Invasive species are a major cause of biodiversity loss and are notoriously expensive and challenging to manage. We developed a decision-analytic framework for evaluating invasive species removal strategies, given objectives of maximizing eradication probability and minimizing costs. The framework uses an existing estimation model for spatially referenced removal data—one of the most accessible types of invasive species data—to obtain estimates of population growth rate, movement probability, and detection probability. We use these estimates in simulations to identify Pareto-efficient strategies—strategies where increases in eradication probability cannot be obtained without increases in cost—from a set of proposed strategies. We applied the framework post hoc to a successful eradication of veiled chameleons (Chamaeleo calyptratus) and identified the potential for substantial improvements in efficiency. Our approach provides managers and policymakers with tools to identify cost-effective strategies for a range of invasive species using only prior knowledge or data from initial physical removals.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/conl.13051","usgsCitation":"Yackel Adams, A.A., Hostetter, N.J., Link, W., and Converse, S.J., 2024, Identifying Pareto-efficient eradication strategies for invasive populations: Conservation Letters, v. 17, no. 5, e13051, 10 p., https://doi.org/10.1111/conl.13051.","productDescription":"e13051, 10 p.","ipdsId":"IP-163878","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":487603,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/conl.13051","text":"Publisher Index Page"},{"id":481449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.64418198330387,\n 21.042535468371597\n ],\n [\n -156.7100067465097,\n 20.94926990791153\n ],\n [\n -156.5767160394666,\n 20.620019168030595\n ],\n [\n -156.3095497551291,\n 20.54804441612707\n ],\n [\n -155.97692015447151,\n 20.640096885489086\n ],\n [\n -155.96802530273143,\n 20.818214275871142\n ],\n [\n -156.26695266911045,\n 20.967008230356555\n ],\n [\n -156.64418198330387,\n 21.042535468371597\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":925523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":925524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Link, William A.","contributorId":299684,"corporation":false,"usgs":false,"family":"Link","given":"William A.","affiliations":[{"id":36625,"text":"Emeritus","active":true,"usgs":false}],"preferred":false,"id":925525,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":925526,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261560,"text":"70261560 - 2024 - Volcanism and tectonics of young basaltic fields in the eastern California shear zone, California, USA","interactions":[],"lastModifiedDate":"2024-12-16T14:49:23.820529","indexId":"70261560","displayToPublicDate":"2024-09-12T08:42:26","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Volcanism and tectonics of young basaltic fields in the eastern California shear zone, California, USA","docAbstract":"Circa 12 Ma, there was a fundamental reorganization of magmatism and tectonics in the Mojave Desert, California, USA, from basaltic to rhyolitic fields associated with extensional tectonics to dispersed basaltic monogenetic fields associated with the northwest- or east-striking strike-slip faults. The broad zone of strike-slip faults associated with the San Andreas transform margin stretched east to Arizona, but the high slip-rate central part that is known as the Quaternary eastern California shear zone is the locus of the post–12 Ma volcanism. We compiled a literature review of 29 basaltic fields, conducted detailed and reconnaissance study of several fields, and conducted geochemistry on many. Most of the volcanic fields are near or cut by the long fault systems, but more importantly, in nearly two-thirds of the fields that have scoria cones, the cones are <1 km from one of these faults. Eighteen volcanic fields have geochemistry data, and of the 441 analyses of major elements, 286 are new U.S. Geological Survey data that include comprehensive trace elements. The data are used to compare fields and determine the compositional variations during the formation of each field. Comparisons between fields show that several nearby volcanic fields have similar geochemical compositions and trends compared to distant fields; we suggest that six magmatic clusters formed, some with 200–300 k.y. duration. Spatial patterns change over the 12 m.y. span, with early fields forming in the north and south and the youngest in a central area from Pisgah to Amboy fields. The volume of magma also changed, most notably with a sixfold increase ca. 4 Ma followed by a 2–3 m.y. quiescence. In addition to the fracture control provided by strike-slip faults as magma conduits, we advance arguments for shear melting in the mantle along the most active faults, with secondary controls of local tectonic interactions, to explain spatial and temporal patterns in the fields.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"From coastal geomorphology to magmatism: Guides to GSA connects 2024 field trips in southern California and beyond","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2024.0070(06)","usgsCitation":"Buesch, D.C., and Miller, D., 2024, Volcanism and tectonics of young basaltic fields in the eastern California shear zone, California, USA, chap. of From coastal geomorphology to magmatism: Guides to GSA connects 2024 field trips in southern California and beyond, v. 70, p. 125-157, https://doi.org/10.1130/2024.0070(06).","productDescription":"33 p.","startPage":"125","endPage":"157","ipdsId":"IP-165587","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":465142,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.81457812408306,\n 32.741341527582605\n ],\n [\n -114.51850207327473,\n 33.84659454462617\n ],\n [\n -114.15791235302103,\n 34.31724338526864\n ],\n [\n -114.80864946075852,\n 35.199130287438805\n ],\n [\n -117.98533883054941,\n 37.62054159081218\n ],\n [\n -121.61806378609694,\n 36.16806803153355\n ],\n [\n -120.26877074820086,\n 34.73154575007007\n ],\n [\n -117.90472675927293,\n 33.87436079530947\n ],\n [\n -117.83103647077345,\n 33.63574209904861\n ],\n [\n -116.63455828291453,\n 32.60190143996401\n ],\n [\n -114.81457812408306,\n 32.741341527582605\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"70","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Van Buer, Nicholas","contributorId":214183,"corporation":false,"usgs":false,"family":"Van Buer","given":"Nicholas","email":"","affiliations":[{"id":38988,"text":"Cal State Poly Pomona","active":true,"usgs":false}],"preferred":false,"id":921131,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Schwartz, Joshua J.","contributorId":289850,"corporation":false,"usgs":false,"family":"Schwartz","given":"Joshua","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":921132,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Buesch, David C. 0000-0002-4978-5027 dbuesch@usgs.gov","orcid":"https://orcid.org/0000-0002-4978-5027","contributorId":1154,"corporation":false,"usgs":true,"family":"Buesch","given":"David","email":"dbuesch@usgs.gov","middleInitial":"C.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":921049,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, David M. 0000-0003-3711-0441","orcid":"https://orcid.org/0000-0003-3711-0441","contributorId":238721,"corporation":false,"usgs":true,"family":"Miller","given":"David M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":921050,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70259248,"text":"70259248 - 2024 - Reexamining the Honolulu Volcanics: Hawai‘i's classic case of rejuvenation volcanism","interactions":[],"lastModifiedDate":"2024-10-03T15:55:03.375339","indexId":"70259248","displayToPublicDate":"2024-09-11T09:12:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2420,"text":"Journal of Petrology","active":true,"publicationSubtype":{"id":10}},"title":"Reexamining the Honolulu Volcanics: Hawai‘i's classic case of rejuvenation volcanism","docAbstract":"Rejuvenated volcanism is a worldwide phenomenon occurring on many oceanic islands in all of the major ocean basins. This plume-related volcanism follows the main edifice-building stage after a hiatus of variable duration (e.g. 0.6–2 Myrs in Hawai'i). The Honolulu Volcanics (HV), the classic case of rejuvenated volcanism, involved monogenetic eruptions from at least 48 vent areas. Previous studies inferred these vents were aligned along 3 to 11 rifts oriented orthogonal to the propagation direction of the Hawaiian plume. HV basalts are known for having high MgO contents (greater than 10 wt %) and upper mantle xenoliths. Thus, HV magmas are assumed to be relatively primitive and to have ascended rapidly (less than 1 day) through the crust. However, new analyses of olivine cores in basalts from 24 HV vents are mostly too low in forsterite content (74–86 mol %) to be in equilibrium with mantle melts. Olivine and clinopyroxene in HV basalts commonly show reverse zoning indicating magma mixing prior to eruption. These results are inconsistent with the rapid ascent of HV magmas directly from their mantle source. Many of the HV magmas underwent storage (probably in the lower crust or uppermost mantle), crystal fractionation and magma mixing prior to eruption. New 40Ar/39Ar dates were determined for 11 HV lavas to evaluate their eruptive history. These ages, 80 to 685 ka, combined with our previous and other 40Ar/39Ar ages for HV lavas reveal long gaps (greater than 50 kyr) between some eruptions. Our comprehensive, whole-rock major and trace element database (63 XRF analyses, 57 ICPMS analyses) of basalts from 37 vents show remarkable compositional diversity with no obvious spatial pattern or temporal trends. The two most recent eruptive sequences have the greatest diversity (basanite and melilitite compositions). HV basanites show systematic trace element trends that may reflect mixing of multiple source components. The nephelinites and melilitites require a complex source history that may have involved residual accessory minerals during mantle melting and a metasomatic component that was not carbonatitic. The new ages and geochemical data show eruptions along most of the previously proposed rift systems were unrelated (except for the Koko Rift). Therefore, geodynamic models that relate HV volcanism to these rift systems are invalid. Lava volumes for two HV eruptions were estimated at 0.11 and 0.23 km3 using surface mapping and water well data. Similar size, recent monogenetic eruptions in Auckland, New Zealand, were inferred to have lasted several months. Thus, if another HV eruption were to occur, which is possible given the long hiatus between eruptions, it would be extremely disruptive for the nearly 1 million residents of Honolulu. None of the existing geodynamic models fully explain the age duration, volumes and the locations of Hawai'i's rejuvenated volcanism. Thus, the cause of this secondary volcanism remains enigmatic.