq, M.D., Heaton, J.S., and Nussear, K., 2020, Genes in space: What Mojave desert tortoise genetics can tell us about landscape connectivity: Conservation Genetics, v. 21, p. 289-303, https://doi.org/10.1007/s10592-020-01251-z.","productDescription":"15 p.","startPage":"289","endPage":"303","ipdsId":"IP-113961","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":437120,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90LIQRI","text":"USGS data release","linkHelpText":"Microsatellite genotypes for desert tortoise (Gopherus agassizii) in Ivanpah Valley (2015-2017)"},{"id":372836,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada ","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.3946533203125,\n 33.65578083204094\n ],\n [\n -114.70275878906249,\n 33.280027811732154\n ],\n [\n -114.40612792968749,\n 35.14686290675633\n ],\n [\n -115.77941894531249,\n 35.92464453144099\n ],\n [\n -116.70227050781249,\n 35.420391545750746\n ],\n [\n -117.32299804687499,\n 34.985003130171066\n ],\n [\n -116.83959960937499,\n 34.347971491244955\n ],\n [\n -116.3946533203125,\n 33.65578083204094\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Dutcher, Kirsten E.","contributorId":221063,"corporation":false,"usgs":false,"family":"Dutcher","given":"Kirsten","email":"","middleInitial":"E.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":783516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergast, Amy G. 0000-0002-7835-6571 avandergast@usgs.gov","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":3963,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"avandergast@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783515,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mitelberg, Anna amitelberg@usgs.gov","contributorId":173293,"corporation":false,"usgs":true,"family":"Mitelberg","given":"Anna","email":"amitelberg@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Matocq, Marjorie D","contributorId":222917,"corporation":false,"usgs":false,"family":"Matocq","given":"Marjorie","email":"","middleInitial":"D","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":783519,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heaton, Jill S.","contributorId":175155,"corporation":false,"usgs":false,"family":"Heaton","given":"Jill","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":783520,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nussear, Ken E","contributorId":221816,"corporation":false,"usgs":false,"family":"Nussear","given":"Ken E","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":783521,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70208267,"text":"sim3446 - 2020 - Seismicity of the Earth 1900–2018","interactions":[{"subject":{"id":98510,"text":"sim3064 - 2010 - Seismicity of the Earth 1900-2007","indexId":"sim3064","publicationYear":"2010","noYear":false,"title":"Seismicity of the Earth 1900-2007"},"predicate":"SUPERSEDED_BY","object":{"id":70208267,"text":"sim3446 - 2020 - Seismicity of the Earth 1900–2018","indexId":"sim3446","publicationYear":"2020","noYear":false,"title":"Seismicity of the Earth 1900–2018"},"id":1}],"lastModifiedDate":"2022-04-22T19:52:50.634837","indexId":"sim3446","displayToPublicDate":"2020-02-07T13:55:21","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":"3446","displayTitle":"Seismicity of the Earth 1900–2018","title":"Seismicity of the Earth 1900–2018","docAbstract":"This map illustrates 119 years of global seismicity in the context of global plate tectonics and the Earth’s physiography. Primarily designed for use by earth scientists, engineers, and educators, this map provides a comprehensive overview of strong (magnitude [M] 5.5 and larger) earthquakes since 1900. The map clearly identifies the locations of the “great” earthquakes (M 8.0 and larger) and the aftershock or rupture area (green fill), if known, of the M 8.3 or larger earthquakes. The circular earthquake symbols are scaled to be proportional to the moment magnitude and therefore to the area of faulting, thus providing a better understanding of the relative sizes and distribution of earthquakes in the magnitude range 5.5 to 9.5. Plotting the known rupture or aftershock areas (which are closely related) of the largest earthquakes also provides a better appreciation of the faulting extent of some of the most famous and damaging instrumentally recorded earthquakes in modern history.
