{"pageNumber":"281","pageRowStart":"7000","pageSize":"25","recordCount":41065,"records":[{"id":70233591,"text":"70233591 - 2020 - Arsenolipids in cultured Picocystis strain ML, and their occurrence in biota and sediment from Mono Lake, California","interactions":[],"lastModifiedDate":"2022-07-27T12:08:06.165445","indexId":"70233591","displayToPublicDate":"2020-06-24T07:06:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10135,"text":"Life","active":true,"publicationSubtype":{"id":10}},"title":"Arsenolipids in cultured Picocystis strain ML, and their occurrence in biota and sediment from Mono Lake, California","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">Primary production in Mono Lake, a hypersaline soda lake rich in dissolved inorganic arsenic, is dominated by<span>&nbsp;</span><span class=\"html-italic\">Picocystis</span><span>&nbsp;</span>strain ML. We set out to determine if this photoautotrophic picoplankter could metabolize inorganic arsenic and in doing so form unusual arsenolipids (e.g., arsenic bound to 2-<span class=\"html-italic\">O</span>-methyl ribosides) as reported in other saline ecosystems and by halophilic algae. We cultivated<span>&nbsp;</span><span class=\"html-italic\">Picocystis</span><span>&nbsp;</span>strain ML on a seawater-based medium with either low (37 µM) or high (1000 µM) phosphate in the presence of arsenite (400 µM), arsenate (800 µM), or without arsenic additions (ca 0.025 µM). Cultivars formed a variety of organoarsenic compounds, including a phytyl 2-<span class=\"html-italic\">O</span>-methyl arsenosugar, depending upon the cultivation conditions and arsenic exposure. When the cells were grown at low P, the organoarsenicals they produced when exposed to both arsenite and arsenate were primarily arsenolipids (~88%) with only a modest content of water-soluble organoarsenic compounds (e.g., arsenosugars). When grown at high P, sequestration shifted to primarily water-soluble, simple methylated arsenicals such as dimethylarsinate; arsenolipids still constituted ~32% of organoarsenic incorporated into cells exposed to arsenate but &lt; 1% when exposed to arsenite. Curiously,<span>&nbsp;</span><span class=\"html-italic\">Picocystis</span><span>&nbsp;</span>strain ML grown at low P and exposed to arsenate sequestered huge amounts of arsenic into the cells accounting for 13.3% of the dry biomass; cells grown at low P and arsenite exposure sequestered much lower amounts, equivalent to 0.35% of dry biomass. Extraction of a resistant phase with trifluoroacetate recovered most of the sequestered arsenic in the form of arsenate. Uptake of arsenate into low P-cultivated cells was confirmed by X-ray fluorescence, while XANES/EXAFS spectra indicated the sequestered arsenic was retained as an inorganic iron precipitate, similar to scorodite, rather than as an As-containing macromolecule. Samples from Mono Lake demonstrated the presence of a wide variety of organoarsenic compounds, including arsenosugar phospholipids, most prevalent in zooplankton (<span class=\"html-italic\">Artemia</span>) and phytoplankton samples, with much lower amounts detected in the bottom sediments. These observations suggest a trophic transfer of organoarsenicals from the phytoplankton (<span class=\"html-italic\">Picocystis</span>) to the zooplankton (<span class=\"html-italic\">Artemia</span>) community, with efficient bacterial mineralization of any lysis-released organoarsenicals back to inorganic oxyanions before they sink to the sediments.<span>&nbsp;</span></div>","language":"English","publisher":"MDPI","doi":"10.3390/life10060093","usgsCitation":"Glabonjat, R.A., Blum, J.S., Miller, L., Webb, S.M., Stolz, J.F., Francesconi, K.A., and Oremland, R.S., 2020, Arsenolipids in cultured Picocystis strain ML, and their occurrence in biota and sediment from Mono Lake, California: Life, v. 10, no. 6, 93, 21 p., https://doi.org/10.3390/life10060093.","productDescription":"93, 21 p.","ipdsId":"IP-119352","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":456298,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/life10060093","text":"Publisher Index Page"},{"id":436919,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90VW2FP","text":"USGS data release","linkHelpText":"Growth of cultured Picocystis strain ML in the presence of arsenic, and occurrence of arsenolipids in these Picocystis as well as biota and sediment from Mono Lake, California"},{"id":404483,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mono Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.29092407226561,\n              37.85100126460795\n            ],\n            [\n              -118.73886108398438,\n              37.85100126460795\n            ],\n            [\n              -118.73886108398438,\n              38.15723682167875\n            ],\n            [\n              -119.29092407226561,\n              38.15723682167875\n            ],\n            [\n              -119.29092407226561,\n              37.85100126460795\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Glabonjat, Ronald A. 0000-0003-3104-1940","orcid":"https://orcid.org/0000-0003-3104-1940","contributorId":225202,"corporation":false,"usgs":false,"family":"Glabonjat","given":"Ronald","email":"","middleInitial":"A.","affiliations":[{"id":41074,"text":"Institute of Chemistry, NAWI Graz, University of Graz, Graz Austria","active":true,"usgs":false}],"preferred":false,"id":847500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blum, Jodi S. 0000-0002-1733-1506 jsblum@usgs.gov","orcid":"https://orcid.org/0000-0002-1733-1506","contributorId":225203,"corporation":false,"usgs":true,"family":"Blum","given":"Jodi","email":"jsblum@usgs.gov","middleInitial":"S.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":847501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Laurence G. 0000-0002-7807-3475 lgmiller@usgs.gov","orcid":"https://orcid.org/0000-0002-7807-3475","contributorId":2460,"corporation":false,"usgs":true,"family":"Miller","given":"Laurence G.","email":"lgmiller@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":847502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Webb, Samuel M. 0000-0003-1188-0464","orcid":"https://orcid.org/0000-0003-1188-0464","contributorId":225204,"corporation":false,"usgs":false,"family":"Webb","given":"Samuel","email":"","middleInitial":"M.","affiliations":[{"id":41075,"text":"Stanford Synchrotron Radiation Lightsource, Menlo Park, California","active":true,"usgs":false}],"preferred":false,"id":847503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stolz, John F.","contributorId":179305,"corporation":false,"usgs":false,"family":"Stolz","given":"John","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":847504,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Francesconi, Kevin A. 0000-0002-2536-0542","orcid":"https://orcid.org/0000-0002-2536-0542","contributorId":225205,"corporation":false,"usgs":false,"family":"Francesconi","given":"Kevin","email":"","middleInitial":"A.","affiliations":[{"id":41074,"text":"Institute of Chemistry, NAWI Graz, University of Graz, Graz Austria","active":true,"usgs":false}],"preferred":false,"id":847505,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Oremland, Ronald S. 0000-0001-7382-0147 roremlan@usgs.gov","orcid":"https://orcid.org/0000-0001-7382-0147","contributorId":931,"corporation":false,"usgs":true,"family":"Oremland","given":"Ronald","email":"roremlan@usgs.gov","middleInitial":"S.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":847506,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70218204,"text":"70218204 - 2020 - Informing amphibian conservation efforts with abundance-based metapopulation models","interactions":[],"lastModifiedDate":"2021-02-19T20:39:19.47464","indexId":"70218204","displayToPublicDate":"2020-06-23T14:35:01","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1892,"text":"Herpetologica","active":true,"publicationSubtype":{"id":10}},"title":"Informing amphibian conservation efforts with abundance-based metapopulation models","docAbstract":"<p><span>Science-based management strategies are needed to halt or reverse the global decline of amphibians. In many cases, sound management requires reliable models built using monitoring data. Historically, monitoring and statistical modeling efforts have focused on estimating occupancy using detection–nondetection data. Spatial occupancy models are useful for studying colonization–extinction dynamics, but richer insights can be gained from estimating abundance and density-dependent demographic rates. We developed an integrated abundance-based metapopulation model of the processes contributing to spatiotemporal variation in patch population density. We fit our model to a combination of detection–nondetection and count data from a 14-yr study of a reintroduced metapopulation of federally threatened Chiricahua Leopard Frogs (Lithobates chiricahuensis). Pond-specific population growth rate was influenced by pond hydroperiod and frog density, such that permanent and semipermanent ponds with low densities of adult frogs experienced the highest annual population growth rates. Immigration rate declined as the distance among ponds increased. After reintroduction in 2003, metapopulation-level abundance increased and appeared to stabilize around 1300 adult frogs (95% CI = 1192–1471) by year 2015. Further, changes in metapopulation abundance were driven mostly by changes in abundance at a few ponds. These high-density populations, which would not have been identifiable with traditional occupancy-based metapopulation models, are likely especially important for species recovery in the area. Abundance-based metapopulation models can be widely applied to inform conservation efforts, by providing higher quality information needed to prioritize habitat patches for management and can be used to make more accurate predictions of metapopulation extinction risk.</span></p>","language":"English","publisher":"The Herpetologists' League","doi":"10.1655/0018-0831-76.2.240","usgsCitation":"Howell, P.E., Hossack, B., Muths, E., Sigafus, B.H., and Chandler, R., 2020, Informing amphibian conservation efforts with abundance-based metapopulation models: Herpetologica, v. 76, no. 2, p. 240-250, https://doi.org/10.1655/0018-0831-76.2.240.","productDescription":"11 p.","startPage":"240","endPage":"250","ipdsId":"IP-111558","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":383399,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Howell, Paige E","contributorId":251713,"corporation":false,"usgs":false,"family":"Howell","given":"Paige","email":"","middleInitial":"E","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":810414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":810415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":243368,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":810416,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sigafus, Brent H. 0000-0002-7422-8927 bsigafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7422-8927","contributorId":4534,"corporation":false,"usgs":true,"family":"Sigafus","given":"Brent","email":"bsigafus@usgs.gov","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":810417,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chandler, Richard B.","contributorId":251714,"corporation":false,"usgs":false,"family":"Chandler","given":"Richard B.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":810418,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70211174,"text":"70211174 - 2020 - Effects of snowpack, temperature, and disease on the demography of a wild population of amphibians","interactions":[],"lastModifiedDate":"2020-08-06T19:20:03.217264","indexId":"70211174","displayToPublicDate":"2020-06-23T10:51:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1892,"text":"Herpetologica","active":true,"publicationSubtype":{"id":10}},"title":"Effects of snowpack, temperature, and disease on the demography of a wild population of amphibians","docAbstract":"<p><span>Understanding the demographic consequences of interactions among pathogens, hosts, and weather conditions is critical in determining how amphibian populations respond to disease and in identifying site-specific conservation actions that can be developed to bolster persistence of amphibian populations. We investigated population dynamics in Boreal Toads (</span><i>Anaxyrus boreas</i><span>) relative to abiotic (fall temperatures and snowpack) and biotic (the abundance of another anuran host and disease) characteristics of the local environment in Wyoming, USA. We used capture–recapture data and a multistate model where state was treated as a hidden Markov process to incorporate disease state uncertainty and assess our a priori hypotheses. Our results indicated that snowpack during the coldest week of winter is more influential to toad survival, disease transition probabilities, and the population-level prevalence of the amphibian chytrid fungus (</span><i>Batrachochytrium dendrobatidis</i><span>) in the spring, than temperatures in the fall or the presence of another host. As hypothesized, apparent survival at low (i.e., &lt;25 cm) snowpack (0.22; confidence interval [CI] = 0.15–0.31) was lower than apparent survival at high snowpack (90.65; CI = 0.50–0.78). Our findings highlight the potential for local environmental factors, like snowpack, to influence disease and host persistence, and demonstrate the ecological complexity of disease effects on population demography in natural environments. This work further emphasizes the need for improved understanding of how climate change may influence the relationships among pathogens, hosts, and their environment for wild animal populations challenged by disease.