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/petrology/egae093","usgsCitation":"Garcia, M.O., Norman, M.D., Jicha, B., Lynn, K.J., and Jiang, P., 2024, Reexamining the Honolulu Volcanics: Hawai‘i's classic case of rejuvenation volcanism: Journal of Petrology, v. 65, no. 9, egae093, 24 p., https://doi.org/10.1093/petrology/egae093.","productDescription":"egae093, 24 p.","ipdsId":"IP-161257","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":498025,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/petrology/egae093","text":"Publisher Index Page"},{"id":462482,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Honolulu Volcanics, Oahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.95228727057142,\n 21.303437544076758\n ],\n [\n -157.7876854268278,\n 21.244288947214542\n ],\n [\n -157.68978498381696,\n 21.25531845437243\n ],\n [\n -157.6413726768336,\n 21.309451323199127\n ],\n [\n -157.6446001639659,\n 21.336510281621585\n ],\n [\n -157.72098402609515,\n 21.470728941249874\n ],\n [\n -157.7780029654312,\n 21.470728941249874\n ],\n [\n -157.98671379998177,\n 21.336510281621585\n ],\n [\n -157.95228727057142,\n 21.303437544076758\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"65","issue":"9","noUsgsAuthors":false,"publicationDate":"2024-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Garcia, Michael O.","contributorId":225524,"corporation":false,"usgs":false,"family":"Garcia","given":"Michael","email":"","middleInitial":"O.","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":914550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norman, Marc D.","contributorId":344700,"corporation":false,"usgs":false,"family":"Norman","given":"Marc","email":"","middleInitial":"D.","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":914551,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jicha, Brian","contributorId":213920,"corporation":false,"usgs":false,"family":"Jicha","given":"Brian","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":914552,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lynn, Kendra J. 0000-0001-7886-4376","orcid":"https://orcid.org/0000-0001-7886-4376","contributorId":290327,"corporation":false,"usgs":true,"family":"Lynn","given":"Kendra","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":914553,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jiang, Peng","contributorId":344701,"corporation":false,"usgs":false,"family":"Jiang","given":"Peng","email":"","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":914554,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70258256,"text":"sir20245075 - 2024 - Low-flow statistics computed for streamflow gages and methods for estimating selected low-flow statistics for ungaged stream locations in Ohio, water years 1975–2020","interactions":[],"lastModifiedDate":"2025-12-23T19:34:36.395091","indexId":"sir20245075","displayToPublicDate":"2024-09-11T08:55:00","publicationYear":"2024","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":"2024-5075","displayTitle":"Low-Flow Statistics Computed for Streamflow Gages and Methods for Estimating Selected Low-Flow Statistics for Ungaged Stream Locations in Ohio, Water Years 1975–2020","title":"Low-flow statistics computed for streamflow gages and methods for estimating selected low-flow statistics for ungaged stream locations in Ohio, water years 1975–2020","docAbstract":"A study was conducted by the U.S. Geological Survey, in cooperation with the Ohio Water Development Authority and the Ohio Environmental Protection Agency, to compute low-flow frequency, flow-duration, and harmonic mean flow statistics for long-term streamflow gages and to develop regression equations to estimate those statistics at unregulated, ungaged stream locations in Ohio. The flow statistics were computed with data collected after the 1974 water year because upward trends and statistically significant step changes (occurring after the late 1960s but before 1975) in annual flow statistics were detected at many candidate gages in Ohio. A total of 180 continuous-record gages in Ohio and bordering States were identified as having at least 10 years of daily flow records during the analytical period (water years 1975–2020). Also identified were five low-flow partial-record gages in Ohio that had instantaneous low flows that correlated strongly with daily streamflows at one of the continuous-record gages (also referred to as index gages). For continuous-record gages, the following flow statistics were computed: annual and seasonal minimum 1-, 7-, 30-, and 90-day flows with 2-, 5-, 10-, 20-, and 50-year recurrence intervals; annual and seasonal 98-, 95-, 90-, 85-, 80-, 75-, 70-, 60-, 50-, 40-, 30-, 20-, and 10-percent duration flows; and the harmonic mean flow. For partial-record gages, estimates were made for annual and seasonal minimum 1-, 7-, 30-, and 90-day low flows with 2-, 10-, and 20-year recurrence intervals and annual and seasonal 98-, 95-, 90-, 85-, and 80-percent duration flows.
The drainage basin of each gage was inspected for anthropogenic or karst features that could appreciably affect or regulate low flows. That inspection resulted in data from 53 of the 180 continuous-record gages and the 5 low-flow partial-record gages being categorized as “unregulated” and subsequently used in regression analyses to develop equations for estimating low-flow statistics. Two hundred and sixty potential explanatory variables were tested for this study. In most cases, a streamflow-variability index (SVI) was chosen as the sole explanatory variable for the regression analyses to predict the harmonic mean and annual and seasonal low-flow yields. The exceptions were for one of the September–November low-flow yield statistics and all the December–February yield statistics. Drainage area, decimal longitude, and usually SVI were chosen as the explanatory variables for those exceptions and to predict the 80-percent duration flows. The SVI values used in the model were estimated from a geospatial grid of SVI values developed for this study by using an empirical Bayesian kriging regression prediction. Observations for continuous-record gages used in the regression analyses were weighted as a function of their record length. Weights for partial-record gages were estimated based on the weights determined for their index gages.
Equations for low-flow yields were developed by using censored regressions with a censoring level of 0.00001 cubic foot per second per square mile. Numerical constraints were placed on the yield equations if they could compute yields less than the yield censoring level or if the yields did not monotonically decrease with increasing SVI. Logistic-regression equations were developed, with SVI and drainage area as explanatory variables, to estimate the probability that the low-flow statistics were greater than the flow censoring level (0.01 cubic foot per second).
The regression equations presented in this report were developed for implementation in the Ohio StreamStats application. The equations are applicable to unregulated streams in Ohio and are not applicable to streams with karst drainage features, diversions, regulation, or other anthropogenic activities that can appreciably affect low flow. The equations were developed by using observations with a range of SVI values from 0.41 to 1.23 log10 cubic foot per second and a range of drainage areas from 0.21 to 540 square miles. The applicability of the equations outside these ranges is not known.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245075","collaboration":"Prepared in cooperation with the Ohio Water Development Authority and the Ohio Environmental Protection Agency","usgsCitation":"VonIns, B.L., and Koltun, G.F., 2024, Low-flow statistics computed for streamflow gages and methods for estimating selected low-flow statistics for ungaged stream locations in Ohio, water years 1975–2020 (ver. 1.1, October 2024): U.S. Geological Survey Scientific Investigations Report 2024–5075, 37 p., https://doi.org/10.3133/sir20245075.","productDescription":"Report: v, 37 p.; Data Release: 2 Tables","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-155169","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":462619,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5075/coverthb2.jpg"},{"id":462944,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5075/sir20245075.pdf","text":"Report","size":"3.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5075 PDF"},{"id":497941,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117489.htm","linkFileType":{"id":5,"text":"html"}},{"id":462953,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92GD1WL","text":"USGS data release","linkHelpText":"Supporting data for low-flow statistics computed for streamflow gages and methods for estimating selected low-flow statistics for ungaged stream locations in Ohio, water years 1975–2020 (ver. 1.1, October 2024)"},{"id":462951,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5075/sir20245075_app2_table2.1.csv","text":"Appendix 2 Table 2.1","size":"4.54 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- CSV file"},{"id":462950,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5075/sir20245075_app2_table2.1.xlsx","text":"Appendix 2 Table 2.1","size":"24.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Table 2.1. 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\"}}]}","edition":"Version 1.0: September 2024; Version 1.1: October 2024","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229
Landslide susceptibility maps are fundamental tools for risk reduction, but the coarse resolution of current continental-scale models is insufficient for local application. Complex relations between topographic and environmental attributes characterizing landslide susceptibility at local scales are not transferrable across areas without landslide data. Existing maps with multiple susceptibility classifications under-represent landslide potential in moderate and gently sloping terrain. We leverage an extensive landslide database (N = 613,724), a high-resolution digital elevation model (10-m), and high-performance computing resources, to develop a new nationwide susceptibility map for the contiguous United States, Hawaii, Alaska, and Puerto Rico. We calculate four alternative linear and nonlinear thresholds of topographic slope and relief using an objective split-sample calibration. We down-sample our results to a 90-m grid to account for uncertainty in the digital elevation model and landslide position, and evaluate these thresholds' ability to differentiate areas of greater susceptibility. The less conservative nonlinear model optimally balances our priorities of capturing observed landslides (99%) while minimizing area covered by susceptible terrain (43%). Independent evaluation with four statewide landslide inventories (N = 172,367) reinforces our model selection but highlights spatially variable performance. Therefore, we propose a novel approach to susceptibility classification using the concentration of landslide-prone terrain within each down-sampled grid. While landslides are possible within any cells containing susceptible terrain, those with the highest concentration capture the majority of observed landslides. Our new map characterizes landside susceptibility more consistently than prior models; our transparent classification approach also provides flexibility for accommodating different tolerances in risk reduction measures.