","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3446","usgsCitation":"Hayes, G.P., Smoczyk, G.M., Villaseñor, A.H., Furlong, K.P., and Benz, H.M, 2020, Seismicity of the Earth 1900–2018: U.S. Geological Survey Scientific Investigations Map 3446, scale 1:22,500,000, https://doi.org/10.3133/sim3446. [Supersedes USGS Scientific Investigations Map 3064.]","productDescription":"1 Sheet: 73.25 x 44.75 inches","onlineOnly":"N","ipdsId":"IP-111771","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":371836,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3446/coverthb2.jpg"},{"id":399519,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109666.htm"},{"id":371837,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3446/sim3446.pdf","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3446"}],"scale":"22500000","contact":"Center Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, Mail Stop 966
Denver, CO 80225
Intermittent rivers are becoming more ecologically stressed worldwide. Flow cessation occurs naturally and spatiotemporally in these systems and anthropogenic activities such as wastewater discharges can have considerable impacts. Public entities mostly monitor water quality in permanent streams, leading to insufficient monitoring of intermittent streams and consequently to their potentially inadequate management.. This study analyzed spatiotemporal patterns of water quality and associated ecological risk through the quantification of physicochemical and microbiological pollutants in the intermittent river system of El Novillo and San Marcos in Northeast Mexico. Results showed that water quality varied geographically and seasonally. Based on national and international criteria, annual averages of water quality parameters analyzed suggested that streamflow in these river systems is of poor quality and poses high ecological risk to aquatic life. In the urban area, annual mean concentrations of Cd and Pb (0.14 and 0.4 mg/L) were 77- and 10-fold higher than their respective water quality criteria (<0.0018 and 0.04 mg/L). Statistically significant (q < 0.05) correlations were identified in concentrations of cyanide, Cd, Cu and Pb between wastewater seeping into the river and streamflow within the urban area. These observations highlight the unique sensitivity of intermittent urban streams to anthropogenic activities and may provide useful information to enhance current water management plans for the El Novillo-San Marcos River system for the protection of ecosystem integrity and human health.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.enmm.2020.100369","usgsCitation":"Lopez, E., Patino, R., Vazquez-Sauceda, M.L., Perez-Castaneda, R., Arellano-Mendez, L.U., Ventura-Houle, R., and Heyer, L., 2020, Water quality and ecological risk assessment of intermittent streamflow through mining and urban areas of San Marcos River sub-basin, Mexico: Environmental Nanotechnology, Monitoring & Management, v. 14, 100369, 9 p., https://doi.org/10.1016/j.enmm.2020.100369.","productDescription":"100369, 9 p.","ipdsId":"IP-109161","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","otherGeospatial":"El Novillo Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.37109375,\n 23.644524198573688\n ],\n [\n -97.998046875,\n 23.644524198573688\n ],\n [\n -97.998046875,\n 25.16517336866393\n ],\n [\n -100.37109375,\n 25.16517336866393\n ],\n [\n -100.37109375,\n 23.644524198573688\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lopez, Elisenda","contributorId":274748,"corporation":false,"usgs":false,"family":"Lopez","given":"Elisenda","email":"","affiliations":[{"id":56648,"text":"Universidad Autónoma de Tamaulipas","active":true,"usgs":false}],"preferred":false,"id":833279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833280,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vazquez-Sauceda, Maria L.","contributorId":274749,"corporation":false,"usgs":false,"family":"Vazquez-Sauceda","given":"Maria","email":"","middleInitial":"L.","affiliations":[{"id":56648,"text":"Universidad Autónoma de Tamaulipas","active":true,"usgs":false}],"preferred":false,"id":833281,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perez-Castaneda, Roberto","contributorId":274750,"corporation":false,"usgs":false,"family":"Perez-Castaneda","given":"Roberto","email":"","affiliations":[{"id":56648,"text":"Universidad Autónoma de Tamaulipas","active":true,"usgs":false}],"preferred":false,"id":833282,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arellano-Mendez, Leonardo U.","contributorId":274751,"corporation":false,"usgs":false,"family":"Arellano-Mendez","given":"Leonardo","email":"","middleInitial":"U.","affiliations":[{"id":56648,"text":"Universidad Autónoma de Tamaulipas","active":true,"usgs":false}],"preferred":false,"id":833283,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ventura-Houle, Rene","contributorId":274752,"corporation":false,"usgs":false,"family":"Ventura-Houle","given":"Rene","email":"","affiliations":[{"id":56648,"text":"Universidad