{"pageNumber":"546","pageRowStart":"13625","pageSize":"25","recordCount":184828,"records":[{"id":70217368,"text":"70217368 - 2021 - Three-dimensional distribution of residence time metrics in the glaciated United States using metamodels trained on general numerical models","interactions":[],"lastModifiedDate":"2024-09-16T22:32:11.340035","indexId":"70217368","displayToPublicDate":"2021-01-12T07:59:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Three-dimensional distribution of residence time metrics in the glaciated United States using metamodels trained on general numerical models","docAbstract":"<div class=\"article-section__content en main\"><p>Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large‐scale numerical models can be resource intensive. Using a novel automated approach, a set of 115 inexpensive general simulation models (GSMs) was used to create RTD metrics (fraction of young groundwater, defined as &lt; 65 years old; mean travel time of young fraction; median travel time of old fraction; and mean path length). GSMs captured the general trends in measured tritium concentrations in 431 wells. Boosted Regression Tree metamodels were trained to predict these RTD metrics using available wall‐to‐wall hydrogeographic digital sets as explanatory features. The metamodels produced a three‐dimensional distribution of predictions throughout the glacial system that generally matched with the numerical model RTD metrics. In addition to the expected importance of aquifer thickness and recharge rate in predicting RTD metrics, two new data sets, Multi‐Order Hydrologic Position (MOHP) and hydrogeologic terrane were important predictors. These variables by themselves produced metamodels with Nash‐Sutcliffe efficiency close to the full metamodel. Metamodel predictions showed that the volume of young groundwater stored in the glaciated U.S. is about 6,000 km<sup>3</sup>, or about 0.5% of globally stored young groundwater.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR027335","usgsCitation":"Starn, J., Kauffman, L.J., Carlson, C.S., Reddy, J., and Fienen, M., 2021, Three-dimensional distribution of residence time metrics in the glaciated United States using metamodels trained on general numerical models: Water Resources Research, v. 57, no. 2, ee2020WR027335, 17 p., https://doi.org/10.1029/2020WR027335.","productDescription":"ee2020WR027335, 17 p.","ipdsId":"IP-111637","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":488991,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr027335","text":"Publisher Index Page"},{"id":436588,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BNWWCU","text":"USGS data release","linkHelpText":"Data for Three-dimensional distribution of groundwater residence time metrics in the glaciated United States using metamodels trained on general numerical simulation models"},{"id":382315,"rank":1,"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      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -125.75744400890645,\n              49.35633946833349\n            ],\n            [\n              -125.75744400890645,\n              42.11912973645357\n            ],\n            [\n              -67.66280273829909,\n              42.11912973645357\n            ],\n            [\n              -67.66280273829909,\n              49.35633946833349\n            ],\n            [\n              -125.75744400890645,\n              49.35633946833349\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"57","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Starn, J. Jeffrey 0000-0001-5909-0010 jjstarn@usgs.gov","orcid":"https://orcid.org/0000-0001-5909-0010","contributorId":1916,"corporation":false,"usgs":true,"family":"Starn","given":"J. Jeffrey","email":"jjstarn@usgs.gov","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":false,"id":808531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kauffman, Leon J. 0000-0003-4564-0362","orcid":"https://orcid.org/0000-0003-4564-0362","contributorId":206428,"corporation":false,"usgs":true,"family":"Kauffman","given":"Leon","email":"","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carlson, Carl S. 0000-0001-7142-3519 cscarlso@usgs.gov","orcid":"https://orcid.org/0000-0001-7142-3519","contributorId":1694,"corporation":false,"usgs":true,"family":"Carlson","given":"Carl","email":"cscarlso@usgs.gov","middleInitial":"S.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reddy, James E. 0000-0002-6998-7267","orcid":"https://orcid.org/0000-0002-6998-7267","contributorId":206426,"corporation":false,"usgs":true,"family":"Reddy","given":"James E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808534,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808535,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70217256,"text":"70217256 - 2021 - Historic population estimates for bottlenose dolphins (Tursiops truncatus) in Aragua, Venezuela indicate monitoring need","interactions":[],"lastModifiedDate":"2021-01-14T13:46:00.315075","indexId":"70217256","displayToPublicDate":"2021-01-12T07:41:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":869,"text":"Aquatic Mammals","active":true,"publicationSubtype":{"id":10}},"title":"Historic population estimates for bottlenose dolphins (Tursiops truncatus) in