The Aransas-Wood Buffalo population of Grus americana (Linnaeus, 1758; whooping cranes) migrates through the U.S. Great Plains, encountering places substantially altered by human activity. Using telemetry data from 2017 to 2022, we investigated whooping crane migration behavior around U.S. Air Force bases in Oklahoma. Our study focused on potential collision risks between whooping cranes and aircraft, a substantial concern for aviation safety. We determined that activity was greatest at Kegelman Air Force Auxiliary Airfield, near whooping crane critical habitat. On average, 61 percent of marked whooping cranes used locations west of Kegelman Air Force Auxiliary Airfield and Vance Air Force Base during autumn migration and 55 percent during spring migration, and few cranes approached within 5 kilometers of airfields. Flight characteristics revealed seasonal variations in altitude and timing; cranes flew at lower altitudes in autumn and had distinct flight patterns. Additionally, we assessed temporal aspects of migration, identifying average arrival and departure dates for spring and autumn migrations. Cranes indicated consistency in seasonal presence, which may aid in risk assessments. Our findings underscore the importance of monitoring potential interactions between whooping cranes and aircraft, particularly around whooping crane critical habitat like the Salt Plains National Wildlife Refuge in Oklahoma. Detailed summaries of migration patterns and flight behavior can be used to assist the U.S. Air Force in assessing collision risks and developing mitigation strategies. Furthermore, these summaries can provide insights for the conservation efforts of this endangered species managed by the U.S. Fish and Wildlife Service and serve as a step towards mitigating risks to aviation safety and the recovery of whooping cranes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241056","collaboration":"Prepared in cooperation with the U.S. Air Force and U.S. Fish and Wildlife Service","programNote":"Species Management Research Program","usgsCitation":"Brandt, D.A., and Pearse, A.T., 2024, Migrating whooping crane activity near U.S. Air Force bases and airfields in Oklahoma: U.S. Geological Survey Open-File Report 2024–1056, 23 p., https://doi.org/10.3133/ofr20241056.","productDescription":"Report: vi, 23 p.; 2 Data Releases","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-165140","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":433672,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1056/coverthb.jpg"},{"id":433673,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1056/ofr20241056.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1056"},{"id":433675,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1056/images/"},{"id":433676,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y8KZJ9","text":"USGS data release","linkHelpText":"Location data for whooping cranes of the Aransas-Wood Buffalo Population, 2009–2018"},{"id":433677,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P138HGIX","text":"USGS data release","linkHelpText":"Whooping crane use around Air Force Bases in Oklahoma, 2017–2022"},{"id":433674,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1056/ofr20241056.XML"},{"id":433678,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241056/full"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100.13000204850229,\n 37.12806045019728\n ],\n [\n -100.13000204850229,\n 33.690676852817916\n ],\n [\n -97.31750204850259,\n 33.690676852817916\n ],\n [\n -97.31750204850259,\n 37.12806045019728\n ],\n [\n -100.13000204850229,\n 37.12806045019728\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
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8711 37th Street Southeast
Jamestown, ND 58401
Laurentia (ancestral North America) records nearly 4 billion years of crustal evolution. Here, a newly compiled continental-scale Pb isotopic database is used to evaluate the Precambrian crustal evolution of Laurentia. Pb model ages yield a 2.7 Ga peak, a 2.5–1.8 Ga minimum and 1.8–0.9 Ga continuum. Pb model ages yield thermochronometric data and track crustal growth via arc-related magmatism and accretionary orogenesis. Model 232Th/204Pb and 238U/204Pb broadly correlate with mapped crustal domains. More homogeneous and less radiogenic 238U/204Pb and 232Th/238U after 2.7 Ga suggests a shift to more juvenile sources, loss of early isotopic reservoirs and greater crustal reworking. U and Th are fractionated from Pb in Proterozoic orogens with abundant ferroan and anorthosite–mangerite–charnockite–granite(AMCG)-suite magmatism. This fractionation suggests the removal of Pb-rich lower crust, supporting petrogenetic models involving lithospheric foundering and magmatic underplating. Lithospheric thinning and associated magmatism may have contributed to high middle Proterozoic geothermal gradients.
Precariously balanced rocks (PBRs) and other fragile geologic features have the potential to constrain the maximum intensity of earthquake ground shaking over millennia. Such constraints may be particularly useful in the eastern United States (U.S.), where few earthquake‐source faults are reliably identified, and moderate earthquakes can be felt at great distances due to low seismic attenuation. We describe five PBRs in northern New York and Vermont—a region of elevated seismic hazard associated with historical seismicity. These boulders appear to be among the most fragile PBRs in the region, based on reports from hobbyists. The PBRs are glacial erratics, best evidenced by glacial striations on bedrock pedestals. The pedestals themselves are locally high knobs, often situated on regionally high topography; this setting limits soil development and indicates that any outwash deposits were likely ephemeral. As a result, PBR ages can be reliably established by the retreat of the last continental ice sheet, ∼15–13 ka. To quantify the fragility of the PBRs, we surveyed them with ground‐based light detection and ranging and calculated geometric parameters from the point clouds, field observations, and seismic responses. Preliminary validation of the 2023 time‐independent U.S. National Seismic Hazard Model (NSHM) shows that the existence of PBRs is generally consistent with the median site‐specific hazard curves. Only the Blue Ridge Road site suggests a modest reduction in hazard. To visualize the ensemble of data, we mapped the minimum permissible distance to potential source faults around each PBR site as a function of source magnitude by using the ground‐motion models from the 2023 NSHM. Viewed in this manner, our data are consistent with potential M∼6.5 earthquake‐source faults in many parts of the Lake Champlain Valley and northern Adirondack Mountains. Our work illustrates a potential pathway for better constraining earthquake‐source faults in regions of cryptic faults.
Lead Belt, an area of major lead mining from the 1860s until 1972 where more than 8.5 million tons of lead were mined. After active mining ceased, the effects of mining activities persisted in the Big River system because of large mine waste pile erosion, and floodplain sediment and streambank contamination along several tributaries and the main stem of the Big River. Lead-contaminated streambed and floodplain sediments extend more than 90 miles from the Old Lead Belt to the confluence of the Big River with the Meramec River. The waste piles and mine-waste contaminated streambed and floodplain sediments have been sources of high concentrations of several trace elements, primarily cadmium, lead, and zinc. The U.S. Environmental Protection Agency Region 7 has made several efforts to prevent further erosion of contaminated sediments into the Big River including the capping of major mine waste piles, reclaiming sediment deposits along the floodplains, and monitoring soil conditions of croplands and residential properties.
A cooperative effort began in 2011 between the U.S. Geological Survey and the U.S. Environmental Protection Agency Region 7 to characterize suspended sediment quantity and quality in the Big River downstream from the Old Lead Belt as reclamation activities in the drainage basin progressed. The study was completed in two phases, and each phase included continuous stage, turbidity, and water temperature monitoring at the Big River below Bonne Terre, Missouri, streamgage and sampling station. Periodic suspended sediment samples also were collected manually (discrete samples) during base flow and selected stormflow events. Continuous streamflow, turbidity, and discrete suspended sediment data were used to develop regression models to compute daily suspended sediment concentrations and loads. During both phases, the discrete stormflow event samples were also evaluated to determine particle size distribution and concentrations of select trace elements. Phase one was completed from October 2011 through September 2013, and phase two, which is the primary focus of this report, was completed from October 2018 through September 2021. Phase two also included time-integrated suspended sediment samples collected using passive samplers. Discrete samples (collected during stormflow events) and passive samples were analyzed for concentrations of barium, cadmium, lead, and zinc in two sediment size fractions (when possible) to estimate trace element loads. Suspended sediment concentrations and loads and select trace element concentration results computed during phase one were compared to those computed during phase two to identify trends in the Big River Basin during the full study period.
The concentrations of cadmium, lead, and zinc in nearly all discrete stormflow event suspended sediment samples and passive suspended sediment samples exceeded the threshold effect concentrations and the probable effect concentrations, which are two sediment quality guidelines. Most samples also exceeded the toxic effect threshold, the level at which sediment is considered to be heavily contaminated and problematic for sediment-dwelling organisms. Bulk cadmium concentrations (median of 7.90 milligrams per kilogram [mg/kg]) exceeded the toxic effect threshold (3.0 mg/kg) in 17 discrete stormflow event samples, and bulk lead concentrations (median of 1,070 mg/kg) exceeded the toxic effect threshold (170 mg/kg) in all 18 discrete stormflow event samples. Bulk zinc concentrations (median of 500 mg/kg) exceeded the toxic effect threshold (540 mg/kg) in eight discrete stormflow event samples. Bulk concentrations of these trace elements in passive suspended sediment samples were slightly greater, with concentrations of cadmium (median of 14.0 mg/kg) and lead (median of 1,860 mg/kg) exceeding the toxic effect threshold in all 18 samples. Bulk concentrations of zinc (median of 733 mg/kg) exceeded the toxic effect threshold in 15 passive samples. Compared to phase one (water years 2012–13), phase two (water years 2019–21) concentrations of lead and cadmium in the fine fraction of discrete suspended sediment samples collected at Big River below Bonne Terre were statistically similar; concentrations of barium and zinc were statistically smaller in samples collected during phase two (water years 2018–21).
Sediment quality data from passive samples and daily mean suspended sediment loads from the regression model were used to calculate annual oads of barium, cadmium, lead, and zinc at the Bonne Terre streamgage. Water year 2019 had the largest loads of barium, cadmium, lead, and zinc (58.6, 1.43, 194, and 76.5 tons, respectively). The total loads of barium, cadmium, lead, and zinc for phase two (water years 2019–21) were 149, 4.00, 520, and 213 tons, respectively. Less than 5 percent of the total lead load calculated for the study period was transported when daily mean streamflow was less than 455 cubic feet per second, which is the approximate flow at which the passive samplers were inundated and began sampling. This highlights that most of the lead load is transported during stormflow events and the effectiveness of using passive samplers for ongoing monitoring of the Big River.