Autónoma de Tamaulipas","active":true,"usgs":false}],"preferred":false,"id":833284,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heyer, Lorenzo","contributorId":274753,"corporation":false,"usgs":false,"family":"Heyer","given":"Lorenzo","email":"","affiliations":[{"id":56648,"text":"Universidad Autónoma de Tamaulipas","active":true,"usgs":false}],"preferred":false,"id":833285,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211204,"text":"70211204 - 2020 - Blind testing of shoreline evolution models","interactions":[],"lastModifiedDate":"2020-07-17T17:46:53.600536","indexId":"70211204","displayToPublicDate":"2020-02-07T12:41:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Blind testing of shoreline evolution models","docAbstract":"Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999–2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014–2017), both approaches showed a decrease in models’ capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-020-59018-y","usgsCitation":"Jennifer Montaño, Coco, G., Antolinez, J., Beuzen, T., Bryan, K.R., Cagigal, L., Bruno Castelle, Davidson, M., Goldstein, E.B., Ibaceta, R., Déborah Idier, Ludka, B.C., Masoud-Ansari, S., Fernando Mendez, A. Brad Murray, Plant, N.G., Ratlif, K., Robinet, A., Ana Rueda, Nadia Sénéchal, Simmons, J., Splinter, K., Scott Stephens, Townend, I., Vitousek, S., and Vos, K., 2020, Blind testing of shoreline evolution models: Scientific Reports, v. 10, 2137, 10 p., https://doi.org/10.1038/s41598-020-59018-y.","productDescription":"2137, 10 p.","ipdsId":"IP-116455","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":457790,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-59018-y","text":"Publisher Index Page"},{"id":376470,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2020-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Jennifer Montaño","contributorId":229413,"corporation":false,"usgs":false,"family":"Jennifer Montaño","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":793154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coco, Giovanni","contributorId":229414,"corporation":false,"usgs":false,"family":"Coco","given":"Giovanni","email":"","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":793155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Antolinez, Jose","contributorId":229415,"corporation":false,"usgs":false,"family":"Antolinez","given":"Jose","email":"","affiliations":[{"id":41638,"text":"University of Cantabria","active":true,"usgs":false}],"preferred":false,"id":793156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beuzen, Tomas 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Auckland","active":true,"usgs":false}],"preferred":false,"id":793159,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bruno Castelle","contributorId":229419,"corporation":false,"usgs":false,"family":"Bruno Castelle","affiliations":[{"id":41639,"text":"University of Bordeaux","active":true,"usgs":false}],"preferred":false,"id":793160,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Davidson, Mark","contributorId":229420,"corporation":false,"usgs":false,"family":"Davidson","given":"Mark","email":"","affiliations":[{"id":7119,"text":"Plymouth University","active":true,"usgs":false}],"preferred":false,"id":793161,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Goldstein, Evan B. 0000-0001-9358-1016","orcid":"https://orcid.org/0000-0001-9358-1016","contributorId":184210,"corporation":false,"usgs":false,"family":"Goldstein","given":"Evan","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":793162,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ibaceta, Raimundo 0000-0003-2203-7640","orcid":"https://orcid.org/0000-0003-2203-7640","contributorId":229421,"corporation":false,"usgs":false,"family":"Ibaceta","given":"Raimundo","email":"","affiliations":[{"id":27304,"text":"University of New South Wales","active":true,"usgs":false}],"preferred":false,"id":793163,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Déborah Idier","contributorId":229422,"corporation":false,"usgs":false,"family":"Déborah Idier","affiliations":[{"id":41640,"text":"BGRM","active":true,"usgs":false}],"preferred":false,"id":793164,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ludka, Bonnie C. 0000-0002-3844-2280","orcid":"https://orcid.org/0000-0002-3844-2280","contributorId":229423,"corporation":false,"usgs":false,"family":"Ludka","given":"Bonnie","email":"","middleInitial":"C.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":793165,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Masoud-Ansari, Sina","contributorId":229424,"corporation":false,"usgs":false,"family":"Masoud-Ansari","given":"Sina","email":"","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":793166,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Fernando Mendez","contributorId":229425,"corporation":false,"usgs":false,"family":"Fernando Mendez","affiliations":[{"id":41638,"text":"University of Cantabria","active":true,"usgs":false}],"preferred":false,"id":793167,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"A. 