</span></p>","language":"English","publisher":"BioOne","doi":"10.1655/0018-0831-76.2.132","usgsCitation":"Muths, E., Hossack, B., Grant, E.H., Pilliod, D., and Mosher, B.A., 2020, Effects of snowpack, temperature, and disease on the demography of a wild population of amphibians: Herpetologica, v. 76, no. 2, p. 132-143, https://doi.org/10.1655/0018-0831-76.2.132.","productDescription":"12 p.","startPage":"132","endPage":"143","ipdsId":"IP-111041","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":436920,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VACHX0","text":"USGS data release","linkHelpText":"Capture-recapture, disease and covariate data for boreal toads from Blackrock Wyoming 2019"},{"id":376433,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bridger-Teton National Forest, Togwetee Pass","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -110.37139892578125,\n              43.49676775343911\n            ],\n            [\n              -109.86602783203125,\n              43.49676775343911\n            ],\n            [\n              -109.86602783203125,\n              43.866218006556394\n            ],\n            [\n              -110.37139892578125,\n              43.866218006556394\n            ],\n            [\n              -110.37139892578125,\n              43.49676775343911\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"76","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":229346,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":792944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":792945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grant, Evan H. 0000-0003-4401-6496","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":229348,"corporation":false,"usgs":true,"family":"Grant","given":"Evan","email":"","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792946,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":792947,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mosher, Brittany A.","contributorId":189579,"corporation":false,"usgs":false,"family":"Mosher","given":"Brittany","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":792948,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70211180,"text":"70211180 - 2020 - A synthesis of evidence of drivers of amphibian declines","interactions":[],"lastModifiedDate":"2020-07-16T15:46:40.508724","indexId":"70211180","displayToPublicDate":"2020-06-23T10:43:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1892,"text":"Herpetologica","active":true,"publicationSubtype":{"id":10}},"title":"A synthesis of evidence of drivers of amphibian declines","docAbstract":"<p><span>Early calls for robust long-term time series of amphibian population data, stemming from discussion following the first World Congress of Herpetology, are now being realized after 25 yr of focused research. Inference from individual studies and locations have contributed to a basic consensus on drivers of amphibian declines. Until recently there were no large-scale syntheses of long-term time series data to test hypotheses about the generality of factors driving population dynamics at broad spatial scales. Through the U.S. Geological Survey's Powell Center for Analysis and Synthesis, we brought together a group of scientists to elucidate mechanisms underlying amphibian declines in North America and Europe. We used time series of field data collected across dozens of study areas to make inferences with these combined data using hierarchical and spatial models. We bring together results from four syntheses of these data to summarize our state of knowledge of amphibian declines, identify commonalities that suggest further avenues of study, and suggest a way forward in addressing amphibian declines—by looking beyond specific drivers to how to achieve stability in remaining populations. The common thread of the syntheses is that declines are real but not ubiquitous, and that multiple factors drive declines but the relative importance of each factor varies among species, populations, and regions. We also found that climate is an important driver of amphibian population dynamics. However, the direction and magnitude of sensitivity to change vary among species in ways unlikely to explain overall rates of decline. Thirty years after the initial identification of a major catastrophe for global biodiversity, the scientific community has empirically demonstrated the reality of the problem, identified putative causes, provided evidence of their impacts, invested in broader-scale actions, and attempted meta-analyses to search out global drivers. We suggest an approach that focuses on key demographic rates that may improve amphibian population trends at multiple sites across the landscape.</span></p>","language":"English","publisher":"BioOne","doi":"10.1655/0018-0831-76.2.101","usgsCitation":"Grant, E.H., Miller, D., and Muths, E., 2020, A synthesis of evidence of drivers of amphibian declines: Herpetologica, v. 76, no. 2, p. 101-107, https://doi.org/10.1655/0018-0831-76.2.101.","productDescription":"7 p.","startPage":"101","endPage":"107","ipdsId":"IP-111040","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":376430,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Grant, Evan H. 0000-0003-4401-6496","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":229348,"corporation":false,"usgs":true,"family":"Grant","given":"Evan","email":"","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, D. A. W.","contributorId":216930,"corporation":false,"usgs":false,"family":"Miller","given":"D. A. W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":792979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":224061,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":792980,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70210791,"text":"70210791 - 2020 - The predictive skills of elastic Coulomb rate-and-state aftershock forecasts during the 2019 Ridgecrest, California, earthquake sequence","interactions":[],"lastModifiedDate":"2020-08-26T19:12:50.615604","indexId":"70210791","displayToPublicDate":"2020-06-23T10:23:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"The predictive skills of elastic Coulomb rate-and-state aftershock forecasts during the 2019 Ridgecrest, California, earthquake sequence","docAbstract":"Operational earthquake forecasting protocols commonly use statistical models for their recognized ease of implementation and robustness in describing the short-term spatiotemporal patterns of triggered seismicity. However, recent advances on physics-based aftershock forecasting reveal comparable performance to the standard statistical counterparts with significantly improved predictive skills when fault and stress field heterogeneities are considered. Here, we perform a pseudo-prospective forecasting experiment during the first month of the 2019 Ridgecrest (California) earthquake sequence. We develop seven Coulomb rate-and-state models that couple static stress change estimates with continuum mechanics expressed by the rate-and-state friction laws. Our model parametrization supports a gradually increasing complexity; we start from a preliminary model implementation with simplified slip distributions and spatially homogeneous receiver faults to reach an enhanced one featuring optimized fault constitutive parameters, finite-fault slip models, secondary triggering effects, and spatially heterogenous planes informed by pre-existing ruptures. The data-rich environment of Southern California allows us to test whether incorporating data collected in near real-time during an unfolding earthquake sequence boosts our predictive power. We assess the absolute and relative performance of the forecasts by means of statistical tests used within the Collaboratory for the Study of Earthquake Predictability (CSEP) and compare their skills against a standard benchmark ETAS model for the short (24 hours after the two Ridgecrest mainshocks) and intermediate-term (one month). Stress-based forecasts expect heightened rates along the whole near-fault region and increased expected seismicity rates in Central Garlock Fault. Our comparative model evaluation supports that faulting heterogeneities coupled with secondary triggering effects are the most critical success components behind physics-based forecasts, but also underlines the importance of model updates incorporating near real-time available aftershock data reaching better performance than ETAS models. We explore the physical basis behind our results by investigating the localized shut down of pre-existing normal faults in the Ridgecrest near-source area.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200028","usgsCitation":"Mancini, S., Segou, M., Werner, M., and Parsons, T.E., 2020, The predictive skills of elastic Coulomb rate-and-state aftershock forecasts during the 2019 Ridgecrest, California, earthquake sequence: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1736-1751, https://doi.org/10.1785/0120200028.","productDescription":"16 p.","startPage":"1736","endPage":"1751","ipdsId":"IP-117717","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456304,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research-information.bris.ac.uk/en/publications/b86ef22d-e493-45b3-b98c-b20b940530be","text":"External Repository"},{"id":375920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -118.33923339843749,\n              35.14237113713991\n            ],\n            [\n              -116.83959960937499,\n              35.14237113713991\n            ],\n            [\n              -116.83959960937499,\n              36.37706783983682\n            ],\n            [\n              -118.33923339843749,\n              36.37706783983682\n            ],\n            [\n              -118.33923339843749,\n              35.14237113713991\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Mancini, Simone 0000-0003-3415-2080","orcid":"https://orcid.org/0000-0003-3415-2080","contributorId":225525,"corporation":false,"usgs":false,"family":"Mancini","given":"Simone","email":"","affiliations":[{"id":37322,"text":"University of Bristol","active":true,"usgs":false}],"preferred":false,"id":791436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Segou, Margarita","contributorId":199044,"corporation":false,"usgs":false,"family":"Segou","given":"Margarita","affiliations":[],"preferred":false,"id":791437,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Werner, Maximillian J","contributorId":195950,"corporation":false,"usgs":false,"family":"Werner","given":"Maximillian J","affiliations":[],"preferred":false,"id":791438,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":791439,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70210729,"text":"fs20203033 - 2020 - NHDPlus High Resolution (NHDPlus HR)---A hydrography framework for the Nation","interactions":[],"lastModifiedDate":"2020-06-23T14:32:17.573219","indexId":"fs20203033","displayToPublicDate":"2020-06-23T09:20:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3033","displayTitle":"NHDPlus High Resolution (NHDPlus HR)&mdash;A Hydrography Framework for the Nation","title":"NHDPlus High Resolution (NHDPlus HR)---A hydrography framework for the Nation","docAbstract":"<p>Reliable and accurate high-resolution mapping of the Nation’s waters are critical inputs to models and decision support systems used to predict risk and enable response to impacts on water resources. It is necessary to know where the water is and how it relates to features beyond the stream network like forests, cities, and infrastructure. An up-to-date, high-resolution national hydrography framework is required for modeling the occurrence of water and to provide the ability to connect detailed information from the surrounding landscape to the stream network. To support this, the U.S. Geological Survey is developing NHDPlus High Resolution (NHDPlus HR), the next generation of NHDPlus using updated, high-resolution datasets to create a modern, scalable, and openly accessible hydrography framework for the inland waters of the Nation.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203033","usgsCitation":"Buto, S.G., and Anderson, R.D., 2020, NHDPlus High Resolution (NHDPlus HR)---A hydrography framework for the Nation: U.S. Geological Survey Fact Sheet 2020-3033, 2 p., https://doi.org/10.3133/fs20203033.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112468","costCenters":[{"id":423,"text":"National Geospatial 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,{"id":70210778,"text":"70210778 - 2020 - Hydrodynamic modeling results showing the effects of the Luce Bayou interbasin transfer on salinity in Lake Houston, TX","interactions":[],"lastModifiedDate":"2020-12-15T20:19:25.123808","indexId":"70210778","displayToPublicDate":"2020-06-23T08:45:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3536,"text":"Texas Water Journal","active":true,"publicationSubtype":{"id":10}},"title":"Hydrodynamic modeling results showing the effects of the Luce Bayou interbasin transfer on salinity in Lake Houston, TX","docAbstract":"<p><span>An overreliance on groundwater resources in the Houston (Texas) metropolitan area led to aquifer drawdowns and land subsidence, so regional water suppliers have been turning to surface water resources to meet water demand. Lake Houston, an important water supply reservoir 24 kilometers (15 miles) northeast of downtown Houston, requires new water supply sources to continue to meet water supply demands for the next several decades. The upcoming Luce Bayou Interbasin Transfer Project will divert up to 500 million gallons per day of Trinity River water into Lake Houston. Trinity River water has significantly different water quality than the Lake Houston tributaries. To evaluate the project’s potential effect on water quality, the U.S. Geological Survey used an enhanced version of a previously released Lake Houston hydrodynamic model. With a focus on salinity and water-surface elevations, the model combined data from 2009 to 2017 with simulated flow from the Luce Bayou Interbasin Transfer to evaluate potential outcomes from three hypothetical flow scenarios. Overall, these scenarios found that the Luce Bayou Interbasin Transfer would cause salinities to moderately rise over most of the modeled time (2009–2017), although salinities were buffered under 2011 drought conditions. Large inflow events equalized salinities under baseline conditions as well as the enhanced flow scenarios.