Annual suspended sediment loads at the Bonne Terre streamgage computed using the regression model were 113,000 tons in water year 2019, 83,400 tons in water year 2020, and 96,500 tons in water year 2021. The event-based suspended sediment loads for the eight sampled stormflow events ranged from 45.3 to 32,500 tons. Although only a portion of all stormflow events during phase two were sampled, the loads accounted for during these eight stormflow events represented approximately 30.9 percent of the total suspended sediment load calculated for the study period, confirming that a large part of suspended sediments continue to be transported in the Big River during stormflow events. Event-based loads of barium, cadmium, lead, and zinc were greatest during the stormflow events sampled in January 2020 (event 4) and March 2021 (event 8). Event-based loads calculated for event 4 for barium, cadmium, lead, and zinc were 17.1, 0.206, 27.2, and 14.5 tons, respectively. During event 8, an estimated 15.6 tons of barium, 0.239 tons of cadmium, 34.0 tons of lead, and 13.6 tons of zinc were transported in suspended sediments. The continued high concentrations of lead in suspended sediments in the Big River, despite reclamation activities, is likely because of the continual transport from streambed and stream banks of lead-enriched sediment, which remain in the system from historical mining activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245085","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Markland, K.M., and Buckley, C.E., 2024, Suspended sediment and trace element transport in the Big River downstream from the Old Lead Belt in southeastern Missouri, 2018–21: U.S. Geological Survey Scientific Investigations Report 2024–5085, 45 p., https://doi.org/10.3133/sir20245085.","productDescription":"Report: ix, 45 p.; 2 Appendixes; Data Release; Dataset","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-153957","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":433616,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1B8YS78","text":"USGS data release","linkHelpText":"Geochemical analyses of water, mine tailings, fluvial suspended sediments, fluvial bed sediments, and fluvial flood deposit sediments from the Big River and Meramec River drainage basins, Missouri"},{"id":433610,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5085/coverthb.jpg"},{"id":433611,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5085/sir20245085.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5085"},{"id":433612,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5085/sir20245085.XML"},{"id":433613,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5085/downloads/","text":"Appendixes 1–2","linkHelpText":"- Model Archives Summaries for Regression Models"},{"id":433614,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5085/images/"},{"id":499482,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117311.htm","linkFileType":{"id":5,"text":"html"}},{"id":433617,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"UGSS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":433615,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245085/full"}],"country":"United States","state":"Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.28892432173701,\n 38.3\n ],\n [\n -91.28892432173701,\n 37.29246138824874\n ],\n [\n -89.90464697798724,\n 37.29246138824874\n ],\n [\n -89.90464697798724,\n 38.3\n ],\n [\n -91.28892432173701,\n 38.3\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The North American river otter (Lontra canadensis) was extirpated from much of the United States in the early 20th century due to habitat loss, pollution of waterways, and overharvesting. The Iowa Department of Natural Resources began a river otter reintroduction effort in 1985, which placed otters in 14 sites across the state. Otters have since been known to occur in every county in Iowa and appear to have successfully repopulated their former range throughout the state. Our objective was to relate land cover characteristics and otter abundance using harvest data. We used data collected by agency staff to map the locations of otter harvest in Iowa from 2006 to 2016. We mapped otter harvest locations at the subwatershed level (also called 12-digit Hydrologic Unit Code or HUC-12). We related otter harvest to land cover variables and predicted otter abundance by land cover type. We found that roads, forests, larger waterways, and Ictaluridae (catfish) presence were negatively correlated with otter harvest. Variables positively correlated with otter harvest were areas with greater land cover diversity, wetland patch density, average stream density, and waterway and wetland areas. The land cover model predicted otters in equal or greater numbers than the harvest data in 62.8% of HUC-12s. The areas of greatest otter abundance estimates were located near recreation areas and urban areas, indicating the underutilization of these heavy-trafficked areas by trappers. Areas of fewer predicted otters were not concentrated in a single area of the state but occurred along the Interstate 80 corridor.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1543","usgsCitation":"Nixon, B., Evelsizer, V., and Klaver, R.W., 2024, Evaluating habitat use and relative abundance of Iowa's river otter with harvest data: Wildlife Society Bulletin, v. 48, no. 3, e1543, 13 p., https://doi.org/10.1002/wsb.1543.","productDescription":"e1543, 13 p.","ipdsId":"IP-155037","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481061,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1543","text":"Publisher Index Page"},{"id":480935,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"48","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Nixon, Bridget A.","contributorId":348634,"corporation":false,"usgs":false,"family":"Nixon","given":"Bridget A.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":923664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evelsizer, Vince","contributorId":348636,"corporation":false,"usgs":false,"family":"Evelsizer","given":"Vince","affiliations":[{"id":24495,"text":"Iowa Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":923666,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70258356,"text":"70258356 - 2024 - Onset and tempo of ignimbrite flare-up volcanism in the eastern and central Mogollon-Datil volcanic field, southern New Mexico, USA","interactions":[],"lastModifiedDate":"2024-10-23T16:12:57.774325","indexId":"70258356","displayToPublicDate":"2024-09-09T09:39:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Onset and tempo of ignimbrite flare-up volcanism in the eastern and central Mogollon-Datil volcanic field, southern New Mexico, USA","docAbstract":"The Cenozoic ignimbrite flare-up (40–18 Ma) generated multiple volcanic fields in the southwestern United States and northern Mexico resulting from asthenospheric mantle upwelling after removal of the Farallon slab. The correlation of tuffs to one another and to source calderas within these volcanic fields is essential for determining spatiotemporal patterns in volcanism and magma geochemistry, which have been used to deduce migration of the Farallon slab at depth and associated mantle melting. However, the correlation of Eocene–Oligocene tuffs in the southwestern U.S. is difficult because of post-emplacement erosion and faulting. This study focuses on spatiotemporal patterns of the initial episode of ignimbrite flare-up activity (ca. 36.5–33.8 Ma) in the Mogollon-Datil volcanic field in south-central New Mexico, USA. We show that alkali feldspar major and trace element geochemistry is an effective tool for correlating tuffs when combined with high-precision, single-crystal 40Ar/39Ar geochronology and bulk-rock geochemistry. Using these data, we correlate several tuff units and differentiate other tuffs that have the same eruption age but very different geochemistry, and we conclude that there was a broadly northwestward migration in volcanism over time. The new tuff correlations are used to investigate spatiotemporal variations in magma geochemistry, erupted volumes, and recurrence intervals during the initial episode of Mogollon-Datil volcanic field volcanism. Early-erupted tuffs restricted to the eastern Mogollon-Datil volcanic field share similarities with western U.S. topaz rhyolites, which suggests that the silicic magmas were generated by partial melting of mafic lower crustal rocks. We also find differences in the compositions, crystallinities, and mineral assemblages between the early- and late-erupted tuffs. The early-erupted tuffs tend to have single-feldspar mineralogies, lower feldspar Or contents, large negative Eu anomalies, and low-whole–rock Ba concentrations. Conversely, late-erupted tuffs have two feldspar plus quartz assemblages, lesser Eu anomalies, higher whole-rock Ba concentrations, and feldspars have higher Or contents. Thus, we suggest that for some of the early eruptions, after magmas underwent crystal fractionation in the crust, the silicic melt largely separated from the crystalline mush prior to eruption, whereas late-erupted tuff magmas underwent crystal fractionation at near the eutectic minimum and were remobilized and erupted with a larger proportion of their crystalline mush. Using our new ages, correlations, and previously published data, we find that the initial phase of Mogollon-Datil volcanic field volcanism produced at least 15 eruptions between 36.5 Ma and 33.8 Ma, with a minimum total erupted volume of ~1350 km3 and an average recurrence interval of 170 k.y. However, eruptions were generally smaller in volume (most <15 km3) than in other coeval fields, and most eruptions (n = 11) occurred in the first 1.2 m.y. (ca. 36.5–35.3 Ma) of activity. Altogether, our work sheds new light on variations in the composition, timing, and migration of volcanism during the initial phase of Mogollon-Datil volcanic field activity and highlights the utility of feldspar geochemistry in both “fingerprinting” tuffs and elucidating magma evolution.