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For bounded outcome variables restricted to the\nunit interval, however, classical modeling approaches based on mean squared error\nloss may severely suer as they do not account for heteroscedasticity in the data.\nTo address this issue, we propose a random forest approach for relating a beta dis-\ntributed outcome to a set of explanatory variables. Our approach explicitly makes\nuse of the likelihood function of the beta distribution for the selection of splits dur-\ning the tree-building procedure. In each iteration of the tree-building algorithm it\nchooses one explanatory variable in combination with a split point that maximizes\nthe log-likelihood function of the beta distribution with the parameter estimates de-\nrived from the nodes of the currently built tree. Results of several simulation studies\nand an application using data from the U.S.A. National Lakes Assessment Survey\ndemonstrate the properties and usefulness of the method, in particular when com-\npared to random forest approaches based on mean squared error loss and parametric\nregression models.","language":"English","publisher":"Taylor and Francis","doi":"10.1080/10618600.2019.1705310","usgsCitation":"Weinhold, L., Schmid, M., Mitchell, R., Maloney, K.O., Wright, M.N., and Berger, M., 2020, A random forest approach for bounded outcome variables: Journal of Computational and Graphical Statistics, v. 29, no. 3, p. 639-658, https://doi.org/10.1080/10618600.2019.1705310.","productDescription":"20 p.","startPage":"639","endPage":"658","ipdsId":"IP-107449","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":457792,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8193767","text":"External Repository"},{"id":376906,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Weinhold, Leonie","contributorId":236854,"corporation":false,"usgs":false,"family":"Weinhold","given":"Leonie","email":"","affiliations":[{"id":47552,"text":"University of Bonn, Germany","active":true,"usgs":false}],"preferred":false,"id":794489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmid, Matthias","contributorId":236855,"corporation":false,"usgs":false,"family":"Schmid","given":"Matthias","affiliations":[{"id":47552,"text":"University of Bonn, Germany","active":true,"usgs":false}],"preferred":false,"id":794490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitchell, Richard M.","contributorId":215406,"corporation":false,"usgs":false,"family":"Mitchell","given":"Richard M.","affiliations":[{"id":39239,"text":"USEPA, Washington D.C.","active":true,"usgs":false}],"preferred":false,"id":794491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794492,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Marvin N.","contributorId":236856,"corporation":false,"usgs":false,"family":"Wright","given":"Marvin","email":"","middleInitial":"N.","affiliations":[{"id":47553,"text":"Leibniz Institute for Prevention Research and Epidemiology, Germany","active":true,"usgs":false}],"preferred":false,"id":794493,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Berger, Moritz","contributorId":236857,"corporation":false,"usgs":false,"family":"Berger","given":"Moritz","email":"","affiliations":[{"id":47552,"text":"University of Bonn, Germany","active":true,"usgs":false}],"preferred":false,"id":794494,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228165,"text":"70228165 - 2020 - The influence of groundwater on the population size and total length of warmwater stream fishes","interactions":[],"lastModifiedDate":"2022-02-07T17:12:22.863141","indexId":"70228165","displayToPublicDate":"2020-02-07T10:51:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3444,"text":"Southeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"The influence of groundwater on the population size and total length of warmwater stream fishes","docAbstract":"Groundwater influences stream environments in numerous ways including structuring biotic assemblages. However, associations between groundwater influence and warmwater fish assemblages are under-studied. We examined relationships between groundwater contribution, population size, and total length (TL) for 5 warmwater fishes at 32 stream reaches in the Ozark Highlands ecoregion. When we controlled for distance from an impoundment, population size and TL were significantly related to groundwater influence for all 5 species. Sunfishes were significantly less abundant in reaches with high levels of groundwater contribution (HGC reaches), whereas Ambloplites rupestris (Rock Bass) and Nocomis asper (Redspot Chub) TLs were significantly greater at HGC reaches. Reach-scale groundwater contribution explained nearly 4 times more unexplained variation among fish densities than did TL. Our study provides insight into the structuring role of groundwater on warmwater fish populations.