Aragua, Venezuela indicate monitoring need","docAbstract":"<p><span>This study reports historic capture-mark-recapture survival and abundance estimates of common bottlenose dolphins (</span><i>Tursiops truncatus</i><span>) based on photo-identification surveys of coastal Venezuela (along the Aragua coast between Turiamo Bay and Puerto Colombia). We used the most recent data available: dolphins identified by unique dorsal fin marks during wet and dry season surveys conducted from 2004 to 2008. Dolphin encounter histories were analyzed in the Closed Capture Robust Design framework, with the top model including random movement, constant survival, and capture-recapture probabilities that varied by secondary periods. Survival of marked adults was estimated at 0.99 (95% CI = 0.97 to 1.00). Population estimates for all adults (marked and unmarked) averaged 31 animals (SD = 13.8), and for all dolphins (all adults and calves), 41 animals (SD = 17.2). Coastal bottlenose dolphins face numerous threats, including ship strikes, oil spills, conflict with recreational and industrial fisheries, other negative human interactions, biotoxins, chemicals, noise, freshwater discharge, and coastal development. Further, small populations are, in general, at increased risk due to reduced resiliency and recovery potential when exposed to such threats and to expected environmental and demographic stochasticity. These historic estimates of abundance and survival are critical for establishing a reference state and indicate a need for ongoing monitoring of the small dolphin population while the Aragua coast is still, as of yet, relatively little impacted by humans. Should coastal development increase (as is the global trend) and/or environmental catastrophes (e.g., harmful algal blooms, hurricanes, and oil spills) occur, these historic estimates will be essential for assessing impacts and guiding management and conservation interventions. Our results show year-round dolphin presence and highlight the Venezuelan coastal–oceanic landscape as an area of both future research and conservation importance.</span><br></p>","language":"English","publisher":"Aquatic Mammals","doi":"10.1578/AM.47.1.2021.10","usgsCitation":"Cobarrubia-Russo, S., Barber-Meyer, S., Barreto, G.R., and Molero-Lizarraga, A., 2021, Historic population estimates for bottlenose dolphins (Tursiops truncatus) in Aragua, Venezuela indicate monitoring need: Aquatic Mammals, v. 1, no. 47, p. 10-20, https://doi.org/10.1578/AM.47.1.2021.10.","productDescription":"11 p.","startPage":"10","endPage":"20","ipdsId":"IP-118661","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":382151,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Venezuela","state":"Aragua","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -67.291259765625,\n              10.541821094659076\n            ],\n            [\n              -67.87353515625,\n              10.477008906900293\n            ],\n            [\n              -67.818603515625,\n              10.3257278721883\n            ],\n            [\n              -67.6318359375,\n              10.109486058403773\n            ],\n            [\n              -67.445068359375,\n              10.001310360636928\n            ],\n            [\n              -67.2802734375,\n              9.903921416774978\n            ],\n            [\n              -67.03857421875,\n              9.709057068618208\n            ],\n            [\n              -66.95068359374999,\n              9.611582210984674\n            ],\n            [\n              -67.0166015625,\n              9.44906182688142\n            ],\n            [\n              -66.73095703125,\n              9.308148692484803\n            ],\n            [\n              -66.51123046875,\n              9.524914302345891\n            ],\n            [\n              -66.544189453125,\n              10.055402736564236\n            ],\n            [\n              -67.03857421875,\n              10.152746165571939\n            ],\n            [\n              -67.291259765625,\n              10.541821094659076\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"1","issue":"47","noUsgsAuthors":false,"publicationDate":"2021-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Cobarrubia-Russo, Sergio 0000-0002-3351-1929","orcid":"https://orcid.org/0000-0002-3351-1929","contributorId":247716,"corporation":false,"usgs":false,"family":"Cobarrubia-Russo","given":"Sergio","email":"","affiliations":[{"id":49631,"text":"Laboratorio de Ecosistemas y Cambio Global, Centro de Ecología, Instituto Venezolano de Investigaciones Científicas","active":true,"usgs":false}],"preferred":false,"id":808175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barber-Meyer, Shannon 0000-0002-3048-2616","orcid":"https://orcid.org/0000-0002-3048-2616","contributorId":217939,"corporation":false,"usgs":true,"family":"Barber-Meyer","given":"Shannon","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":808176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barreto, Guillermo R.","contributorId":247743,"corporation":false,"usgs":false,"family":"Barreto","given":"Guillermo","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":808205,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Molero-Lizarraga, Alimar 