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02698.1","usgsCitation":"Vermillion, K.B., Johnson, E.R., Amato, J.M., Heizler, M.T., and Lente, J., 2024, Onset and tempo of ignimbrite flare-up volcanism in the eastern and central Mogollon-Datil volcanic field, southern New Mexico, USA: Geosphere, v. 20, no. 5, p. 1364-1389, https://doi.org/10.1130/GES02698.1.","productDescription":"26 p.","startPage":"1364","endPage":"1389","ipdsId":"IP-155176","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":439171,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02698.1","text":"Publisher Index Page"},{"id":433721,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"eastern and central Mogollon-Datil volcanic field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.333,\n 33.333\n ],\n [\n -108.333,\n 32.333\n ],\n [\n -106.333,\n 32.333\n ],\n [\n -106.333,\n 33.333\n ],\n [\n -108.333,\n 33.333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Vermillion, Karissa B.","contributorId":344167,"corporation":false,"usgs":false,"family":"Vermillion","given":"Karissa","email":"","middleInitial":"B.","affiliations":[{"id":36391,"text":"University of Houston","active":true,"usgs":false}],"preferred":false,"id":913028,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Emily Renee 0000-0002-7967-6913","orcid":"https://orcid.org/0000-0002-7967-6913","contributorId":269628,"corporation":false,"usgs":true,"family":"Johnson","given":"Emily","email":"","middleInitial":"Renee","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":913029,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Amato, Jeffrey M.","contributorId":247883,"corporation":false,"usgs":false,"family":"Amato","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[{"id":49682,"text":"Dept of Geolgical Sciences, New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":913030,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heizler, Matthew T.","contributorId":184261,"corporation":false,"usgs":false,"family":"Heizler","given":"Matthew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":913031,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lente, Jenna","contributorId":344168,"corporation":false,"usgs":false,"family":"Lente","given":"Jenna","email":"","affiliations":[{"id":82311,"text":"Waste Isolation Pilot Plant","active":true,"usgs":false}],"preferred":false,"id":913032,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70258621,"text":"70258621 - 2024 - Mantle melting in regions of thick continental lithosphere: Examples from Late Cretaceous and younger volcanic rocks, Southern Rocky Mountains, Colorado (USA)","interactions":[],"lastModifiedDate":"2024-10-07T16:37:46.120127","indexId":"70258621","displayToPublicDate":"2024-09-09T07:03:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Mantle melting in regions of thick continental lithosphere: Examples from Late Cretaceous and younger volcanic rocks, Southern Rocky Mountains, Colorado (USA)","docAbstract":"Major- and trace-element data together with Nd and Sr isotopic compositions and 40Ar/39Ar age determinations were obtained for Late Cretaceous and younger volcanic rocks from north-central Colorado, USA, in the Southern Rocky Mountains to assess the sources of mantle-derived melts in a region underlain by thick (≥150 km) continental lithosphere. Trachybasalt to trachyandesite lava flows and volcanic cobbles of the Upper Cretaceous Windy Gap Volcanic Member of the Middle Park Formation have low εNd(t) values from −3.4 to −13, 87Sr/86Sr(t) from ~0.705 to ~0.707, high large ion lithophile element/high field strength element ratios, and low Ta/Th (≤0.2) values. These characteristics are consistent with the production of mafic melts during the Late Cretaceous to early Cenozoic Laramide orogeny through flux melting of asthenosphere above shallowly subducting and dehydrating oceanic lithosphere of the Farallon plate, followed by the interaction of these melts with preexisting, low εNd(t), continental lithospheric mantle during ascent. This scenario requires that asthenospheric melting occurred beneath continental lithosphere as thick as 200 km, in accordance with mantle xenoliths entrained in localized Devonian-age kimberlites. Such depths are consistent with the abundances of heavy rare earth elements (Yb, Sc) in the Laramide volcanic rocks, which require parental melts derived from garnet-bearing mantle source rocks. New 40Ar/39Ar ages from the Rabbit Ears and Elkhead Mountains volcanic fields confirm that mafic magmatism was reestablished in this region ca. 28 Ma after a hiatus of over 30 m.y. and that the locus of volcanism migrated to the west through time. These rocks have εNd(t) and 87Sr/86Sr(t) values equivalent to their older counterparts (−3.5 to −13 and 0.7038–0.7060, respectively), but they have higher average chondrite-normalized La/Yb values (~22 vs. ~10), and, for the Rabbit Ears volcanic field, higher and more variable Ta/Th values (0.29–0.43). The latter are general characteristics of all other post– 40 Ma volcanic rocks in north-central Colorado for which literature data are available. Transitions from low to intermediate Ta/Th mafic volcanism occurred diachronously across southwest North America and are interpreted to have been a consequence of melting of continental lithospheric mantle previously metasomatized by aqueous fluids derived from the underthrusted Farallon plate. Melting occurred as remnants of the Farallon plate were removed and the continental lithospheric mantle was conductively heated by upwelling asthenosphere. A similar model can be applied to post–40 Ma magmatism in north-central Colorado, with periodic, east to west, removal of stranded remnants of the Farallon plate from the base of the continental lithospheric mantle accounting for the production, and western migration, of volcanism. The estimated depth of the lithosphere-asthenosphere boundary in north-central Colorado (~150 km) indicates that the lithosphere remains too thick to allow widespread melting of upwelling asthenosphere even after lithospheric thinning in the Cenozoic. The preservation of thick continental lithospheric mantle may account for the absence of oceanic-island basalt–like basaltic volcanism (high Ta/Th values of ~1 and εNd[t] > 0), in contrast to areas of southwest North America that experienced larger-magnitude extension and lithosphere thinning, where oceanic-island basalt–like late Cenozoic basalts are common.
Migratory shorebirds are one of the fastest declining groups of North American avifauna. Yet, relatively little is known about how these species select habitat during migration. We explored the habitat selection of Buff-breasted Sandpipers (Calidris subruficollis) during spring and fall migration through the Texas Coastal Plain, a major stopover region for this species. Using tracking data from 118 birds compiled over 4 years, we found Buff-breasted Sandpipers selected intensively managed crops such as sod and short-stature crop fields, but generally avoided rangeland and areas near trees and shrubs. This work supports prior studies that also indicate the importance of short-stature vegetation for this species. Use of sod and corn varied by season, with birds preferring sod in spring, and avoiding corn when it is tall, but selecting for corn in fall after harvest. This dependence on cropland in the Texas Coastal Plain is contrary to habitat use observed in other parts of their non-breeding range, where rangelands are used extensively. The species' almost complete reliance on a highly specialized crop, sod, at this critical stopover site raises concerns about potential exposure to contaminants as well as questions about whether current management practices are providing suitable conditions for migratory grassland birds.
Research efforts focusing on salmonid populations have highlighted the need to better understand demographic parameters for the fry and parr life stages. Monitoring these small fish presents a challenge because negative effects from handling and tagging can bias subsequent parameter estimates. Removal models and associated sampling designs represent one class of mark-recapture models with potential to be applied to very small juvenile salmon, yet existing methods associated with removal studies are not well-suited for all study environments. For example, populations residing in large storage reservoirs may yield low capture probabilities when subjected to removal sampling, making unbiased estimation of survival using traditional removal models difficult. To address this limitation, we developed a sampling design and associated model using parentage-based tagging in hatchery-raised juvenile Chinook salmon (Oncorhynchus tshawytscha) to estimate survival over a 2-year study period in a large storage reservoir in western Oregon, USA. Individual fish were identified to family groups, serving as replicate batch marks in a robust design removal model framework. Results from a simulation suggested that parameter estimates were unbiased even at very low capture probabilities, although the use of model constraints (i.e., covariates or constant parameter values) was necessary to achieve this. Model fitting to field data supported a trend in survival over time, with survival increasing with time since release in the first study year but decreasing in the second.
In the summer and fall of 2023, the Gulf of Mexico Deepwater Hydrate Coring Expedition (UT-GOM2-2) drilled, cored, made downhole measurements, and analyzed samples from the seafloor to the base of the gas hydrate stability zone in one location (Site H, WR313) in the Terrebonne basin, deepwater Gulf of Mexico.
Analyses of data and samples from the expedition will inform biological, geochemical, and geomechanical models to constrain the role of gas hydrates in the carbon cycle and the potential for gas hydrates as an energy resource. Pressure and conventional cores were collected continuously to a depth of 155.1 meters below the seafloor (mbsf). At deeper depths, cores were taken periodically from hydrate-bearing sands and their bounding muds to a total depth of 861.3 mbsf. 162.6 m of conventional core and 54.8 m of pressure core were obtained.
Twelve temperature measurements were made between 27.1 and 144.5 mbsf to determine the geothermal gradient. At the seafloor, more than 4 m of sandy silt of unknown origin was encountered. Beneath this sand, to a depth of ~200 mbsf, the section was composed of interbedded mud and biogenic carbonate ooze. The biogenic ooze correlated to low density and high porosity intervals observed in the previously acquired logging while drilling (LWD) data and as measured. Calcareous nannofossil biostratigraphy constrains the entire record to the Pleistocene (< 0.91 million years) with a pronounced increase in sedimentation rate with depth. Beneath 200 mbsf, the section was predominantly composed of mud with two thicker, hydrate-bearing coarse-grained intervals, which are commonly known as the Blue and Orange sands.
The dissolved gas concentration was quantified from pressure cores. In the shallow section, dissolved methane concentration increased below the sulfate-methane transition zone (SMTZ) and reaches saturation (the limit of solubility for methane) at 147 mbsf. Gas expansion was very common in conventional and depressurized pressure (conventionalized) cores below the SMTZ.
At deeper depths, the methane concentration within muds bounding the Blue and Orange reservoirs was generally found to be less than saturation. The dissolved and hydrate gas composition is consistent with a microbial source, containing greater than 99.99% methane and only trace concentrations of ethane, propane, and butane. The methane to ethane ratio (C1/C2) and the methane to ethane plus propane (C1/(C2+C3)) decrease with depth down to at least 678 mbsf, mainly driven by the increase in ethane with depth. It is unclear if this trend continues through the Orange sand interval. The δ13C isotopic signature of methane ranges between -69.9 and -78.5 ‰ Vienna Pee Dee Belemnite (VPDB).
Pressure core recovery of all sandy intervals was poor. However, pressure core logs of the Orange sand show intervals of low density and high velocity, which are indicative of high hydrate saturation. One core from within the Orange sand was composed of interbedded graded sandy silt and mud. The sandy silts from this core are composed of mainly quartz and feldspar with some lithics. Most of the recovered pressure core samples are maintained at near in-situ pressure and temperature (within the hydrate stability field) at the University of Texas Pressure Core Center awaiting analysis.