","language":"English","publisher":"Eagle Hill Institute","doi":"10.1656/058.019.0210","usgsCitation":"Mollenhauer, R., Miller, A., Goff, J., and Brewer, S.K., 2020, The influence of groundwater on the population size and total length of warmwater stream fishes: Southeastern Naturalist, v. 19, no. 2, p. 308-324, https://doi.org/10.1656/058.019.0210.","productDescription":"17 p.","startPage":"308","endPage":"324","ipdsId":"IP-109195","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395546,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri, Oklahoma","otherGeospatial":"Ozark Highlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.61151123046875,\n 36.49418152677427\n ],\n [\n -93.79852294921875,\n 36.51405119943165\n ],\n [\n -93.85620117187499,\n 37.00035919622158\n ],\n [\n -94.61975097656249,\n 36.99816565700228\n ],\n [\n -94.888916015625,\n 37.00255267215955\n ],\n [\n -95.34484863281249,\n 36.35052700542763\n ],\n [\n -95.38330078125,\n 36.140092827322654\n ],\n [\n -95.2789306640625,\n 35.561277754384555\n ],\n [\n -94.84771728515625,\n 35.30167705397601\n ],\n [\n -94.73785400390625,\n 35.32408937278183\n ],\n [\n -94.493408203125,\n 35.576916524038616\n ],\n [\n -94.53460693359374,\n 35.99578538642032\n ],\n [\n -94.61151123046875,\n 36.49418152677427\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mollenhauer, Robert","contributorId":242899,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"Robert","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":833286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Andrew D.","contributorId":243521,"corporation":false,"usgs":false,"family":"Miller","given":"Andrew D.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":833287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goff, Josh","contributorId":243395,"corporation":false,"usgs":false,"family":"Goff","given":"Josh","email":"","affiliations":[{"id":48711,"text":"Dauphin Island Sea Lab","active":true,"usgs":false}],"preferred":false,"id":833288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":833289,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205997,"text":"pp1861 - 2020 - Geochronologic age constraints on tectonostratigraphic units of the central Virginia Piedmont, USA","interactions":[],"lastModifiedDate":"2022-04-22T19:07:33.289959","indexId":"pp1861","displayToPublicDate":"2020-02-07T10:10:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1861","displayTitle":"Geochronologic Age Constraints on Tectonostratigraphic Units of the Central Virginia Piedmont, USA","title":"Geochronologic age constraints on tectonostratigraphic units of the central Virginia Piedmont, USA","docAbstract":"New geologic mapping coupled with uranium-lead (U-Pb) zircon geochronology (sensitive high-resolution ion microprobe-reverse geometry [SHRIMP-RG] and laser ablation-inductively coupled plasma-mass spectrometry [LA-ICP-MS]) analyses of 10 samples, provides new constraints on the tectonostratigraphic framework of the central Virginia Piedmont. Detrital zircon analysis confirms that the Silurian-Devonian Quantico Formation is a postorogenic successor basin, with zircons derived primarily from Ordovician Chopawamsic Formation volcanic rocks. Detrital zircons from strata of the Long Island syncline, previously mapped as a separate successor basin, have a peri-Gondwanan component distinct from Laurentian-sourced rocks of the Potomac terrane to the west. Volcanism of the Chopawamsic Formation spanned at least 14 million years during the Ordovician. The Chopawamsic Formation contains sheet-like Late Ordovician-Silurian granodioritic and tonalitic intrusions that were once mapped as Carboniferous. Biotite-muscovite migmatitic paragneiss, which borders the Chopawamsic Formation on its southeast side and also occurs east of the Lakeside fault, preserves evidence of Silurian deformation and metamorphism, with a Carboniferous (Alleghanian) overprint. Limited SHRIMP-RG analysis of detrital zircons from this paragneiss yields a Laurentian (Mesoproterozoic) signature, which suggests that the structurally concordant contact between volcanic rocks of the Chopawamsic Formation and paragneiss is either a pre-Alleghanian fault or an unconformity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1861","usgsCitation":"Carter, M.W., McAleer, R.J., Holm-Denoma, C.S., Spears, D.B., Regan, S.P., Burton, W.C., and Evans, N.H., 2020, Geochronologic age constraints on tectonostratigraphic units of the central Virginia Piedmont, USA: U.S. Geological Survey Professional Paper 1861, 28 p., https://doi.org/10.3133/pp1861.","productDescription":"Report: vi, 28 p.; 2 