0000-0003-1646-9818","orcid":"https://orcid.org/0000-0003-1646-9818","contributorId":247717,"corporation":false,"usgs":false,"family":"Molero-Lizarraga","given":"Alimar","email":"","affiliations":[{"id":49634,"text":"Unidad de Diversidad Biológica, Instituto Venezolano de Investigaciones Cientificas IVIC","active":true,"usgs":false}],"preferred":false,"id":808206,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70217252,"text":"70217252 - 2021 - Exposure to domoic acid is an ecological driver of cardiac disease in southern sea otters","interactions":[],"lastModifiedDate":"2021-01-14T13:31:16.688143","indexId":"70217252","displayToPublicDate":"2021-01-12T07:28:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"Exposure to domoic acid is an ecological driver of cardiac disease in southern sea otters","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0002\" class=\"abstract author\"><div id=\"abss0002\"><p id=\"spara008\">Harmful algal blooms produce toxins that bioaccumulate in the food web and adversely affect humans, animals, and entire marine ecosystems. Blooms of the diatom<span>&nbsp;</span><i>Pseudo-nitzschia</i><span>&nbsp;</span>can produce domoic acid (DA), a toxin that most commonly causes neurological disease in endothermic animals, with cardiovascular effects that were first recognized in southern sea otters. Over the last 20 years, DA toxicosis has caused significant morbidity and mortality in marine mammals and seabirds along the west coast of the USA. Identifying DA exposure has been limited to toxin detection in biological fluids using biochemical assays, yet measurement of systemic toxin levels is an unreliable indicator of exposure dose or timing. Furthermore, there is little information regarding repeated DA exposure in marine wildlife. Here, the association between long-term environmental DA exposure and fatal cardiac disease was investigated in a longitudinal study of 186 free-ranging sea otters in California from 2001 – 2017, highlighting the chronic health effects of a marine toxin. A novel Bayesian spatiotemporal approach was used to characterize environmental DA exposure by combining several DA surveillance datasets and integrating this with life history data from radio-tagged otters in a time-dependent survival model. In this study, a sea otter with high DA exposure had a 1.7-fold increased hazard of fatal cardiomyopathy compared to an otter with low exposure. Otters that consumed a high proportion of crab and clam had a 2.5- and 1.2-times greater hazard of death due to cardiomyopathy than otters that consumed low proportions. Increasing age is a well-established predictor of cardiac disease, but this study is the first to identify that DA exposure affects the risk of cardiomyopathy more substantially in prime-age adults than aged adults. A 4-year-old otter with high DA exposure had 2.3 times greater risk of fatal cardiomyopathy than an otter with low exposure, while a 10-year old otter with high DA exposure had just 1.2 times greater risk. High<span>&nbsp;</span><i>Toxoplasma gondii</i><span>&nbsp;</span>titers also increased the hazard of death due to heart disease 2.4-fold. Domoic acid exposure was most detrimental for prime-age adults, whose survival and reproduction are vital for population growth, suggesting that persistent DA exposure will likely impact long-term viability of this threatened species. These results offer insight into the pervasiveness of DA in the food web and raise awareness of under-recognized chronic health effects of DA for wildlife at a time when toxic blooms are on the rise.</p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2020.101973","usgsCitation":"Moriarty, M.E., Tinker, M., Miller, M., Tomoleoni, J.A., Staedler, M.M., Fujii, J.A., Batac, F.I., Dodd, E.M., Kudela, R.M., Zubkousky-White, V., and Johnson, C., 2021, Exposure to domoic acid is an ecological driver of cardiac disease in southern sea otters: Harmful Algae, v. 101, 101973, 12 p., https://doi.org/10.1016/j.hal.2020.101973.","productDescription":"101973, 12 p.","ipdsId":"IP-125410","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":453868,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hal.2020.101973","text":"Publisher Index Page"},{"id":382149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.56347656249999,\n              34.27083595165\n            ],\n            [\n              -120.10253906249999,\n              34.27083595165\n            ],\n            [\n              -120.10253906249999,\n              37.38761749978395\n            ],\n            [\n              -122.56347656249999,\n              37.38761749978395\n            ],\n            [\n              -122.56347656249999,\n              34.27083595165\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"101","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moriarty, Megan E.","contributorId":247708,"corporation":false,"usgs":true,"family":"Moriarty","given":"Megan","email":"","middleInitial":"E.","affiliations":[{"id":49627,"text":"Karen C. Drayer Wildlife Health Center and EpiCenter for Disease Dynamics, One Health Institute, University of California Davis School of Veterinary Medicine, 1089 Veterinary Medicine Dr. VM3B, Davis, CA, United