In the shallow section, samples will be used to determine the flux of organic carbon through the basin system, find the rate at which that carbon was consumed, and understand the microbial population responsible for these processes. In the deeper section, samples from in and around the hydrate reservoirs will be used to determine the petrophysical properties of the reservoir and bounding seals in these systems.
","language":"English","publisher":"U.S. Department of Energy","doi":"10.2172/2439982","usgsCitation":"Flemings, P.B., Thomas, C., Phillips, S.C., Collett, T., Cook, A.E., Solomon, E.S., Colwell, F.S., Johnson, J.E., Awwiller, D., Aylward, I., Bhandari, A., Brooks, D., Cardona, A., Casso, M., Coyte, R., Darrah, T., Davis, M., Dugan, B., Duncan, D., Germaine, J.T., Holland, M., Houghton, J., Mills, N.T., Mimitz, M., Minarich, D., Morono, Y., Murphy, Z., O’Connell, J., Petrou, E., Pettigrew, T., Pohlman, J., Portnov, A., Purkey Phillips, M., Redd, T., Sawyer, D.E., Schultheiss, P., Shannon, K., Sullivan, C., Small, C., Tozier, K., Tsang, M., Van Der Maal, C., Waite, W., and Walton, T., 2024, Terrebonne Basin northern Gulf of Mexico, 30 July-28 September 2023: Preliminary Report, 95 p., https://doi.org/10.2172/2439982.","productDescription":"95 p.","ipdsId":"IP-167042","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science 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all mission areas in the U.S. Geological Survey (USGS). Recognizing the importance of climate change to its future research agenda, the USGS’s Climate Science Steering Committee requested the development of a Climate Science Plan to identify future research directions. Subject matter experts from across the Bureau formed the USGS Climate Science Plan Writing Team, which convened in September 2022 to identify and outline the major climate science topics of future concern and develop an integrated approach to conducting climate science in support of the USGS and U.S. Department of the Interior missions.
The resulting USGS Climate Science Plan identifies three major priorities under which USGS climate science proceeds: (1) characterize climate change and associated impacts, (2) assess climate change risks and develop approaches to mitigate climate change, and (3) provide climate science tools and support. The Climate Science Plan identifies 12 specific goals to achieve the outcomes of the three priorities.
To achieve these goals, the USGS Climate Science Plan also outlines climate science guidelines—key elements for conducting climate-based research—as well as emerging opportunities to support successful climate science. The USGS Climate Science Plan provided in this circular will guide future research priorities and science-support investments, as well as continued development of the climate workforce for decades to come, ensuring that the USGS continues to serve as one of the Nation’s leading climate science agencies.
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12201 Sunrise Valley Drive
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Reston, VA 20192
Email: casc@usgs.gov
Stable isotope data have made pivotal contributions to nearly every discipline of the physical and natural sciences. As the generation and application of stable isotope data continues to grow exponentially, so does the need for a unifying data repository to improve accessibility and promote collaborative engagement. This paper provides an overview of the design, development, and implementation of IsoBank, a community-driven initiative to create an open-access repository for stable isotope data implemented online in 2021. A central goal of IsoBank is to provide a web-accessible database supporting interdisciplinary stable isotope research and educational opportunities. To achieve this goal, we convened a multi-disciplinary group of over 40 analytical experts, stable isotope researchers, database managers, and web developers to collaboratively design the database. This paper outlines the main features of IsoBank and provides a focused description of the core metadata structure. We present plans for future database and tool development and engagement across the scientific community. These efforts will help facilitate interdisciplinary collaboration among the many users of stable isotopic data while also offering useful data resources and standardization of metadata reporting across eco-geoinformatics landscapes.
Objective
In many Great Plains rivers, functional turnover—the change in proportional dominance of members in biological communities that fill certain ecological roles—has occurred due to impoundment and habitat alteration. The Powder River of Montana and Wyoming remains one of the few unregulated prairie rivers, but long-term monitoring is limited, so we analyzed changes over time at the functional, assemblage, and species levels.
Methods
We used fish sampling data from 43 different sources collected from 1893 to 2022 to analyze trends in fish communities.
Result
Across the main-stem Powder River, Sand Shiner Miniellus stramineus and Channel Catfish Ictalurus punctatus substantially increased in abundance, whereas Sturgeon Chub Macrhybopsis gelida decreased. While most other species did not show significant changes in relative abundance (although the always rare Lake Chub Couesius plumbeus may have been extirpated), significant functional turnover occurred in the upper river due to increases in generalist feeders, predators, omnivores, and cavity-guarding species, with declines in benthic feeders, invertivores, and pelagic broadcast spawners, among others. Community and functional changes were more substantial in the upper river than in the lower river, possibly due to augmented streamflow from a major tributary.
Conclusion
Functional turnover within the upper river was substantial despite the relative stability of most individual species, even when the Sand Shiner—the most significantly increasing species—was excluded from analysis. This suggests small but consistent increases and decreases within functional groups, which cumulatively are likely impacting the ecosystem. We hypothesize a complex set of mechanisms causing these changes that offer avenues for future work. The collation of data from disparate studies and the resampling of even a limited number of historical fish collection locations can greatly aid in identifying potential fish community changes in systems where monitoring is limited.
","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10479","usgsCitation":"Clancy, N., McFarland, J., Ahern, M., and Walters, A.W., 2024, Functional turnover in a prairie-river fish community over 130 years: Transactions of the American Fisheries Society, v. 153, no. 5, p. 525-540, https://doi.org/10.1002/tafs.10479.","productDescription":"16 p.","startPage":"525","endPage":"540","ipdsId":"IP-160072","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":498246,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10479","text":"Publisher Index Page"},{"id":485715,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","otherGeospatial":"Powder River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.47539172686184,\n 46.90930172134972\n ],\n [\n -107.47539172686184,\n 43.10755581226584\n ],\n [\n -105.58412934297272,\n 43.10755581226584\n ],\n [\n -105.58412934297272,\n 46.90930172134972\n ],\n [\n -107.47539172686184,\n 46.90930172134972\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"153","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-08-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Clancy, Niall G.","contributorId":354901,"corporation":false,"usgs":false,"family":"Clancy","given":"Niall G.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":936635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McFarland, Jonathan A.","contributorId":354902,"corporation":false,"usgs":false,"family":"McFarland","given":"Jonathan A.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":936636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ahern, Megan G.","contributorId":354904,"corporation":false,"usgs":false,"family":"Ahern","given":"Megan G.","affiliations":[{"id":37636,"text":"Salish Kootenai College","active":true,"usgs":false}],"preferred":false,"id":936637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":936638,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256082,"text":"70256082 - 2024 - Science target prioritization framework for remote sensing","interactions":[],"lastModifiedDate":"2026-03-27T18:42:29.813683","indexId":"70256082","displayToPublicDate":"2024-09-05T13:37:35","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Science target prioritization framework for remote sensing","docAbstract":"Behind the scenes of a remote sensing mission there are complex decision making and planning operations. Streamlining these operations, with a quantitative scientific value framework, aids efficient and optimized science data collection. While there have been previous efforts to quantify the science value for specific science scenarios, our work aims to develop a general framework which can be applied across different scenarios. We describe a pipeline of processes which combines model forecast and observation data, in computational forms, as dictated by the mission objectives set forth by subject matter experts. The framework is described with use cases involving the monitoring of nitrogen dioxide (NO2) concentrations over the Gulf of Mexico and methane concentrations over interior Alaska.