Tables","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099524","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":399507,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109674.htm"},{"id":372001,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/pp/1861/pp1861_table3.xlsx","text":"Table 3","size":"447 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Isotopic data for all analyses by secondary ionization mass spectrometry on the U.S. Geological Survey/Stanford sensitive high-resolution ion microprobe-reverse geometry"},{"id":372000,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/pp/1861/pp1861_table2.xlsx","text":"Table 2","size":"98.5 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Isotopic data for all analyses by laser ablation-inductively coupled plasma-mass spectrometry at the U.S. Geological Survey Central Mineral and Environmental Resources Science Center Isotope Laboratory in Denver, Colorado"},{"id":372114,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1861/pp1861.pdf","text":"Report","size":"9.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1861"},{"id":371998,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1861/coverthb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"central Virginia Piedmont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.37,\n 37.6278\n ],\n [\n -77.5,\n 37.6278\n ],\n [\n -77.5,\n 38.3758\n ],\n [\n -78.37,\n 38.3758\n ],\n [\n -78.37,\n 37.6278\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
The Dixie Valley fault bounds the east side of the Stillwater Range in west‐central Nevada and last ruptured in 1954. Offset basalts indicate that slip began more recently than ~14 Ma, and prior work has interpreted the southern segment as an active low‐angle normal fault. Oligocene igneous rocks in the southern Stillwater Range were steeply tilted during large‐magnitude extension prior to ~14 Ma. To refine the timing of early extension and the onset of slip on the Dixie Valley fault, we collected two transects of samples for apatite fission track, apatite and zircon (U‐Th)/He (AHe and ZHe), and apatite 4He/3He thermochronometry. Apatite fission track ages from the Oligocene IXL pluton indicate rapid cooling ~18–14 Ma, and AHe and ZHe ages from the Cretaceous La Plata Canyon pluton indicate rapid cooling ~16–19 Ma. We interpret these data to record cooling during rapid extension. AHe ages from the IXL pluton are ~6–8 Ma and record cooling during slip on the Dixie Valley fault. We modeled these ages and 4He/3He spectra from one sample as the result of cooling during exhumation of a tilted fault block at a constant extension rate. The model predicts slip on the Dixie Valley fault beginning ~8 Ma. Although it does not constrain the initial fault dip, the model illustrates how a low‐angle fault requires a higher extension rate to reproduce cooling ages. Consequently, we prefer a high‐angle southern Dixie Valley fault for strain compatibility with the high‐angle northern segment.
Quantifying human impacts on the nitrogen (N) cycle and investigating natural ecosystem N cycling depend on the magnitude of inputs from natural biological nitrogen fixation (BNF). Here, we present two bottom‐up approaches to quantify tree‐based symbiotic BNF based on forest inventory data across the coterminous United States and SE Alaska. For all major N‐fixing tree genera, we quantify BNF inputs using (1) ecosystem N accretion rates (kg N ha−1 yr−1) scaled with spatial data on tree abundance and (2) percent of N derived from fixation (%Ndfa) scaled with tree N demand (from tree growth rates and stoichiometry). We estimate that trees fix 0.30–0.88 Tg N yr−1 across the study area (1.4–3.4 kg N ha−1 yr−1). Tree‐based N fixation displays distinct spatial variation that is dominated by two genera, Robinia (64% of tree‐associated BNF) and Alnus (24%). The third most important genus, Prosopis, accounted for 5%. Compared to published estimates of other N fluxes, tree‐associated BNF accounted for 0.59 Tg N yr−1, similar to asymbiotic (0.37 Tg N yr−1) and understory symbiotic BNF (0.48 Tg N yr−1), while N deposition contributed 1.68 Tg N yr−1 and rock weathering 0.37 Tg N yr−1. Overall, our results reveal previously uncharacterized spatial patterns in tree BNF that can inform large‐scale N assessments and serve as a model for improving tree‐based BNF estimates worldwide. This updated, lower BNF estimate indicates a greater ratio of anthropogenic to natural N inputs, suggesting an even greater human impact on the N cycle.