</span></p>","language":"English","publisher":"Texas Water Resources Institute","doi":"10.21423/twj.v11i1.7094","usgsCitation":"Smith, E.A., and Shah, S.D., 2020, Hydrodynamic modeling results showing the effects of the Luce Bayou interbasin transfer on salinity in Lake Houston, TX: Texas Water Journal, v. 11, no. 1, p. 64-88, https://doi.org/10.21423/twj.v11i1.7094.","productDescription":"25 p.","startPage":"64","endPage":"88","ipdsId":"IP-107391","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":456306,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21423/twj.v11i1.7094","text":"Publisher Index Page"},{"id":436921,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AVUJ73","text":"USGS data release","linkHelpText":"Lake Houston (Texas) EFDC 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,{"id":70211119,"text":"70211119 - 2020 - The Fire and Tree Mortality Database, for empirical modeling of individual tree mortality after fire","interactions":[],"lastModifiedDate":"2020-07-16T17:44:25.115906","indexId":"70211119","displayToPublicDate":"2020-06-22T11:14:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"The Fire and Tree Mortality Database, for empirical modeling of individual tree mortality after fire","docAbstract":"<p><span>Wildland fires have a multitude of ecological effects in forests, woodlands, and savannas across the globe. A major focus of past research has been on tree mortality from fire, as trees provide a vast range of biological services. We assembled a database of individual-tree records from prescribed fires and wildfires in the United States. The Fire and Tree Mortality (FTM) database includes records from 164,293 individual trees with records of fire injury (crown scorch, bole char, etc.), tree diameter, and either mortality or top-kill up to ten years post-fire. Data span 142 species and 62 genera, from 409 fires occurring from 1981-2016. Additional variables such as insect attack are included when available. The FTM database can be used to evaluate individual fire-caused mortality models for pre-fire planning and post-fire decision support, to develop improved models, and to explore general patterns of individual fire-induced tree death. The database can also be used to identify knowledge gaps that could be addressed in future research.</span></p>","language":"English","publisher":"Springer Nature","doi":"10.1038/s41597-020-0522-7","usgsCitation":"Cansler, C., Hood, S.M., Varner, J., van Mantgem, P., Agne, M.C., Andrus, R.A., Ayres, M.P., Ayres, B.D., Bakker, J., Battaglia, M.A., Bentz, B.J., Breece, C.R., Brown, J.K., Cluck, D.R., Coleman, T.W., Corace, R.G., Covington, W.W., Cram, D.S., Cronan, J.B., Crouse, J.E., Das, A., Davis, R.S., Dickinson, D.M., Fitzgerald, S.A., Fule, P., Ganio, L.M., Grayson, L.M., Halpern, C.B., Hanula, J.L., Harvey, B.J., Hiers, J.K., Huffman, D.W., Keifer, M., Keyser, T.L., Kobziar, L.N., Kolb, T.E., Kolden, C.A., Kopper, K.E., Kreitler, J.R., Kreye, J.K., Latimer, A.M., Lerch, A.P., Lombardero, M.J., McDaniel, V.L., McHugh, C.W., McMillin, J.D., Moghaddas, J.J., O’Brien, J.J., Perrakis, D.D., Peterson, D.W., Pritchard, S.J., Progar, R.A., Raffa, K.F., Reinhardt, E.D., Restaino, J.C., 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,{"id":70223191,"text":"70223191 - 2020 - The 1933 Long Beach Earthquake (California, USA): Ground motions and rupture scenario","interactions":[],"lastModifiedDate":"2021-08-17T12:40:37.989468","indexId":"70223191","displayToPublicDate":"2020-06-22T07:39:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"The 1933 Long Beach Earthquake (California, USA): Ground motions and rupture scenario","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>We present a synoptic analysis of the ground motions from the 11 March 1933 Mw 6.4 Long Beach, California, earthquake, the largest known earthquake within the central Los Angeles Basin region. Our inferred shaking intensity pattern supports the association of the earthquake with the Newport-Inglewood fault; it further illuminates the concentration of severe damage in the town of Compton, where accounts suggest vertical ground motions exceeding 1 <i>g</i>. We use a broadband simulation approach to develop a rupture scenario for this earthquake, informed by the damage distribution. The predicted shaking for a 25-km-long fault matches the intensity distribution, with an indication that non-linear site response on soft sediments in some near-field regions was stronger than predicted using a simple model to account for non-linearity. Our results suggest that the concentration of damage near Compton can be explained by a combination of local site amplification, source-controlled directivity, and three-dimensional basin effects whereby energy was channeled towards the deepest part of the Los Angeles Basin.</p></div></div>","language":"English","publisher":"Nature","doi":"10.1038/s41598-020-66299-w","usgsCitation":"Hough, S.E., and Graves, R., 2020, The 1933 Long Beach Earthquake (California, USA): Ground motions and rupture scenario: Scientific Reports, v. 10, 10017, 10 p., https://doi.org/10.1038/s41598-020-66299-w.","productDescription":"10017, 10 p.","ipdsId":"IP-119086","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456331,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-66299-w","text":"Publisher Index Page"},{"id":387981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Long Beach","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -118.33923339843749,\n              33.678639851675555\n            ],\n            [\n              -118.0206298828125,\n              33.678639851675555\n            ],\n            [\n              -118.0206298828125,\n              33.88865750124075\n            ],\n            [\n              -118.33923339843749,\n              33.88865750124075\n            ],\n            [\n              -118.33923339843749,\n              33.678639851675555\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":821326,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":821327,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70228546,"text":"70228546 - 2020 - Balancing fish-energy-cost tradeoffs through strategic basin-wide dam management","interactions":[],"lastModifiedDate":"2022-02-14T20:23:17.020586","indexId":"70228546","displayToPublicDate":"2020-06-20T15:22:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3267,"text":"Resources, Conservation and Recycling","active":true,"publicationSubtype":{"id":10}},"title":"Balancing fish-energy-cost tradeoffs through strategic basin-wide dam management","docAbstract":"<p>Dam management often involves tradeoffs among hydropower generation capacity, environmental impacts, and project costs. However, our understandings of such tradeoffs under a full range of dam management options remain limited, which hinders our ability to make sound and scientifically defensible dam management decisions. In order to assess the scope for theoretical tradeoffs, a dynamic model of hydropower production, important fish populations, and project costs was developed using the system dynamics modeling technique. Three dam management options investigated the likely outcomes from: dam removal, fishway installation (e.g., pool-and-weir, Denil, and fish lift), and no action. The model was applied to the Penobscot River located in Maine, United States as a proof of concept, where recent actions (i.e., dam removal and fishway construction) have been undertaken. We modeled theoretical influence of these actions on four significant sea-run fish (alewife <i>Alosa pseudoharengus</i>, American shad <i>Alosa sapidissima</i>, Atlantic salmon <i>Salmo salar</i>, and sea lamprey <i>Petromyzon marinus</i>) by developing an index of spawner population potential based on population models for each species. Optimal dam management solutions may maximize spawner population potential and energy production to 60-62% of maximum achievable values while limiting the project cost to US$17 million (44% of the maximum value). Our results demonstrate that basin-scale management strategies may increase the migratory fish restoration while preserving hydropower generation capacity. Diversification of management options (e.g., combination of fishway installations, dam removals, and generation capacity) may increase the efficacy of strategic fish-energy-cost tradeoffs.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.resconrec.2020.104990","usgsCitation":"Song, C., O’Malley, A., Zydlewski, J.D., and Mo, W., 2020, Balancing fish-energy-cost tradeoffs through strategic basin-wide dam management: Resources, Conservation and Recycling, v. 161, 104990, 12 p., https://doi.org/10.1016/j.resconrec.2020.104990.","productDescription":"104990, 12 p.","ipdsId":"IP-117399","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":456334,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.resconrec.2020.104990","text":"Publisher Index Page"},{"id":395927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Penobscot River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -69.8126220703125,\n              44.286502899553156\n            ],\n            [\n              -67.63458251953125,\n              44.286502899553156\n            ],\n            [\n              -67.63458251953125,\n              45.79625461321962\n            ],\n            [\n              -69.8126220703125,\n              45.79625461321962\n            ],\n            [\n              -69.8126220703125,\n              44.286502899553156\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"161","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Song, Cuihong","contributorId":265998,"corporation":false,"usgs":false,"family":"Song","given":"Cuihong","email":"","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":834541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Malley, Andrew","contributorId":169716,"corporation":false,"usgs":false,"family":"O’Malley","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":834542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":834540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mo, Weiwei","contributorId":266002,"corporation":false,"usgs":false,"family":"Mo","given":"Weiwei","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":834543,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70228940,"text":"70228940 - 2020 - Habitat associations and distributions of two endemic crayfishes, Cambarus (Erebicambarus) maculatus Hobbs & Pflieger, 1988 and Faxonius (Billecambarus) harrisonii (Faxon, 1884) (Decapoda: Astacoidea: Cambaridae), in the Meramec River drainage, Missouri, USA","interactions":[],"lastModifiedDate":"2022-02-24T16:03:02.114215","indexId":"70228940","displayToPublicDate":"2020-06-20T09:59:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"displayTitle":"Habitat associations and distributions of two endemic crayfishes, <i>Cambarus</i> (<i>Erebicambarus</i>) <i>maculatus</i> Hobbs & Pflieger, 1988 and <i>Faxonius</i> (<i>Billecambarus</i>) <i>harrisonii</i> (Faxon, 1884) (Decapoda: Astacoidea: Cambaridae), in the Meramec River drainage, Missouri, USA","title":"Habitat associations and distributions of two endemic crayfishes, Cambarus (Erebicambarus) maculatus Hobbs & Pflieger, 1988 and Faxonius (Billecambarus) harrisonii (Faxon, 1884) (Decapoda: Astacoidea: Cambaridae), in the Meramec River drainage, Missouri, USA","docAbstract":"<p><span>Understanding the habitat associations and distributions of rare species is important to inform management and policy decisions.&nbsp;</span><i>Cambarus</i><span>&nbsp;(</span><i>Erebicambarus</i><span>)&nbsp;</span><i>maculatus</i><span>&nbsp;&nbsp;</span><span id=\"jumplink-CIT0044\" class=\"xrefLink\"></span><a class=\"link link-ref link-reveal xref-bibr\" data-open=\"CIT0044\">Hobbs &amp; Pflieger, 1988</a><span>, the freckled crayfish, and&nbsp;</span><i>Faxonius</i><span>&nbsp;(</span><i>Billecambarus</i><span>)&nbsp;</span><i>harrisonii</i><span>&nbsp;(</span><span id=\"jumplink-CIT0033\" class=\"xrefLink\"></span><a class=\"link link-ref link-reveal xref-bibr\" data-open=\"CIT0033\">Faxon, 1884</a><span>), the belted crayfish, are two of Missouri’s endemic crayfish species. Both species are listed as Vulnerable (S3) on Missouri’s Species and Communities of Conservation Concern Checklist due to their limited range within the Meramec River drainage (MRD) and the impact of anthropogenic activities therein. Their distributional overlap offers an opportunity for multi-species research to address gaps in information required for conservation. We sampled 140 sites throughout the MRD during the summers of 2017 and 2018 for crayfishes and associated habitat variables, which we related to crayfish presence in an occupancy modeling framework. We found that&nbsp;</span><i>C. maculatus</i><span>&nbsp;occupancy was associated with larger stream size, boulder substrate, dolomite lithology, aquatic vegetation beds, dissolved oxygen, and pool mesohabitat.