","conferenceTitle":"2024 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"July 7-12, 2024","conferenceLocation":"Athens, Greece","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS53475.2024.10642436","usgsCitation":"Ravindra, V., Caldwell, D., Chandarana Saephan, M., Duncan, B., Strode, S., Swartz, W., Manies, K.L., Frank, J., Levinson, R., and Turkov, E., 2024, Science target prioritization framework for remote sensing, 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, July 7-12, 2024, p. 689-693, https://doi.org/10.1109/IGARSS53475.2024.10642436.","productDescription":"5 p.","startPage":"689","endPage":"693","ipdsId":"IP-166703","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":501742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ravindra, Vinay","contributorId":340220,"corporation":false,"usgs":false,"family":"Ravindra","given":"Vinay","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":906631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caldwell, Douglas","contributorId":340222,"corporation":false,"usgs":false,"family":"Caldwell","given":"Douglas","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":906632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chandarana Saephan, Meghan","contributorId":340224,"corporation":false,"usgs":false,"family":"Chandarana Saephan","given":"Meghan","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":906633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duncan, Bryan","contributorId":340226,"corporation":false,"usgs":false,"family":"Duncan","given":"Bryan","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":906634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Strode, Sarah A.","contributorId":151018,"corporation":false,"usgs":false,"family":"Strode","given":"Sarah A.","affiliations":[{"id":17844,"text":"University of Washington, Seattle, Washington, USA","active":true,"usgs":false}],"preferred":false,"id":906635,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Swartz, William","contributorId":340228,"corporation":false,"usgs":false,"family":"Swartz","given":"William","affiliations":[{"id":81510,"text":"John Hopkins University Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":906636,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Manies, Kristen L. 0000-0003-4941-9657 kmanies@usgs.gov","orcid":"https://orcid.org/0000-0003-4941-9657","contributorId":2136,"corporation":false,"usgs":true,"family":"Manies","given":"Kristen","email":"kmanies@usgs.gov","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":906637,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frank, Jeremy","contributorId":340229,"corporation":false,"usgs":false,"family":"Frank","given":"Jeremy","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":906638,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Levinson, Richard","contributorId":340230,"corporation":false,"usgs":false,"family":"Levinson","given":"Richard","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":906639,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Turkov, Eugene","contributorId":340231,"corporation":false,"usgs":false,"family":"Turkov","given":"Eugene","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":906640,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70257164,"text":"sir20245057 - 2024 - Chloride concentrations in groundwater from the western part of the Southern Hills regional aquifer system, Louisiana, 2021–22","interactions":[],"lastModifiedDate":"2026-02-03T19:45:44.629229","indexId":"sir20245057","displayToPublicDate":"2024-09-05T11:54:10","publicationYear":"2024","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":"2024-5057","displayTitle":"Chloride Concentrations in Groundwater From the Western Part of the Southern Hills Regional Aquifer System, Louisiana, 2021–22","title":"Chloride concentrations in groundwater from the western part of the Southern Hills regional aquifer system, Louisiana, 2021–22","docAbstract":"Groundwater is heavily used for public supply and industrial uses in the Baton Rouge, Louisiana, area. Lowered water levels resulting from groundwater withdrawals have induced the movement of saltwater towards wells in East Baton Rouge and West Baton Rouge Parishes. Saltwater intrusion has the potential to affect water supply infrastructure, reduce water availability for some uses, and increase treatment costs. To document current conditions, samples were collected from 161 wells screened in 10 aquifers of the Southern Hills regional aquifer system during November 2021 through February 2022. The results were compared with historical data to identify where chloride concentrations are increasing, which could indicate that saltwater intrusion is occurring. Saltwater intrusion, to varying degrees and areal extents, was observed in most of the 10 aquifers. The limited availability of monitoring wells near or within some of the known saltwater plume areas restricts tracking of the movement or delineation of the plumes’ current extents.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245057","issn":"2328-0328","collaboration":"Prepared in cooperation with the Capital Area Groundwater Conservation Commission","usgsCitation":"Lindaman, M.A., 2024, Chloride concentrations in groundwater from the western part of the Southern Hills regional aquifer system, Louisiana, 2021–22: U.S. Geological Survey Scientific Investigations Report 2024–5057, 33 p., https://doi.org/10.3133/sir20245057.","productDescription":"Report: viii, 33 p.; 2 Data Releases","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-144771","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":499479,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117310.htm","linkFileType":{"id":5,"text":"html"}},{"id":432497,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS NWIS database","linkHelpText":"USGS water data for the Nation"},{"id":432496,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A9OBT6","text":"USGS Data Release","linkHelpText":"Chloride concentration data for the western part of the Southern Hills regional aquifer system, Louisiana, 2021–22, and selected historical data"},{"id":432495,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5057/images"},{"id":432494,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245057/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5057 HTML"},{"id":432493,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5057/sir20245057.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5057 XML"},{"id":432492,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5057/sir20245057.pdf","size":"3.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5057"},{"id":432491,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5057/coverthb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.79916041640998,\n 31.001662882587297\n ],\n [\n -91.79916041640998,\n 29.990391322259512\n ],\n [\n -90.56239851210685,\n 29.990391322259512\n ],\n [\n -90.56239851210685,\n 31.001662882587297\n ],\n [\n -91.79916041640998,\n 31.001662882587297\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"This publication describes the U.S. Geological Survey Volcano Science Center (VSC) Response Plan for Significant Volcanic Events (hereinafter referred to as “the plan”) that has been developed for U.S volcano observatories over the past several years in consultation with the lead scientist, or Scientist-in-Charge (SIC), of each of the five U.S. Geological Survey (USGS) volcano observatories. The goal of the plan is to define a standardized management system that ensures the VSC can achieve the following during a volcanic crisis:
The plan addresses situations in which the scale of a response at least temporarily eclipses the response capabilities of a single observatory. The plan features two integrated response structures for managing and carrying out operations within the VSC during a crisis: the Observatory Volcanic Event Response Team (OVERT) and the Center Volcanic Event Response Team (CVERT). The design of these structures reflects lessons learned from past volcanic responses and is influenced by the Incident Command System used by the U.S. Federal Government for managing emergency responses. The plan clarifies expectations regarding the flow of information during a response, summarizes required tasks of the responding observatory and VSC to ensure a successful response, defines response-team roles and responsibilities, and describes the internal communication practices critical for an effective and coordinated response.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1518","usgsCitation":"Moran, S.C., Neal, C.A., and Murray, T.L., 2024, The U.S. Geological Survey Volcano Science Center’s Response Plan for Significant Volcanic Events: U.S. Geological Survey Circular 1518, 65 p., https://doi.org/10.3133/cir1518.","productDescription":"v, 65 p.","numberOfPages":"65","onlineOnly":"N","ipdsId":"IP-143031","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":433419,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1518/circ1518.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":433418,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1518/covrthb.jpg"}],"country":"Commonwealth of the Northern Mariana Islands, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.11713420288632,\n 61.002142023255914\n ],\n [\n -149.98540899170055,\n 63.04244518393395\n ],\n [\n -155.50779067376675,\n 61.49623318483353\n ],\n [\n -163.75816043675115,\n 56.14684556104572\n ],\n [\n -181.00143715585634,\n 52.48269604532007\n ],\n [\n -179.43766066935802,\n 49.56517123226905\n ],\n [\n -161.45364658708968,\n 53.202707628643395\n ],\n [\n -150.28015443637352,\n 56.59536969182122\n ],\n [\n -147.11713420288632,\n 61.002142023255914\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.04098501277775,\n 18.310791613598795\n ],\n [\n -154.27974725488383,\n 19.999127121481195\n ],\n [\n -156.13858195798778,\n 21.6642146784459\n ],\n [\n -160.5190347101758,\n 22.913848569868435\n ],\n [\n -160.89532419064514,\n 21.555359696802086\n ],\n [\n -155.04098501277775,\n 18.310791613598795\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.13805709963371,\n 32.80221941212619\n ],\n [\n -114.68347246965284,\n 32.75317958295028\n ],\n [\n -109.91889194502293,\n 31.908789770860224\n ],\n [\n -103.41968841512762,\n 32.15190215555489\n ],\n [\n -104.81753636115526,\n 44.92966193095208\n ],\n [\n -110.83268320595475,\n 45.05113861610195\n ],\n [\n -112.79654476340819,\n 44.84983806584674\n ],\n [\n -116.08121402894493,\n 49.009671497489734\n ],\n [\n -123.2819295472598,\n 48.98733781992226\n ],\n [\n -122.97762463880268,\n 48.07800384577902\n ],\n [\n -124.77196722296571,\n 48.35892442461622\n ],\n [\n -124.02527687101941,\n 46.40252508317741\n ],\n [\n -124.43207521869675,\n 42.518614030953046\n ],\n [\n -123.96606882815117,\n 40.245733159142105\n ],\n [\n -121.02446730458165,\n 35.789849150485125\n ],\n [\n -120.3490991587176,\n 34.53564829828612\n ],\n [\n -117.13805709963371,\n 32.80221941212619\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 145.87594127125067,\n 15.351997445662661\n ],\n [\n 145.47515460820574,\n 15.351997445662661\n ],\n [\n 145.47515460820574,\n 14.851891090064058\n ],\n [\n 145.87594127125067,\n 14.851891090064058\n ],\n [\n 145.87594127125067,\n 15.351997445662661\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Volcano Science Center
Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA, 98683