States","active":true,"usgs":false}],"preferred":true,"id":808157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tinker, M. Tim 0000-0002-3314-839X","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":221787,"corporation":false,"usgs":false,"family":"Tinker","given":"M. Tim","affiliations":[{"id":40428,"text":"University of California, Santa Cruz; former USGS PI","active":true,"usgs":false}],"preferred":false,"id":808158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Melissa","contributorId":214302,"corporation":false,"usgs":false,"family":"Miller","given":"Melissa","affiliations":[{"id":39007,"text":"CA Dept of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":808159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tomoleoni, Joseph A. 0000-0001-6980-251X jtomoleoni@usgs.gov","orcid":"https://orcid.org/0000-0001-6980-251X","contributorId":167551,"corporation":false,"usgs":true,"family":"Tomoleoni","given":"Joseph","email":"jtomoleoni@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":808160,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Staedler, Michelle M. 0000-0002-1101-6580","orcid":"https://orcid.org/0000-0002-1101-6580","contributorId":213742,"corporation":false,"usgs":false,"family":"Staedler","given":"Michelle","email":"","middleInitial":"M.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":808161,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fujii, Jessica A. 0000-0003-4794-479X","orcid":"https://orcid.org/0000-0003-4794-479X","contributorId":196602,"corporation":false,"usgs":false,"family":"Fujii","given":"Jessica","email":"","middleInitial":"A.","affiliations":[],"preferred":true,"id":808162,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Batac, Francesca I.","contributorId":168467,"corporation":false,"usgs":false,"family":"Batac","given":"Francesca","email":"","middleInitial":"I.","affiliations":[{"id":13632,"text":"CDFW, Bishop, CA","active":true,"usgs":false}],"preferred":false,"id":808163,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dodd, Erin M.","contributorId":168468,"corporation":false,"usgs":false,"family":"Dodd","given":"Erin","email":"","middleInitial":"M.","affiliations":[{"id":13632,"text":"CDFW, Bishop, CA","active":true,"usgs":false}],"preferred":false,"id":808164,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kudela, Raphael M.","contributorId":205181,"corporation":false,"usgs":false,"family":"Kudela","given":"Raphael","email":"","middleInitial":"M.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":808165,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Zubkousky-White, Vanessa","contributorId":247709,"corporation":false,"usgs":false,"family":"Zubkousky-White","given":"Vanessa","email":"","affiliations":[{"id":49630,"text":"California Department of Public Health, Environmental Management Branch, 850 Marina Bay Pkwy, Richmond, CA, United States","active":true,"usgs":false}],"preferred":false,"id":808166,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Johnson, Christine K.","contributorId":23771,"corporation":false,"usgs":false,"family":"Johnson","given":"Christine K.","affiliations":[],"preferred":false,"id":808167,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70217347,"text":"70217347 - 2021 - Gondwanic inheritance on the building of the western Central Andes (Domeyko Range, Chile): Structural and thermochronological approach (U-Pb and 40Ar-39Ar)","interactions":[],"lastModifiedDate":"2021-03-19T20:33:25.768042","indexId":"70217347","displayToPublicDate":"2021-01-12T06:40:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Gondwanic inheritance on the building of the western Central Andes (Domeyko Range, Chile): Structural and thermochronological approach (U-Pb and <sup>40</sup>Ar-<sup>39</sup>Ar)","title":"Gondwanic inheritance on the building of the western Central Andes (Domeyko Range, Chile): Structural and thermochronological approach (U-Pb and 40Ar-39Ar)","docAbstract":"<p><span>Tectonics inheritance controls the evolution of many orogens. To unravel the role of the Gondwanan heritage (late Paleozoic to Triassic) over the building of the Central Andes in northern Chile (Domeyko Range), we performed detrital U‐Pb zircon and&nbsp;</span><sup>40</sup><span>Ar/</span><sup>39</sup><span>Ar muscovite geochronology along with structural analyses (kinematics and structural balancing).&nbsp;</span><sup>40</sup><span>Ar/</span><sup>39</sup><span>Ar dating of detrital muscovite reveals contrasting cooling histories for the Paleozoic basement of Triassic rift sub‐basins, indicating that NW‐striking crustal structures segmented the Andean forearc since at least the middle Permian, likely related to an accretional fabric developed along SW Gondwana. These structures can be inferred based on scattered faults, gravimetric data, and basement age disruptions. During the Late Triassic, NS‐striking master faults and secondary NW‐ to NNW‐striking faults configured an oblique rift, primarily driven by subduction dynamics. We suggest that along SW Gondwana, the slab‐pull would have controlled the development of subduction‐related rift basins close to the trench whereas Triassic inland rifts were mainly driven by Pangea‐breakup stresses. Compressional tectonics began in the Late Cretaceous, yet the inversion of the Triassic rift would have started during the Eocene with the inception of the metallogenic‐fertile transpressional Domeyko fault system. Thus, the structural style of this range was determined by the architecture of the Triassic rift, where the inversion of deep‐seated faults accounted for west‐vergent thick‐ and thin‐skinned structures. Pre‐Andean NW‐striking structures also accommodated tectonic rotations during the Incaic orogeny (Eocene‐Oligocene) and may delimit the rupture zone of large earthquakes, suggesting an underestimated role of such ancient discontinuities in Andean neotectonics.