&nbsp;</span><i>Faxonius harrisonii</i><span>&nbsp;occupancy increased with boulder substrate, aquatic vegetation beds, the presence of&nbsp;</span><i>C. maculatus</i><span>, and decreased in third-order streams. We also expanded the known range for both species within the MRD. Range estimates (watershed area) for&nbsp;</span><i>C. maculatus</i><span>&nbsp;and&nbsp;</span><i>F. harrisonii</i><span>&nbsp;were 4,347 km</span><sup>2</sup><span>&nbsp;and 3,690 km</span><sup>2</sup><span>, respectively. This study demonstrates the importance of targeted rather than opportunistic sampling for species distribution.</span></p>","language":"English","publisher":"Oxford University Press","doi":"10.1093/jcbiol/ruaa033","usgsCitation":"Chilton, J., Rosenberger, A.E., and DiStefano, R., 2020, Habitat associations and distributions of two endemic crayfishes, Cambarus (Erebicambarus) maculatus Hobbs & Pflieger, 1988 and Faxonius (Billecambarus) harrisonii (Faxon, 1884) (Decapoda: Astacoidea: Cambaridae), in the Meramec River drainage, Missouri, USA, v. 40, no. 4, p. 351-363, https://doi.org/10.1093/jcbiol/ruaa033.","productDescription":"13 p.","startPage":"351","endPage":"363","ipdsId":"IP-123414","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456340,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jcbiol/ruaa033","text":"Publisher Index Page"},{"id":396426,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Meramec River drainage","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -91.90887451171875,\n              37.54239958054064\n            ],\n            [\n              -90.428466796875,\n              37.54239958054064\n            ],\n            [\n              -90.428466796875,\n              38.59970036588819\n            ],\n            [\n              -91.90887451171875,\n              38.59970036588819\n            ],\n            [\n              -91.90887451171875,\n              37.54239958054064\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"40","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Chilton, J.","contributorId":280068,"corporation":false,"usgs":false,"family":"Chilton","given":"J.","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":835981,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberger, Amanda E. 0000-0002-5520-8349 arosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5520-8349","contributorId":5581,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Amanda","email":"arosenberger@usgs.gov","middleInitial":"E.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DiStefano, Robert  J.","contributorId":213268,"corporation":false,"usgs":false,"family":"DiStefano","given":"Robert  J.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":835982,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70212869,"text":"70212869 - 2020 - 3D fault architecture controls the dynamism of earthquake swarm","interactions":[],"lastModifiedDate":"2020-09-02T01:05:58.372079","indexId":"70212869","displayToPublicDate":"2020-06-19T20:03:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"3D fault architecture controls the dynamism of earthquake swarm","docAbstract":"<p><span>The vibrant evolutionary patterns made by earthquake swarms are incompatible with standard, effectively two-dimensional (2D) models for general fault architecture. We leverage advances in earthquake monitoring with a deep-learning algorithm to image a fault zone hosting a 4-year-long swarm in southern California. We infer that fluids are naturally injected into the fault zone from below and diffuse through strike-parallel channels while triggering earthquakes. A permeability barrier initially limits up-dip swarm migration but ultimately is circumvented. This enables fluid migration within a shallower section of the fault with fundamentally different mechanical properties. Our observations provide high-resolution constraints on the processes by which swarms initiate, grow, and arrest. These findings illustrate how swarm evolution is strongly controlled by 3D variations in fault architecture.</span></p>","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/science.abb0779","usgsCitation":"Ross, Z., Cochran, E.S., Trugman, D., and Smith, J., 2020, 3D fault architecture controls the dynamism of earthquake swarm: Science, v. 368, no. 6497, p. 1357-1361, https://doi.org/10.1126/science.abb0779.","productDescription":"5 p.","startPage":"1357","endPage":"1361","ipdsId":"IP-118187","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456344,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1739984","text":"External Repository"},{"id":378083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"368","issue":"6497","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ross, Z.","contributorId":215300,"corporation":false,"usgs":false,"family":"Ross","given":"Z.","email":"","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":797732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trugman, D.","contributorId":173991,"corporation":false,"usgs":false,"family":"Trugman","given":"D.","email":"","affiliations":[{"id":15303,"text":"University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":797734,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Jonathan D.","contributorId":239737,"corporation":false,"usgs":false,"family":"Smith","given":"Jonathan D.","affiliations":[],"preferred":false,"id":797735,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70210923,"text":"70210923 - 2020 - Hurricane Sandy effects on coastal marsh elevation change","interactions":[],"lastModifiedDate":"2020-09-24T14:26:25.740789","indexId":"70210923","displayToPublicDate":"2020-06-19T09:19:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Hurricane Sandy effects on coastal marsh elevation change","docAbstract":"High-magnitude storm events such as Hurricane Sandy are powerful agents of geomorphic change in coastal marshes, potentially altering their surface elevation trajectories. But how do a storm’s impacts vary across a large region spanning a variety of wetland settings and storm exposures and intensities. We determined the short-term impacts of Hurricane Sandy at 223 surface elevation table–marker horizon stations in estuarine marshes located across the northeast region of the United States by comparing post-storm surface elevation change with pre-storm elevation trends. We hypothesized that the storm’s effect on marsh elevation trends would be influenced by position relative to landfall (right or left) and distance from landfall. The structural equation model presented predicts that marshes located to the left of landfall were more likely to experience an elevation gain greater than expected, and this positive deviation from pre-storm elevation trends tended to have a greater magnitude than those experiencing negative deviations (elevation loss), potentially due to greater sediment deposition. The magnitude of negative deviations from elevation change in marshes to the right of landfall was greater than for positive deviations, with a greater effect in marshes within 200 km of landfall, potentially from the extent and magnitude of storm surge. Overall, results provide an integrated picture of how storm characteristics combined with the local wetland setting are important to a storm’s impact on surface elevation, and that the surface elevation response can vary widely among sites across a region impacted by the same storm.","language":"English","publisher":"Springer","doi":"10.1007/s12237-020-00758-5","usgsCitation":"Yeates, A.G., Grace, J., Olker, J.H., Guntenspergen, G.R., Cahoon, D., Adamowicz, S.C., Anisfeld, S.C., Barrett, N., Benzecry, A., Blum, L.K., Christian, R.T., Grzyb, J., Kracauer Hartig, E., Hines Leo, K., Lerberg, S., Lynch, J.C., Maher, N., Megonigal, J., Reay, W.G., Siok, D., Starke, A., Turner, V., and Warren, S., 2020, Hurricane Sandy effects on coastal marsh elevation change: Estuaries and Coasts, v. 43, p. 1640-1657, https://doi.org/10.1007/s12237-020-00758-5.","productDescription":"18 p.","startPage":"1640","endPage":"1657","ipdsId":"IP-104537","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":490065,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarworks.wm.edu/vimsarticles/1998","text":"External Repository"},{"id":376123,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Delaware, Maine, Maryland, Massachusetts, New Jersey, New York, Rhode Island, Virginia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -76.39892578125,\n              37.125286284966805\n            ],\n            [\n              -75.03662109375,\n              37.59682400108367\n            ],\n            [\n          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Patrick","contributorId":228820,"corporation":false,"usgs":false,"family":"Megonigal","given":"J Patrick","affiliations":[{"id":41515,"text":"Smithsonian Env Res Ctr","active":true,"usgs":false}],"preferred":false,"id":792169,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Reay, William G.","contributorId":176767,"corporation":false,"usgs":false,"family":"Reay","given":"William","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":792170,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Siok, Drexel","contributorId":208065,"corporation":false,"usgs":false,"family":"Siok","given":"Drexel","email":"","affiliations":[{"id":37703,"text":"Delaware Department of Natural Resources and Environmental Control, Delaware Coastal Programs, Dover, Del","active":true,"usgs":false}],"preferred":false,"id":792171,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Starke, Adam","contributorId":208067,"corporation":false,"usgs":false,"family":"Starke","given":"Adam","email":"","affiliations":[{"id":37696,"text":"The Nature Conservancy, Cold Spring Harbor, NY","active":true,"usgs":false}],"preferred":false,"id":792172,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Turner, Vincent","contributorId":208070,"corporation":false,"usgs":false,"family":"Turner","given":"Vincent","email":"","affiliations":[{"id":37706,"text":"Edwin B. Forsythe National Wildlife Refuge, Oceanville, NJ","active":true,"usgs":false}],"preferred":false,"id":792173,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Warren, Scott","contributorId":228821,"corporation":false,"usgs":false,"family":"Warren","given":"Scott","email":"","affiliations":[{"id":41516,"text":"Connecticut College","active":true,"usgs":false}],"preferred":false,"id":792174,"contributorType":{"id":1,"text":"Authors"},"rank":23}]}}
,{"id":70210696,"text":"pp1842HH - 2020 - The effects of management practices on grassland birds—Baird’s Sparrow (<i>Centronyx bairdii</i>)","interactions":[{"subject":{"id":70210696,"text":"pp1842HH - 2020 - The effects of management practices on grassland birds—Baird’s Sparrow (<i>Centronyx bairdii</i>)","indexId":"pp1842HH","publicationYear":"2020","noYear":false,"chapter":"HH","displayTitle":"The Effects of Management Practices on Grassland Birds—Baird’s Sparrow (<i>Centronyx bairdii</i>)","title":"The effects of management practices on grassland birds—Baird’s Sparrow (<i>Centronyx bairdii</i>)"},"predicate":"IS_PART_OF","object":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"id":1}],"isPartOf":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"lastModifiedDate":"2023-12-20T21:07:44.827916","indexId":"pp1842HH","displayToPublicDate":"2020-06-18T16:00:21","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1842","chapter":"HH","displayTitle":"The Effects of Management Practices on Grassland Birds—Baird’s Sparrow (<i>Centronyx bairdii</i>)","title":"The effects of management practices on grassland birds—Baird’s Sparrow (<i>Centronyx bairdii</i>)","docAbstract":"<p>Keys to Baird’s Sparrow (<i>Centronyx bairdii</i>) management are providing native or tame grasslands with moderately deep litter, controlling excessive grazing, and curtailing shrub encroachment. Baird’s Sparrows have been reported to use habitats with less than or equal to (≤) 101 centimeters (cm) average vegetation height, 3–46 cm visual obstruction reading (VOR), 15–71 percent grass cover, 5–25 percent forb cover, ≤50 percent shrub cover, less than (&lt;) 44 percent bare ground, 10–63 percent litter cover, and ≤21 cm litter depth.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842HH","usgsCitation":"Shaffer, J.A., Igl, L.D., Johnson, D.H., Sondreal, M.L., Goldade, C.M., Nenneman, M.P., and Euliss, B.R., 2020, The effects of management practices on grassland birds—Baird’s Sparrow (<i>Centronyx bairdii</i>), chap. HH <i>of</i> Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 21 p., https://doi.org/10.3133/pp1842HH.","productDescription":"v, 21 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-097125","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":375658,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/hh/coverthb.jpg"},{"id":375659,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/hh/pp1842hh.pdf","text":"Report","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–HH"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/npwrc\" data-mce-href=\"https://www.usgs.gov/centers/npwrc\">Northern Prairie Wildlife Research Center</a> <br>U.S. Geological Survey<br>8711 37th Street Southeast <br>Jamestown, ND&nbsp;58401</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Capsule Statement</li><li>Breeding Range</li><li>Suitable Habitat</li><li>Area Requirements and Landscape Associations</li><li>Brood Parasitism by Cowbirds and Other Species</li><li>Breeding-Season Phenology and Site Fidelity</li><li>Species’ Response to Management</li><li>Management Recommendations from the Literature</li><li>References</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-06-18","noUsgsAuthors":false,"publicationDate":"2020-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Shaffer, Jill A. 0000-0003-3172-0708","orcid":"https://orcid.org/0000-0003-3172-0708","contributorId":223126,"corporation":false,"usgs":true,"family":"Shaffer","given":"Jill A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Igl, Lawrence D. 0000-0003-0530-7266","orcid":"https://orcid.org/0000-0003-0530-7266","contributorId":214801,"corporation":false,"usgs":true,"family":"Igl","given":"Lawrence D.