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GB006241","usgsCitation":"Staccone, A., Liao, W., Perakis, S.S., Compton, J., Clark, C., and Menge, D., 2020, A spatially explicit, empirical estimate of tree-based biological nitrogen fixation in forests of the United States: Global Biogeochemical Cycles, v. 34, no. 2, e2019GB006241, 18 p., https://doi.org/10.1029/2019GB006241.","productDescription":"e2019GB006241, 18 p.","ipdsId":"IP-104007","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457798,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gb006241","text":"Publisher Index Page"},{"id":378747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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EPA","active":true,"usgs":false}],"preferred":false,"id":799602,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clark, Christopher L.","contributorId":168382,"corporation":false,"usgs":false,"family":"Clark","given":"Christopher L.","affiliations":[{"id":25276,"text":"US EPA, National Center for Envirenmental Assessment, DC","active":true,"usgs":false}],"preferred":false,"id":799603,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Menge, Duncan 0000-0003-4736-9844","orcid":"https://orcid.org/0000-0003-4736-9844","contributorId":241126,"corporation":false,"usgs":false,"family":"Menge","given":"Duncan","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":799604,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208463,"text":"70208463 - 2020 - Sensitivity of warm water fishes and rainbow trout to selected contaminants","interactions":[],"lastModifiedDate":"2020-03-11T15:27:24","indexId":"70208463","displayToPublicDate":"2020-02-07T09:08:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1103,"text":"Bulletin of Environmental Contamination and Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of warm water fishes and rainbow trout to selected contaminants","docAbstract":"Guidelines for developing water quality standards allow U.S. states to exclude toxicity data for the family Salmonidae (trout and salmon) when deriving guidelines for warm-water habitats. This practice reflects the belief that standards based on salmonid data may be overprotective of toxic effects on other fish taxa. In acute tests with six chemicals and eight fish species, the salmonid, Rainbow Trout (Oncorhynchus mykiss), was the most sensitive species tested with copper, zinc, and sulfate, but warm-water species were most sensitive to nickel, chloride, and ammonia. Overall, warm-water fishes, including sculpins (Cottidae) and sturgeons (Acipenseridae), were about as sensitive as salmonids in acute tests and in limited chronic testing with Lake Sturgeon (Acipenser fulvescens) and Mottled Sculpin (Cottus bairdi). In rankings of published acute values, invertebrate taxa were most sensitive for all six chemicals tested and there was no trend for greater sensitivity of salmonids compared to warm-water fish.
","language":"English","publisher":"Springer","doi":"10.1007/s00128-020-02788-y","usgsCitation":"Besser, J.M., Dorman, R.A., Ivey, C.D., Cleveland, D.M., and Steevens, J.A., 2020, Sensitivity of warm water fishes and rainbow trout to selected contaminants: Bulletin of Environmental Contamination and Toxicology, v. 104, p. 321-326, https://doi.org/10.1007/s00128-020-02788-y.","productDescription":"6 p.","startPage":"321","endPage":"326","ipdsId":"IP-112054","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":372219,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"104","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Besser, John M. 0000-0002-9464-2244 jbesser@usgs.gov","orcid":"https://orcid.org/0000-0002-9464-2244","contributorId":2073,"corporation":false,"usgs":true,"family":"Besser","given":"John","email":"jbesser@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":781992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dorman, Rebecca A. 0000-0002-5748-7046","orcid":"https://orcid.org/0000-0002-5748-7046","contributorId":28522,"corporation":false,"usgs":true,"family":"Dorman","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":781993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ivey, Chris D. 0000-0002-0485-7242 civey@usgs.gov","orcid":"https://orcid.org/0000-0002-0485-7242","contributorId":3308,"corporation":false,"usgs":true,"family":"Ivey","given":"Chris","email":"civey@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":781994,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cleveland, Danielle M. 0000-0003-3880-4584 dcleveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3880-4584","contributorId":187471,"corporation":false,"usgs":true,"family":"Cleveland","given":"Danielle","email":"dcleveland@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":781995,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":781996,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}