The landscape theory of food web architecture (LTFWA) describes relationships among body size, trophic position, mobility, and energy channels that serve to couple heterogenous habitats, which in turn promotes long-term system stability. However, empirical tests of the LTFWA are rare and support differs among terrestrial, freshwater, and marine systems. Further, it is unclear whether the theory applies in highly altered ecosystems dominated by introduced species such as the Laurentian Great Lakes. Here, we provide an empirical test of the LTFWA by relating body size, trophic position, and the coupling of different energy channels using stable isotope data from species throughout the Lake Michigan food web. We found that body size was positively related to trophic position, but for a given trophic position, organisms predominately supported by pelagic energy had smaller body sizes than organisms predominately supported by nearshore benthic energy. We also found a hump-shaped trophic relationship in the food web where there is a gradual increase in the coupling of pelagic and nearshore energy channels with larger body sizes as well as higher trophic positions. This highlights the important role of body size and connectivity among habitats in structuring food webs. However, important deviations from expectations are suggestive of how species introductions and other anthropogenic impacts can affect food web structure in large lakes. First, native top predators appear to be flexible couplers that may provide food web resilience, whereas introduced top predators may confer less stability when they specialize on a single energy pathway. Second, some smaller bodied prey fish and invertebrates, in addition to mobile predators, coupled energy from pelagic and nearshore energy channels, which suggests that some prey species may also be important integrators of energy pathways in the system. We conclude that patterns predicted by the LTFWA are present in the face of species introductions and other anthropogenic stressors to a degree, but time-series evaluations are needed to fully understand the mechanisms that promote stability.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.4413","usgsCitation":"Maitland, B.M., Bootsma, H.A., Bronte, C.R., Bunnell, D., Feiner, Z.S., Fenske, K., Fetzer, W., Foley, C., Gerig, B., Happell, A., Hook, T.O., Keppeler, F.W., Kornis, M., Lepak, R., McNaught, A., Roth, B., Turschak, B., Hoffman, J.C., and Jensen, O.P., 2024, Testing food web theory in a large lake: The role of body size in habitat coupling in Lake Michigan: Ecology, v. 105, e4413, 18 p., https://doi.org/10.1002/ecy.4413.","productDescription":"e4413, 18 p.","ipdsId":"IP-158860","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":433548,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":439174,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.4413","text":"Publisher Index Page"}],"country":"United 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the U.S. Geological Survey’s (USGS) 3D Elevation Program (3DEP) is to facilitate the acquisition of nationwide lidar data. Although data meet USGS lidar specifications, some point cloud tiles include noisy and incorrectly classified points. The enhanced accuracy of classified point clouds can improve support for many downstream applications such as hydrologic analysis, urban planning, and forest management. Despite noisy and incorrectly classified points, the current 3DEP classification specifications result in data that can be useful for Digital Terrain Model (DTM) extraction; however, the quality of the classification application can be improved to match state-of-the-art capabilities. Deep Learning (DL)-based approaches have been developed with outstanding performance for point cloud classification. This study will utilize the proven DL technologies to prepare for developing a user-friendly open-source toolkit that would automate classification to refine and enrich the results of existing and future 3DEP data.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2024 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"July 7-12, 2024","conferenceLocation":"Athens, Greece","language":"English","publisher":"The Institute of Electrical and Electronics Engineers (IEEE)","doi":"10.1109/IGARSS53475.2024.10641055","usgsCitation":"Liu, J., Qin, R., and Song, S., 2024, Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer, in Proceedings of 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Athens, Greece, July 7-12, 2024, p. 8518-8521, https://doi.org/10.1109/IGARSS53475.2024.10641055.","productDescription":"4 p.","startPage":"8518","endPage":"8521","ipdsId":"IP-159942","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":439175,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/poster/Automated_Deep_Learning-based_Point_Cloud_Classification_on_USGS_3DEP_LiDAR_Data_Using_a_Transformer/26169397","text":"External Repository"},{"id":434765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Jung-Kuan 0000-0001-8461-8200","orcid":"https://orcid.org/0000-0001-8461-8200","contributorId":333940,"corporation":false,"usgs":true,"family":"Liu","given":"Jung-Kuan","email":"","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":913062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Qin, Rongjun","contributorId":333939,"corporation":false,"usgs":false,"family":"Qin","given":"Rongjun","email":"","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":913063,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Song, Shuang","contributorId":344174,"corporation":false,"usgs":false,"family":"Song","given":"Shuang","email":"","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":913064,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70258078,"text":"sir20235038 - 2024 - Three-dimensional geologic framework model of the Rio San Jose groundwater basin and adjacent areas, New Mexico","interactions":[],"lastModifiedDate":"2026-01-29T22:50:07.82264","indexId":"sir20235038","displayToPublicDate":"2024-09-04T14:30:00","publicationYear":"2024","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":"2023-5038","displayTitle":"Three-Dimensional Geologic Framework Model of the Rio San Jose Groundwater Basin and Adjacent Areas, New Mexico","title":"Three-dimensional geologic framework model of the Rio San Jose groundwater basin and adjacent areas, New Mexico","docAbstract":"As part of a U.S. Geological Survey study in cooperation with the Bureau of Reclamation and the Pueblo of Acoma, New Mexico, and the Pueblo of Laguna, New Mexico, a digital three-dimensional geologic framework model was constructed for the Rio San Jose and its surface-water drainage basin in west-central New Mexico. This three-dimensional model defines the altitude, thickness, and extent of 18 geologic units for use in a regional numerical hydrologic model. The model included an undifferentiated Proterozoic basement layer, 13 consolidated Paleozoic and Mesozoic rock units, and 4 Cenozoic units. Model input data were compiled from published cross sections, well data, structure contour maps, selected geophysical data, and data derived from geologic maps and structural features in the study area. These data were used to construct faulted surfaces that represent the upper and lower subsurface geologic unit boundaries. The digital three-dimensional geologic framework model combines faults, the altitude of the tops of each geologic unit, and boundary lines depicting the subsurface extent of each geologic unit. The digital three-dimensional geologic model described in this report and the corresponding data release represents the generalized geometry of the subsurface geologic units; it reproduces with reasonable accuracy the input geologic data and is consistent with previously published subsurface conceptualizations of the region. The geologic framework model is at a scale and resolution appropriate for use as the foundation for a numerical hydrologic model of the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235038","collaboration":"Prepared in cooperation with the Bureau of Reclamation, the Pueblo of Acoma, New Mexico, and the Pueblo of Laguna, New Mexico","usgsCitation":"Sweetkind, D.S., and Galanter, A.E., 2024, Three-dimensional geologic framework model of the Rio San Jose groundwater basin and adjacent areas, New Mexico: U.S. Geological Survey Scientific Investigations Report 2023–5038, 35 p., https://doi.org/10.3133/sir20235038.","productDescription":"Report: vii, 35 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-137937","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":433454,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MPAGA7","text":"USGS data release","linkHelpText":"Digital data for three-dimensional geologic framework model of the Rio San Jose groundwater basin, New Mexico"},{"id":433422,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5038/coverthb.jpg"},{"id":433423,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5038/sir20235038.pdf","text":"Report","size":"43.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5038"},{"id":433462,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5038/sir20235038.xml"},{"id":433425,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2023/5038/sir20235038_ReadMe.txt","size":"8.00 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2023-5038 Read me file"},{"id":433426,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2023/5038/sir20235038.mp4","text":"Animation","size":"29.2 MB","description":"SIR 2023-5038 animation","linkHelpText":"Video to accompany Figure 9. Perspective view of three-dimensional geologic framework solid model showing modeled geologic units"},{"id":433461,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5038/images"},{"id":433580,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235038/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5038"},{"id":499302,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_111885.htm","linkFileType":{"id":5,"text":"html"}},{"id":433424,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5038/sir20235038_optimized.pdf","text":"Report","size":"24.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5038 optimized for screen reading","linkHelpText":"Optimized for screen reading"}],"country":"United States","state":"New Mexico","otherGeospatial":"Rio San Jose Groundwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.77168949319592,\n 36.208846271635096\n ],\n [\n -108.77168949319592,\n 34.24361847680689\n ],\n [\n -106.69527347757077,\n 34.24361847680689\n ],\n [\n -106.69527347757077,\n 36.208846271635096\n ],\n [\n -108.77168949319592,\n 36.208846271635096\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection (MassDEP), began a study in 2018 to develop a water-quality monitoring strategy (WQMS) for Mount Hope Bay and the Taunton River Estuary in southeastern Massachusetts. MassDEP is interested in water-quality data in Mount Hope Bay and the Taunton River Estuary to characterize current water-quality conditions, assess nutrient-related effects, and capture conditions before and after planned upgrades to wastewater treatment facilities. The WQMS provides an overview of the environmental setting of Mount Hope Bay and the Taunton River Estuary and of dissolved oxygen and nutrient-related water-quality issues, reviews historical and existing monitoring data, and provides recommendations for future monitoring designed to meet five MassDEP management objectives: (1) support MassDEP’s review of coastal and marine dissolved oxygen criteria, (2) assess conditions within MassDEP waterbody assessment units in Mount Hope Bay and the Taunton River Estuary with respect to selected criteria in the Massachusetts Surface Water Quality Standards, (3) assess conditions along the freshwater/saltwater interface (salt wedge) of the Taunton River Estuary and delineate the boundary between the freshwater and saltwater waterbody assessment units, (4) estimate data requirements needed to determine nutrient loads flowing into Mount Hope Bay and the Taunton River Estuary, and (5) evaluate data requirements needed to support hydrodynamic and water-quality models for Mount Hope Bay and the Taunton River Estuary. This WQMS may be used by MassDEP to develop a statewide approach for monitoring estuaries in Massachusetts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245049","isbn":"978-1-4113-4582-9","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Armstrong, D.S., 2024, Water-quality monitoring strategy for Mount Hope Bay and the Taunton River Estuary, southeastern Massachusetts: U.S. Geological Survey Scientific Investigations Report 2024–5049, 48 p., https://doi.org/10.3133/sir20245049","productDescription":"Report: ix, 48 p.; 2 Tables","numberOfPages":"48","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-143071","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":433311,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5049/sir20245049_table1.1.xlsx","text":"Table 1.1","size":"30.3 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2024-5049, Table 1.1","linkHelpText":"- Water-resource and environmental monitoring and sample collection programs in the greater Mount Hope Bay and Taunton River Estuary, southeastern Massachusetts"},{"id":499469,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117312.htm","linkFileType":{"id":5,"text":"html"}},{"id":433310,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5049/images"},{"id":433306,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5049/coverthb2.jpg"},{"id":433309,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5049/sir20245049.XML","description":"SIR 2024-5049 XML"},{"id":433308,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245049/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5049 HTML"},{"id":433307,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5049/sir20245049.pdf","text":"Report","size":"17.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5049 PDF"},{"id":433390,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5049/sir20245049_table1.1.csv","text":"Table 1.1","size":"12.1 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5049, Table 1.1 (csv)"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Mount Hope Bay, Taunton River Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.38907759427538,\n 42.148684709184096\n ],\n [\n -71.38907759427538,\n 41.46079492189094\n ],\n [\n -70.51569507946306,\n 41.46079492189094\n ],\n [\n -70.51569507946306,\n 42.148684709184096\n ],\n [\n -71.38907759427538,\n 42.148684709184096\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532