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Douglas H. 0000-0002-7778-6641 douglas_h_johnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7778-6641","contributorId":1387,"corporation":false,"usgs":true,"family":"Johnson","given":"Douglas","email":"douglas_h_johnson@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791004,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sondreal, Marriah L.","contributorId":73532,"corporation":false,"usgs":true,"family":"Sondreal","given":"Marriah","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":791005,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goldade, Christopher M.","contributorId":90668,"corporation":false,"usgs":true,"family":"Goldade","given":"Christopher M.","affiliations":[],"preferred":false,"id":791006,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nenneman, Melvin P.","contributorId":190777,"corporation":false,"usgs":false,"family":"Nenneman","given":"Melvin","email":"","middleInitial":"P.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":791007,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Euliss, Betty R.","contributorId":191881,"corporation":false,"usgs":false,"family":"Euliss","given":"Betty","email":"","middleInitial":"R.","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":791008,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70211531,"text":"70211531 - 2020 - Modified QuEChERS extraction for the analysis of young-of-year smallmouth bass using GC × GC-TOFMS","interactions":[],"lastModifiedDate":"2020-07-30T16:47:05.265837","indexId":"70211531","displayToPublicDate":"2020-06-18T11:43:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":763,"text":"Analytical Methods","active":true,"publicationSubtype":{"id":10}},"title":"Modified QuEChERS extraction for the analysis of young-of-year smallmouth bass using GC × GC-TOFMS","docAbstract":"<div class=\"capsule__text\"><p>Signs of disease, such as external lesions, have been prevalent in smallmouth bass throughout the Susquehanna River Basin, USA. Previous targeted chemical studies in this system have identified known persistent organic pollutants, but a common explanatory link across multiple affected sites remains undetermined. A fast and robust extraction method that can be applied to young-of-year fish is needed to effectively screen for target and non-target compounds that may be impacting organism health. The quick, easy, cheap, effective, rugged, and safe (QuEChERS) extraction methodology was optimized to perform both targeted and non-targeted chemical analyses from a single extraction of whole young-of-year fish. Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) was used for extract analysis. Sample extraction was performed using the solvent ethyl acetate, followed by a two-step cleanup in which samples were frozen for lipid removal and subjected to dispersive solid phase extraction using Florisil. A sample of 21 young-of-year smallmouth bass collected from areas with disease and exhibiting different types of external lesions were evaluated for 233 target compounds. A total of 34 organic contaminants, including polychlorinated biphenyls, brominated diphenyl ethers, organochlorinated pesticides, and personal care products, were detected. Data from this sample set was then analyzed for non-targets. Using the Fisher ratio method and multivariate analysis, an additional 10 significant features were identified specific to either fish with visible lesions or with no visible disease characteristics.</p></div>","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D0AY00880J","usgsCitation":"Teehan, P., Schall, M.K., Blazer, V., Gruber, B., and Dorman, F.L., 2020, Modified QuEChERS extraction for the analysis of young-of-year smallmouth bass using GC × GC-TOFMS: Analytical Methods, v. 12, p. 3697-3704, https://doi.org/10.1039/D0AY00880J.","productDescription":"8 p.","startPage":"3697","endPage":"3704","ipdsId":"IP-118177","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":376913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","city":"Harrisburg","otherGeospatial":"Susquehanna River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -76.9379425048828,\n              40.20981886323868\n            ],\n            [\n              -76.83631896972655,\n              40.20981886323868\n            ],\n            [\n              -76.83631896972655,\n              40.30204709875983\n            ],\n            [\n              -76.9379425048828,\n              40.30204709875983\n            ],\n            [\n              -76.9379425048828,\n              40.20981886323868\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Teehan, Paige","contributorId":236874,"corporation":false,"usgs":false,"family":"Teehan","given":"Paige","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":794530,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schall, Megan K.","contributorId":115964,"corporation":false,"usgs":false,"family":"Schall","given":"Megan","email":"","middleInitial":"K.","affiliations":[{"id":17758,"text":"Pennsylvania State Univ.","active":true,"usgs":false}],"preferred":false,"id":794531,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794532,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gruber, Beate","contributorId":236875,"corporation":false,"usgs":false,"family":"Gruber","given":"Beate","email":"","affiliations":[{"id":47556,"text":"Research Institute for Chromatography, Belgium","active":true,"usgs":false}],"preferred":false,"id":794533,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dorman, Frank L","contributorId":236876,"corporation":false,"usgs":false,"family":"Dorman","given":"Frank","email":"","middleInitial":"L","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":794534,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70212520,"text":"70212520 - 2020 - Can nutrient additions facilitate recovery of Pacific salmon?","interactions":[],"lastModifiedDate":"2020-10-12T17:18:17.323586","indexId":"70212520","displayToPublicDate":"2020-06-18T09:41:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6455,"text":"Canadian Journal Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Can nutrient additions facilitate recovery of Pacific salmon?","docAbstract":"<div class=\"box-pad border-lightgray margin-bottom\"><div class=\"abstractSection\"><div class=\"abstractSection abstractInFull\"><p class=\"first last\"><span>Multiple restoration actions have been implemented in response to declining salmon populations. Among these is the addition of salmon carcasses or artificial nutrients to mimic marine-derived nutrients historically provided by large spawning runs of salmon. A key assumption in this approach is that increased nutrients will catalyze salmon population growth. Although effects on aquatic ecosystems have been observed during treatments, it is unclear whether permanent population increases for salmon will occur. To test this assumption and address associated uncertainties, we linked a food web model with a salmon life cycle model to examine whether carcass additions in a river reach would improve conditions for salmon in the long term. Model results confirmed immediate increases in the biomass of periphyton, macroinvertebrates, and fish during carcass additions. In turn, juvenile salmon grew larger and experienced improved freshwater and smolt survival, which translated to a greater number of adults returning to spawn. However, once additions ceased, salmon abundance returned to pretreatment levels, which, based on our model, is owing to a combination of instream and out-of-basin factors. Overall, results of this work suggest that benefits during carcass and nutrient additions may not translate into persistent productivity of salmon unless additions are sustained indefinitely or other limiting factors are addressed.</span></p></div></div></div>","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0438","usgsCitation":"Benjamin, J.R., Bellmore, J.R., Whitney, E., and Dunham, J.B., 2020, Can nutrient additions facilitate recovery of Pacific salmon?: Canadian Journal Fisheries and Aquatic Sciences, v. 77, p. 1601-1611, https://doi.org/10.1139/cjfas-2019-0438.","productDescription":"11 p.","startPage":"1601","endPage":"1611","ipdsId":"IP-113407","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":500999,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/101959","text":"External 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0000-0002-5140-6460","orcid":"https://orcid.org/0000-0002-5140-6460","contributorId":195609,"corporation":false,"usgs":false,"family":"Bellmore","given":"James","email":"","middleInitial":"R","affiliations":[],"preferred":false,"id":796665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitney, Emily","contributorId":238834,"corporation":false,"usgs":false,"family":"Whitney","given":"Emily","affiliations":[{"id":16298,"text":"University of Alaska Southeast","active":true,"usgs":false}],"preferred":false,"id":796666,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":796667,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70228727,"text":"70228727 - 2020 - Estimating the drivers of species distributions with opportunistic data using mediation analysis","interactions":[],"lastModifiedDate":"2022-02-17T15:15:00.528851","indexId":"70228727","displayToPublicDate":"2020-06-18T09:07:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the drivers of species distributions with opportunistic data using mediation analysis","docAbstract":"<p><span>Ecological occupancy modeling has historically relied on high-quality, low-quantity designed-survey data for estimation and prediction. In recent years, there has been a large increase in the amount of high-quantity, unknown-quality opportunistic data. This has motivated research on how best to combine these two data sources in order to optimize inference. Existing methods can be infeasible for large datasets or require opportunistic data to be located where designed-survey data exist. These methods map species occupancies, motivating a need to properly evaluate covariate effects (e.g., land cover proportion) on their distributions. We describe a spatial estimation method for supplementarily including additional opportunistic data using mediation analysis concepts. The opportunistic data mediate the effect of the covariate on the designed-survey data response, decomposing it into a direct and indirect effect. A component of the indirect effect can then be quickly estimated via regressing the mediator on the covariate, while the other components are estimated through a spatial occupancy model. The regression step allows for use of large quantities of opportunistic data that can be collected in locations with no designed-survey data available. Simulation results suggest that the mediated method produces an improvement in relative MSE when the data are of reasonable quality. However, when the simulated opportunistic data are poorly correlated with the true spatial process, the standard, unmediated method is still preferable. A spatiotemporal extension of the method is also developed for analyzing the effect of deciduous forest land cover on red-eyed vireo distribution in the southeastern United States and find that including the opportunistic data do not lead to a substantial improvement. Opportunistic data quality remains an important consideration when employing this method, as with other data integration methods.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3165","usgsCitation":"Huberman, D.B., Reich, B.J., Pacifici, K., and Collazo, J.A., 2020, Estimating the drivers of species distributions with opportunistic data using mediation analysis: Ecosphere, v. 11, no. 6, e03165, 13 p., https://doi.org/10.1002/ecs2.3165.","productDescription":"e03165, 13 p.","ipdsId":"IP-113854","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456360,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3165","text":"Publisher Index Page"},{"id":396100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.03125,\n              24.84656534821976\n            ],\n            [\n              -66.09375,\n              24.84656534821976\n            ],\n            [\n              -66.09375,\n              49.26780455063753\n            ],\n            [\n              -97.03125,\n              49.26780455063753\n            ],\n            [\n              -97.03125,\n              24.84656534821976\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"11","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Huberman, D. B.","contributorId":279615,"corporation":false,"usgs":false,"family":"Huberman","given":"D.","email":"","middleInitial":"B.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835221,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reich, B. J.","contributorId":279616,"corporation":false,"usgs":false,"family":"Reich","given":"B.","email":"","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pacifici, Krishna","contributorId":244494,"corporation":false,"usgs":false,"family":"Pacifici","given":"Krishna","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835223,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835224,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70210681,"text":"sir20205045 - 2020 - Modeling <i>Escherichia coli</i> in the Missouri River near Omaha, Nebraska, 2012–16","interactions":[],"lastModifiedDate":"2020-06-18T14:21:59.738964","indexId":"sir20205045","displayToPublicDate":"2020-06-17T15:15:22","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5045","displayTitle":"Modeling <i>Escherichia coli</i> in the Missouri River near Omaha, Nebraska, 2012–16","title":"Modeling <i>Escherichia coli</i> in the Missouri River near Omaha, Nebraska, 2012–16","docAbstract":"<p>The city of Omaha, Nebraska, has a combined sewer system in some areas of the city. In Omaha, Nebr., a moderate amount of rainfall will lead to the combination of stormwater and untreated sewage or wastewater being discharged directly into the Missouri River and Papillion Creek and is called a combined sewer overflow (CSO) event. In 2009, the city of Omaha began the implementation of their Long Term Control Plan (LTCP) to mitigate the effects of CSOs on the Missouri River and Papillion Creek. As part of the LTCP, the city partnered with the U.S. Geological Survey (USGS) in 2012 to begin monitoring in the Missouri River. Since 2012, monthly discrete water-quality samples for many constituents have been collected from the Missouri River at four sites. At 3 of the 4 sites, water quality has been monitored continuously for selected constituents and physical properties. These discrete water-quality samples and continuous water-quality monitoring data (from July 2012 to 2020) have been collected to better understand the water quality of the Missouri River, how it is changing with time, how it changes upstream from the city of Omaha to downstream, and how it varies during base-flow conditions and during periods of runoff.</p><p>The purpose of this report is to document the development of <i>Escherichia coli</i> (<i>E. coli</i>) concentration models for these four Missouri River sites. Analysis was completed using the first 5 years of data (through 2016) to determine if the current approach is sufficient to meet future analysis goals and to understand if proposed models such as Load Estimator (LOADEST) models will be able to represent water-quality changes in the Missouri River.</p><p>Multiple linear regression models were developed to estimate <i>E. coli</i> concentration using LOADEST as implemented in the rloadest package in the R statistical software program. A set of explanatory variables, including streamflow and streamflow anomalies, precipitation, information about CSOs, and continuous water quality, were evaluated for potential inclusion in regression models. The best model at Missouri River at NP Dodge Park at Omaha, Nebr. (USGS station 412126095565201; hereafter “NP Dodge”) included basin explanatory variables of upstream antecedent precipitation index measured at Tekamah, Nebr.; decimal time; season; and turbidity. The best model at Missouri River at Freedom Park Omaha, Nebr. (USGS station 411636095535401; hereafter “Freedom Park”) included the same explanatory variables as the NP Dodge model with the addition of turbidity anomalies and flow anomalies. The best models at the two downstream sites (Missouri River near Council Bluffs, Iowa, USGS station 06610505 and Missouri River near La Platte, Nebr., USGS station 410333095530101) included the same explanatory variables as the Freedom Park model with the addition of local antecedent precipitation index as measured at Eppley Airport in Omaha, Nebr., and additional turbidity and flow anomalies. The final selected models were the best models given our modeling design constraint in which explanatory variables included in the model for the upstream site were included in the downstream models.</p><p>Explanatory variables currently (2020) being collected and included in the selected models through 2016 explained 64–75 percent of the variability of <i>E. coli</i> concentration in the Missouri River. Explaining 64–75 percent of the variability might be considered low when working with physical constituents (total nitrogen or sediment), but with the natural variability of biological constituents such as <i>E. coli</i>, the uncertainty of <i>E. coli</i> laboratory measurements, and the added complexity of modeling in a large drainage basin with multiple sources, these results are adequate and indicate that the explanatory variables being collected and models such as LOADEST can represent water-quality changes in the Missouri River for <i>E. coli</i> concentration from 2012 to 2016.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205045","collaboration":"Prepared in cooperation with the city of Omaha, Nebraska","usgsCitation":"Densmore, B.K., Hall, B.M., and Moser, M.T., 2020, Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16: U.S. Geological Survey Scientific Investigations Report 2020–5045, 24 p.,  https://doi.org/10.3133/sir20205045.","productDescription":"Report: vi, 24 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-098296","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":375621,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5045/coverthb.jpg"},{"id":375622,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5045/sir20205045.pdf","text":"Report","size":"9.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5045"},{"id":375623,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97S6WSV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Modeling <i>Escherichia coli</i> in the Missouri River near Omaha, Nebraska, 2012–16: Model Inputs and Outputs"}],"country":"United States","state":"Nebraska","city":"Omaha","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -96.23611450195312,\n              41.166249339092\n            ],\n            [\n              -95.78155517578124,\n              41.15901221836655\n            ],\n            [\n              -95.7843017578125,\n              41.37783904584602\n            ],\n            [\n              -95.95321655273436,\n              41.37886950966323\n            ],\n            [\n              -96.23611450195312,\n              41.37165592008984\n            ],\n            [\n              -96.23611450195312,\n              41.166249339092\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/ne-water\" href=\"https://www.usgs.gov/centers/ne-water\">Nebraska Water Science Center</a> <br>U.S. Geological Survey<br>5231 South 19th Street <br>Lincoln, NE 68512</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods of Study</li><li>Missouri River <i>Escherichia Coli</i> Concentration Model Results</li><li>Model Capabilities and Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-06-17","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Densmore, Brenda K. 0000-0003-2429-638X bdensmore@usgs.gov","orcid":"https://orcid.org/0000-0003-2429-638X","contributorId":4896,"corporation":false,"usgs":true,"family":"Densmore","given":"Brenda","email":"bdensmore@usgs.gov","middleInitial":"K.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hall, Brent M. 0000-0003-3815-5158 bhall@usgs.gov","orcid":"https://orcid.org/0000-0003-3815-5158","contributorId":4547,"corporation":false,"usgs":true,"family":"Hall","given":"Brent","email":"bhall@usgs.gov","middleInitial":"M.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790928,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moser, Matthew T. 0000-0002-4891-3381","orcid":"https://orcid.org/0000-0002-4891-3381","contributorId":94994,"corporation":false,"usgs":true,"family":"Moser","given":"Matthew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":790929,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70210708,"text":"70210708 - 2020 - Comparing trends in modeled and observed streamflows at minimally altered basins in the United States","interactions":[],"lastModifiedDate":"2020-06-19T15:59:45.860026","indexId":"70210708","displayToPublicDate":"2020-06-17T09:43:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Comparing trends in modeled and observed streamflows at minimally altered basins in the United States","docAbstract":"We compared modeled and observed streamflow trends from 1984–2016 using five statistical transfer models and one deterministic, distributed-parameter, process-based model, for 26 flow metrics at 502 basins in the United States that are minimally influenced by development. We also looked at a measure of overall model fit and average bias. A higher percentage of basins, for all models, had relatively low trend differences between modeled and observed mean/ median flows than for very high or low flows such as the annual 1-day high and 7-day low flows. Mean-flow metrics also had the largest percentage of basins with relatively good overall model fit and low bias. The five statistical transfer models performed better at more basins than the process-based model. The overall model fit for all models, for mean and/or high flows, was correlated with one or more measures of basin precipitation or aridity. Our study and previous studies generally observed good model performance for high flows up to 90th or 95th percentile flows. However, we found model performance was substantially worse for more extreme flows, including 99th percentile and annual 1-day high flows; this shows the importance of including more extreme high flows in analyses of model performance.","language":"English","publisher":"MDPI","doi":"10.3390/w12061728","usgsCitation":"Hodgkins, G.A., Dudley, R., Russell, A.M., and LaFontaine, J.H., 2020, Comparing trends in modeled and observed streamflows at minimally altered basins in the United States: Water, v. 12, 1728, 20 p., https://doi.org/10.3390/w12061728.","productDescription":"1728, 20 p.","ipdsId":"IP-117596","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":456370,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12061728","text":"Publisher Index Page"},{"id":436926,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XT4WSP","text":"USGS data release","linkHelpText":"Cross-validation results for five statistical methods of daily streamflow estimation at 1,385 reference streamgages in the conterminous United States, Water Years 1981-2017"},{"id":375682,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n            [\n              [\n                -94.81758,\n                49.38905\n              ],\n              [\n                -94.64,\n                48.84\n              ],\n              [\n                -94.32914,\n                48.67074\n              ],\n              [\n                -93.63087,\n                48.60926\n              ],\n              [\n                -92.61,\n                48.45\n              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\"name\": \"United States\"\n      }\n    }\n  ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791051,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dudley, Robert W. 0000-0002-0934-0568","orcid":"https://orcid.org/0000-0002-0934-0568","contributorId":220211,"corporation":false,"usgs":true,"family":"Dudley","given":"Robert W.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791052,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Russell, Amy M. 0000-0003-0582-0094 arussell@usgs.gov","orcid":"https://orcid.org/0000-0003-0582-0094","contributorId":200011,"corporation":false,"usgs":true,"family":"Russell","given":"Amy","email":"arussell@usgs.gov","middleInitial":"M.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791053,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"LaFontaine, Jacob H. 0000-0003-4923-2630 jlafonta@usgs.gov","orcid":"https://orcid.org/0000-0003-4923-2630","contributorId":2258,"corporation":false,"usgs":true,"family":"LaFontaine","given":"Jacob","email":"jlafonta@usgs.gov","middleInitial":"H.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791054,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70211183,"text":"70211183 - 2020 - Quantifying gas emissions associated with the 2018 rift eruption of Kīlauea Volcano using ground-based DOAS measurements","interactions":[],"lastModifiedDate":"2020-07-16T17:45:55.970993","indexId":"70211183","displayToPublicDate":"2020-06-17T09:30:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying gas emissions associated with the 2018 rift eruption of Kīlauea Volcano using ground-based DOAS measurements","docAbstract":"<p><span>Starting on 3 May 2018, a series of eruptive fissures opened in Kīlauea Volcano’s lower East Rift Zone (LERZ). Over the course of the next 3 months, intense degassing accompanied lava effusion from these fissures. Here, we report on ground-based observations of the gas emissions associated with Kīlauea’s 2018 eruption. Visual observations combined with radiative transfer modeling show that ultraviolet light could not efficiently penetrate the gas and aerosol plume in the LERZ, complicating SO</span><sub>2</sub><span>&nbsp;measurements by differential optical absorption spectroscopy (DOAS). By applying a statistical method that integrates a radiative transfer model with the DOAS retrievals, we were able to calculate sulfur dioxide (SO</span><sub>2</sub><span>) emission rates along with estimates of their uncertainty. We find that sustained SO</span><sub>2</sub><span>&nbsp;emissions were highest in June and early July, when approximately 200 kt SO</span><sub>2</sub><span>&nbsp;were emitted daily. At&nbsp;the 68% confidence interval, we estimate that 7.1–13.6 Mt SO</span><sub>2</sub><span>&nbsp;were released from the LERZ during the entire May to September eruptive episode. Scaling our results with in situ measurements of plume composition, we calculate that 11–21 Mt H</span><sub>2</sub><span>O and 1.5–2.8 Mt CO</span><sub>2</sub><span>&nbsp;were also emitted. The gas and aerosol emissions caused hazardous conditions in areas proximal to the active vents, but plume dispersion modeling shows that the eruption also significantly impacted air quality hundreds of kilometers downwind. Combined with petrologic studies of the erupted lavas, our measurements indicate that 1.1–2.3 km</span><sup>3</sup><span>&nbsp;dense-rock equivalent of lava were erupted from the LERZ, which is approximately twice the concomitant collapse volume of the volcano’s summit.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00445-020-01390-8","usgsCitation":"Kern, C., Lerner, A., Elias, T., Nadeau, P.A., Holland, L., Kelly, P.J., Werner, C., Clor, L., and Cappos, M., 2020, Quantifying gas emissions associated with the 2018 rift eruption of Kīlauea Volcano using ground-based DOAS measurements: Bulletin of Volcanology, v. 82, 55, 24 p., https://doi.org/10.1007/s00445-020-01390-8.","productDescription":"55, 24 p.","ipdsId":"IP-115197","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":436927,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LXBJF3","text":"USGS data release","linkHelpText":"Differential Optical Absorption Spectroscopy data acquired during the 2018 rift eruption of Kilauea Volcano"},{"id":376424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.3082275390625,\n              19.379170499941292\n            ],\n            [\n              -155.2333831787109,\n              19.379170499941292\n            ],\n            [\n              -155.2333831787109,\n              19.449111649832837\n            ],\n            [\n              -155.3082275390625,\n              19.449111649832837\n            ],\n            [\n              -155.3082275390625,\n              19.379170499941292\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Kern, Christoph 0000-0002-8920-5701 ckern@usgs.gov","orcid":"https://orcid.org/0000-0002-8920-5701","contributorId":3387,"corporation":false,"usgs":true,"family":"Kern","given":"Christoph","email":"ckern@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":792994,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lerner, Allan 0000-0001-7208-1493","orcid":"https://orcid.org/0000-0001-7208-1493","contributorId":215623,"corporation":false,"usgs":false,"family":"Lerner","given":"Allan","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":792995,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elias, Tamar 0000-0002-9592-4518 telias@usgs.gov","orcid":"https://orcid.org/0000-0002-9592-4518","contributorId":3916,"corporation":false,"usgs":true,"family":"Elias","given":"Tamar","email":"telias@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":792996,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nadeau, Patricia A. 0000-0002-6732-3686","orcid":"https://orcid.org/0000-0002-6732-3686","contributorId":215616,"corporation":false,"usgs":true,"family":"Nadeau","given":"Patricia","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":792997,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holland, Lacey","contributorId":147879,"corporation":false,"usgs":false,"family":"Holland","given":"Lacey","email":"","affiliations":[{"id":16953,"text":"University of Utah, Atmospheric Sciences","active":true,"usgs":false}],"preferred":false,"id":792998,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":792999,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Werner, Cynthia 0000-0003-3311-6694","orcid":"https://orcid.org/0000-0003-3311-6694","contributorId":224428,"corporation":false,"usgs":false,"family":"Werner","given":"Cynthia","affiliations":[{"id":37768,"text":"USGS Contractor","active":true,"usgs":false}],"preferred":false,"id":793000,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Clor, Laura E. 0000-0003-2633-5100","orcid":"https://orcid.org/0000-0003-2633-5100","contributorId":209969,"corporation":false,"usgs":true,"family":"Clor","given":"Laura E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":793001,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cappos, Michael 0000-0001-9883-1475","orcid":"https://orcid.org/0000-0001-9883-1475","contributorId":215607,"corporation":false,"usgs":true,"family":"Cappos","given":"Michael","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":793002,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70212476,"text":"70212476 - 2020 - Assessing the value of removing earthquake-hazard-related epistemic uncertainties, exemplified using average annual loss in California","interactions":[],"lastModifiedDate":"2020-11-30T16:34:15.09351","indexId":"70212476","displayToPublicDate":"2020-06-17T09:05:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the value of removing earthquake-hazard-related epistemic uncertainties, exemplified using average annual loss in California","docAbstract":"<p><span>To aid in setting scientific research priorities, we assess the potential value of removing each of the epistemic uncertainties currently represented in the US Geological Survey California seismic-hazard model, using average annual loss (AAL) as the risk metric of interest. Given all the uncertainties, represented with logic-tree branches, we find a mean AAL of $3.94 billion. The modal value is 17.5% lower than the mean, and there is a 78% chance that the true AAL value is more than 10% away from the mean, and a 5% chance that it is a factor 2.1 greater or lower than the mean. We quantify the extent to which resolving each uncertainty improves the AAL estimate. The most influential branch is one that adds additional epistemic uncertainty to ground motion models, but others are found to be influential as well, such as the rate of M ≥ 5 events throughout the region. We discuss the broader implications of our findings, and note that the time dependence caused by spatiotemporal clustering can be much more influential on AAL than the epistemic uncertainties explored here.</span></p>","language":"English","publisher":"Sage Journals","doi":"10.1177/8755293020926185","usgsCitation":"Field, E., Milner, K.R., and Porter, K., 2020, Assessing the value of removing earthquake-hazard-related epistemic uncertainties, exemplified using average annual loss in California: Earthquake Spectra, v. 36, no. 4, p. 1912-1929, https://doi.org/10.1177/8755293020926185.","productDescription":"18 p.","startPage":"1912","endPage":"1929","ipdsId":"IP-117774","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":377560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70208133,"text":"sir20195144 - 2020 - Small basin annual yield and percentage of snowmelt runoff in North Dakota, 1931–2016","interactions":[],"lastModifiedDate":"2020-06-17T14:21:21.015204","indexId":"sir20195144","displayToPublicDate":"2020-06-17T07:36:04","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5144","displayTitle":"Small Basin Annual Yield and Percentage of Snowmelt Runoff in North Dakota, 1931–2016","title":"Small basin annual yield and percentage of snowmelt runoff in North Dakota, 1931–2016","docAbstract":"<p>The North Dakota hydrology manual prepared by the U.S. Department of Agriculture, Soil Conservation Service, presents methodologies primarily used for developing hydrology for onfarm conservation practices, watershed projects, Resource Conservation and Development project measures, and river basin studies. The manual includes data necessary for determining hydrologic factors and developing a design discharge for a given site and intended purpose. The U.S. Geological Survey, in cooperation with the North Dakota Natural Resources Conservation Service, developed methods to reproduce and update the annual yield maps for chapter 7 of the North Dakota hydrology manual. Annual yields, in acre-feet per square mile, for the 50- and 80-percent exceedance probabilities and expected percentage of snowmelt runoff isolines were estimated using U.S. Geological Survey streamflow data from 1931 to 2016 for 71 selected streamgages with drainage areas of 505 square miles or less. An application of a modified Maintenance of Variance Extension Type III was used to estimate missing annual streamflow volumes. An alternate expected percentage of snowmelt runoff isolines was estimated using High Plains Climatic Center precipitation and snowmelt data from 1931 to 2016 for 85 selected sites. The final expected percentage of snowmelt runoff isolines was estimated using streamflow data instead of precipitation and snowfall depth data. A snowmelt runoff seasonal period of March–May produced better isoline slopes than a November–May runoff seasonal period. Slopes of the expected percentage of snowmelt runoff isolines were sensitive to amounts of missing record. Suitable isoline slopes appeared when the missing record was set to 50 percent (43 years) and 66 percent (57 years) for the 86-year period of 1931–2016.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195144","collaboration":"Prepared in cooperation with the Natural Resources Conservation Service—North Dakota","usgsCitation":"Williams-Sether, T., and Wheeling, S.L., 2020, Small basin annual yield and percentage of snowmelt runoff in North Dakota, 1931–2016: U.S. Geological Survey Scientific Investigations Report 2019–5144, 37 p., https://doi.org/10.3133/sir20195144.","productDescription":"Report: vii, 38 p.; Dataset; 2 Appendixes","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-104356","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":375620,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144.pdf","text":"Report","size":"6.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5144"},{"id":375416,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5144/coverthb.jpg"},{"id":375418,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144_appendix_1.xlsx","text":"Appendix 1","size":"136 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5144 Appendix 1","linkHelpText":"—Table 1.1. Example data and computations for U.S. Geological Survey station 05056100"},{"id":375419,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144_appendix_2.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2019–5144 Appendix 2","linkHelpText":"—R Code Script and Supporting Data for the Modified Maintenance of Variance Extension Type III, MOVE.3, Application"},{"id":375420,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System","description":"USGS Data Release","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"North 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Dakota\",\"nation\":\"USA  \"}}]}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/dakota-water\" href=\"https://www.usgs.gov/centers/dakota-water\">Dakota Water Science Center</a> <br>U.S. Geological Survey<br>821 East Interstate Avenue<br>Bismarck, ND 58503–1608 <br>Mountain View Road<br>Rapid City, SD 57702 </p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction and Background</li><li>Purpose and Scope</li><li>Methods</li><li>Annual Yields and Percentage of Snowmelt Runoff</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Methods Used to Generate and Adjust Annual Streamflow Volumes Used in Move.3</li><li>Appendix 2. R Code Script and Supporting Data for the Modified Maintenance of Variance Extension Type III, MOVE.3, Application</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-06-17","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams-Sether, Tara 0000-0001-6515-9416","orcid":"https://orcid.org/0000-0001-6515-9416","contributorId":214143,"corporation":false,"usgs":true,"family":"Williams-Sether","given":"Tara","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":780663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wheeling, Spencer L. 0000-0003-4411-6526","orcid":"https://orcid.org/0000-0003-4411-6526","contributorId":221899,"corporation":false,"usgs":true,"family":"Wheeling","given":"Spencer","email":"","middleInitial":"L.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":780664,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70227644,"text":"70227644 - 2020 - Geochemical modeling of iron and aluminum precipitation during mixing and neutralization of acid mine drainage","interactions":[],"lastModifiedDate":"2022-01-24T13:13:01.142534","indexId":"70227644","displayToPublicDate":"2020-06-17T07:09:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical modeling of iron and aluminum precipitation during mixing and neutralization of acid mine drainage","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">Geochemical modeling of precipitation reactions in the complex matrix of acid mine drainage is fundamental to understanding natural attenuation, lime treatment, and treatment procedures that separate constituents for potential reuse or recycling. The three main dissolved constituents in acid mine drainage are iron, aluminum, and sulfate. During the neutralization of acid mine drainage (AMD) by mixing with clean tributaries or by titration with a base such as sodium hydroxide or slaked lime, Ca(OH)<sub>2</sub>, iron precipitates at pH values of 2–3 if oxidized and aluminum precipitates at pH values of 4–5 and both processes buffer the pH during precipitation. Mixing processes were simulated using the ion-association model in the PHREEQC code. The results are sensitive to the solubility product constant (K<sub>sp</sub>) used for the precipitating phases. A field example with data on discharge and water composition of AMD before and after mixing along with massive precipitation of an aluminum phase is simulated and shows that there is an optimal K<sub>sp</sub><span>&nbsp;</span>to give the best fit to the measured data. Best fit is defined when the predicted water composition after mixing and precipitation matches most closely the measured water chemistry. Slight adjustment to the proportion of stream discharges does not give a better fit.</div>","language":"English","publisher":"MDPI","doi":"10.3390/min10060547","usgsCitation":"Nordstrom, D.K., 2020, Geochemical modeling of iron and aluminum precipitation during mixing and neutralization of acid mine drainage: Minerals, v. 10, no. 6, 547, 12 p., https://doi.org/10.3390/min10060547.","productDescription":"547, 12 p.","ipdsId":"IP-119702","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":456374,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min10060547","text":"Publisher Index Page"},{"id":394754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Alpine County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-119.9025,38.9332],[-119.8471,38.8948],[-119.8261,38.8802],[-119.8223,38.8775],[-119.7899,38.8554],[-119.7873,38.8541],[-119.752,38.8297],[-119.743,38.8235],[-119.7285,38.8132],[-119.7033,38.7953],[-119.678,38.7781],[-119.6166,38.7357],[-119.6088,38.7303],[-119.5977,38.7226],[-119.5771,38.7084],[-119.5847,38.7029],[-119.5883,38.698],[-119.5912,38.6907],[-119.5965,38.6762],[-119.6024,38.6703],[-119.6118,38.668],[-119.6159,38.6649],[-119.6159,38.6585],[-119.6159,38.6458],[-119.6165,38.6345],[-119.6194,38.6254],[-119.6223,38.6159],[-119.6188,38.6059],[-119.6129,38.5991],[-119.5987,38.5924],[-119.597,38.5919],[-119.5958,38.5842],[-119.5928,38.5769],[-119.5893,38.5706],[-119.5869,38.5674],[-119.5857,38.5552],[-119.5781,38.5484],[-119.5734,38.5452],[-119.5687,38.5421],[-119.5657,38.5298],[-119.5592,38.5239],[-119.5551,38.5162],[-119.5592,38.5103],[-119.5586,38.5049],[-119.5557,38.5026],[-119.5498,38.5022],[-119.5427,38.4986],[-119.5451,38.4895],[-119.5427,38.4832],[-119.5463,38.4759],[-119.5527,38.4718],[-119.5574,38.4682],[-119.558,38.4591],[-119.5603,38.4446],[-119.5627,38.4378],[-119.5692,38.4356],[-119.5674,38.4283],[-119.5662,38.4247],[-119.5644,38.4215],[-119.5621,38.4165],[-119.5627,38.4093],[-119.5674,38.4056],[-119.5762,38.4034],[-119.582,38.4007],[-119.5856,38.397],[-119.5991,38.4047],[-119.6026,38.4038],[-119.6061,38.4011],[-119.6132,38.4015],[-119.6214,38.3961],[-119.6208,38.3911],[-119.6125,38.373],[-119.6078,38.3698],[-119.6084,38.3671],[-119.6149,38.3662],[-119.6248,38.3594],[-119.6342,38.3544],[-119.6301,38.3476],[-119.633,38.3421],[-119.6313,38.3372],[-119.6348,38.3308],[-119.6354,38.3294],[-119.6365,38.3286],[-119.6594,38.3461],[-119.6986,38.3652],[-119.6924,38.3791],[-119.6967,38.4089],[-119.708,38.4177],[-119.751,38.4168],[-119.7686,38.4067],[-119.7785,38.4078],[-119.7836,38.4051],[-119.8006,38.4015],[-119.8133,38.3877],[-119.8362,38.3826],[-119.8529,38.3728],[-119.8689,38.3678],[-119.8793,38.3597],[-120.0169,38.4363],[-120.0528,38.4534],[-120.0727,38.4478],[-120.0719,38.4936],[-120.072,38.5013],[-120.0727,38.5117],[-120.0719,38.5684],[-120.0729,38.611],[-120.0732,38.6554],[-120.0741,38.7016],[-119.9685,38.7746],[-119.9567,38.7796],[-119.9479,38.7842],[-119.9444,38.7887],[-119.942,38.7919],[-119.9391,38.7947],[-119.9439,38.8028],[-119.9368,38.8096],[-119.9292,38.8183],[-119.9215,38.8233],[-119.9239,38.8283],[-119.9216,38.8323],[-119.911,38.8342],[-119.908,38.8365],[-119.9074,38.8383],[-119.9075,38.8469],[-119.9052,38.8569],[-119.8969,38.8564],[-119.8904,38.8578],[-119.8833,38.8637],[-119.8792,38.866],[-119.8775,38.8692],[-119.8775,38.8724],[-119.8781,38.8746],[-119.887,38.8805],[-119.8799,38.8855],[-119.8841,38.8964],[-119.8806,38.9032],[-119.8854,38.9145],[-119.8985,38.9226],[-119.9003,38.9262],[-119.9025,38.9332]]]},\"properties\":{\"name\":\"Alpine\",\"state\":\"CA\"}}]}","volume":"10","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Nordstrom, D. 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,{"id":70210673,"text":"sir20205040 - 2020 - Missouri StreamStats—St. Louis County and the City of St. Louis urban application","interactions":[],"lastModifiedDate":"2020-06-16T20:33:11.424554","indexId":"sir20205040","displayToPublicDate":"2020-06-16T09:37:03","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5040","displayTitle":"Missouri StreamStats—St. Louis County and the City of St. Louis Urban Application","title":"Missouri StreamStats—St. Louis County and the City of St. Louis urban application","docAbstract":"<p>To address a major limitation of the functionality of the Missouri statewide StreamStats application in the urban areas of St. Louis County and the City of St. Louis, Missouri, the U.S. Geological Survey, in cooperation with the Metropolitan St. Louis Sewer District, defined watershed boundaries and hydrography for the study area using high-resolution 3-meter digital elevation data derived from light detection and ranging sources, high-resolution 6-inch imagery, and storm sewer network geospatial data. The combined sanitary sewers, a part of the storm sewer network, were integrated into the open channel hydrography and elevation data using a new Arc Hydro stormwater tool developed to facilitate the incorporation of the combined sanitary sewer network into the StreamStats application.</p><p>The combined sanitary sewer network was edited for connectivity and flow direction before integration into the Missouri-St. Louis StreamStats application. Inlet structures in the geospatial data were defined as HydroJunction features that allow for stormwater runoff to enter the combined sanitary sewer network. An Arc Hydro stormwater processing workflow and a sewershed delineation tool were developed to integrate the combined sanitary sewer network with the hydrographic dataset and digital elevation model in the study area.</p><p>The StreamStats application developed for the study area provides various data exploration tools that can be used to examine the spatial data and to obtain general descriptive information and flow statistics at streamgages in the study area. Watersheds and sewersheds can be delineated and basin characteristics can be determined at any point on the open channel network or the combined sanitary sewer network in the study area. Peak-flow statistics can be computed at any point on the open channel network. A report summarizing the results is generated by the StreamStats application and can be downloaded and used in other software.</p><p>The Missouri-St. Louis StreamStats application is limited to the area inside St. Louis County and the City of St. Louis and excludes locations on the main stem of the Mississippi, Missouri, and Meramec Rivers. The limitations of the Missouri-St. Louis StreamStats application include possible inaccuracies using regression equations for peak-flow statistics developed assuming natural flow conditions and topographically derived watersheds determined from a coarser resolution of data than is used in this application. Additionally, published regression equations for peak-flow statistics did not incorporate any pipe flow or sewershed delineations when they were developed, which limits the applicability of peak-flow statistics to basins based on primarily topographic delineation. Inaccuracies in resolution, completeness, location, or attribution of geospatial elevation data, hydrographic data, derived stream lines, derived watershed boundaries, and combined sanitary sewer data can limit the accuracy and functionality of the Missouri-St. Louis StreamStats application.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205040","collaboration":"Prepared in cooperation with the Metropolitan St. Louis Sewer District","usgsCitation":"Southard, R.E., Haluska, T., Richards, J.M., Ellis, J.T., Dartiguenave, C., and Djokic, D., 2020, Missouri StreamStats—St. Louis County and the City of St. Louis urban application: U.S. Geological Survey Scientific Investigations Report 2020–5040, 27 p., https://doi.org/10.3133/sir20205040.","productDescription":"Report: vii, 27 p.; Appendix; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-098907","costCenters":[{"id":36532,"text":"Central Midwest Water Science 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Louis\",\"state\":\"MO\"}}]}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/cm-water\" href=\"https://www.usgs.gov/centers/cm-water\">Central Midwest Water Science Center</a> <br>U.S. Geological Survey<br>1400 Independence Road <br>Rolla, MO 65401 </p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Missouri-St. Louis StreamStats Application Functions</li><li>Summary</li><li>References Cited</li><li>Appendix</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-06-16","noUsgsAuthors":false,"publicationDate":"2020-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Southard, Rodney E. 0000-0001-8024-9698 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,{"id":70198567,"text":"sir20185104 - 2020 - Conceptual framework and approach for conducting a geoenvironmental assessment of undiscovered uranium resources","interactions":[],"lastModifiedDate":"2020-06-16T14:09:59.229883","indexId":"sir20185104","displayToPublicDate":"2020-06-16T09:20:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5104","displayTitle":"Conceptual Framework and Approach for Conducting a Geoenvironmental Assessment of Undiscovered Uranium Resources","title":"Conceptual framework and approach for conducting a geoenvironmental assessment of undiscovered uranium resources","docAbstract":"<p>This report presents a novel conceptual framework and approach for conducting a geologically based environmental assessment, or geoenvironmental assessment, of undiscovered uranium resources within an area likely to contain uranium deposits. The framework is based on a source-to-receptor model that prioritizes the most likely contaminant sources, contaminant pathways, and affected environmental media for three common uranium extraction methods—open pit or underground mining with milling and in situ recovery (ISR). Data on regional geology, hydrology, and climate, as well as historical uranium mining and milling records are used to estimate the probable amounts of waste rock, tailings, wastewater, surface land disturbance, and subsurface aquifer disturbance for likely mining methods. Constituents of concern that might take the form of leachates, dust, radon, and sediments formed by chemical and physical weathering are also identified in the geoenvironmental assessment. Finally, areas where constituents of concern are likely to occur and persist in air, land, surface water, and groundwater are indicated by the potential for dispersion of dust by wind, accumulation of radon because of air stagnation, dispersion of sediments and wastewater by runoff, and infiltration of wastewater or leachates with consideration of the likely mobility of contaminants in surface water and groundwater. The geoenvironmental assessment output can be summarized in the following primary products: (1) a descriptive geoenvironmental model; (2) maps and statistics of variables that indicate the potential for constituents of concern to occur and persist in air, land, surface water, and groundwater within a tract that is geologically permissive for the occurrence of uranium; and (3) tables providing estimated or indicated quantities of waste rock, tailings, wastewater, dust, and radon emissions that could be associated with undiscovered uranium resources, if extracted, for each permissive tract. The uranium geoenvironmental assessment could help natural resource managers to prioritize and (or) identify (1) important potential contaminant pathways, (2) management practices required depending on the types of constituents that could be of concern, (3) areas for response in the event of accidental release, and (4) future directions for study. Furthermore, indicators of rock and water volumes potentially associated with an undiscovered uranium deposit may be evaluated to make quantitative comparisons of water required for uranium production or potential waste products generated during uranium extraction from areas permissive for uranium resource occurrence throughout the United States.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185104","usgsCitation":"Gallegos, T.J., Walton-Day, K., and Seal, R.R., II, 2020, Conceptual framework and approach for conducting a geoenvironmental assessment of undiscovered uranium resources: U.S. Geological Survey Scientific Investigations Report 2018–5104, 28 p., https://doi.org/10.3133/sir20185104.","productDescription":"vi, 28 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070792","costCenters":[{"id":191,"text":"Colorado Water Science 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           -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/eersc\" data-mce-href=\"https://www.usgs.gov/centers/eersc\">Eastern Energy Resources Science Center</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive<br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Background</li><li>The Proposed Geoenvironmental Assessment Framework for Undiscovered Uranium Resource</li><li>Establishing the Geoenvironmental Assessment Approach</li><li>Geoenvironmental Assessment Outcomes</li><li>Limitations and Science Needs</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li><li>Glossary</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-06-16","noUsgsAuthors":false,"publicationDate":"2020-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Gallegos, Tanya J. 0000-0003-3350-6473","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":206859,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":741953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walton-Day, Katherine 0000-0002-5908-2683 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-5908-2683","contributorId":206860,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":741954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seal,, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":141204,"corporation":false,"usgs":true,"family":"Seal,","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":741955,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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