</span></p>","language":"English","publisher":"Wiley","doi":"10.1029/2020TC006475","usgsCitation":"Espinoza, M., Oliveros, V., Vasquez, P., Giambiagi, L., Morgan, L.E., Gonzalez, R., Solari, L., and Bechis, F., 2021, Gondwanic inheritance on the building of the western Central Andes (Domeyko Range, Chile): Structural and thermochronological approach (U-Pb and 40Ar-39Ar): Tectonics, v. 40, no. 3, e2020TC006475, https://doi.org/10.1029/2020TC006475.","productDescription":"e2020TC006475","ipdsId":"IP-106092","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":382288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Espinoza, Mauricio 0000-0003-2557-1603","orcid":"https://orcid.org/0000-0003-2557-1603","contributorId":247823,"corporation":false,"usgs":false,"family":"Espinoza","given":"Mauricio","email":"","affiliations":[{"id":49667,"text":"Universidad de Concepción","active":true,"usgs":false}],"preferred":false,"id":808446,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oliveros, Veronica","contributorId":247824,"corporation":false,"usgs":false,"family":"Oliveros","given":"Veronica","email":"","affiliations":[{"id":49667,"text":"Universidad de Concepción","active":true,"usgs":false}],"preferred":false,"id":808447,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vasquez, Paulina","contributorId":247826,"corporation":false,"usgs":false,"family":"Vasquez","given":"Paulina","email":"","affiliations":[{"id":49668,"text":"Servicio Nacional de Geología y Minería","active":true,"usgs":false}],"preferred":false,"id":808448,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Giambiagi, Laura 0000-0001-6286-7206","orcid":"https://orcid.org/0000-0001-6286-7206","contributorId":247829,"corporation":false,"usgs":false,"family":"Giambiagi","given":"Laura","email":"","affiliations":[{"id":49669,"text":"Centro Regional de Investigaciones Científicas y Tecnológicas","active":true,"usgs":false}],"preferred":false,"id":808449,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morgan, Leah E. 0000-0001-9930-524X lemorgan@usgs.gov","orcid":"https://orcid.org/0000-0001-9930-524X","contributorId":176174,"corporation":false,"usgs":true,"family":"Morgan","given":"Leah","email":"lemorgan@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":808450,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gonzalez, Rodrigo","contributorId":247830,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Rodrigo","email":"","affiliations":[{"id":27795,"text":"Universidad Católica del Norte","active":true,"usgs":false}],"preferred":false,"id":808451,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Solari, Luigi 0000-0002-9769-6846","orcid":"https://orcid.org/0000-0002-9769-6846","contributorId":247831,"corporation":false,"usgs":false,"family":"Solari","given":"Luigi","email":"","affiliations":[{"id":25354,"text":"Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":808452,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bechis, Florencia","contributorId":247833,"corporation":false,"usgs":false,"family":"Bechis","given":"Florencia","email":"","affiliations":[{"id":49670,"text":"Universidad Nacional de Río Negro","active":true,"usgs":false}],"preferred":false,"id":808453,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70217220,"text":"70217220 - 2021 - Eroding Cascadia— Sediment and solute transport and landscape denudation in western Oregon and northwestern California","interactions":[],"lastModifiedDate":"2021-10-08T11:27:36.141752","indexId":"70217220","displayToPublicDate":"2021-01-11T07:43:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1786,"text":"Geological Society of America Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Eroding Cascadia— Sediment and solute transport and landscape denudation in western Oregon and northwestern California","docAbstract":"<div class=\"article-section-wrapper js-article-section js-content-section  \"><p>Riverine measurements of sediment and solute transport give empirical basin-scale estimates of bed-load, suspended-sediment, and silicate-solute fluxes for 100,000 km<sup>2</sup><span>&nbsp;</span>of northwestern California and western Oregon. This spatially explicit sediment budget shows the multifaceted control of geology and physiography on the rates and processes of fluvial denudation. Bed-load transport is greatest for steep basins, particularly in areas underlain by the accreted Klamath terrane. Bed-load flux commonly decreases downstream as clasts convert to suspended load by breakage and attrition, particularly for softer rock types. Suspended load correlates strongly with lithology, basin slope, precipitation, and wildfire disturbance. It is highest in steep regions of soft rocks, and our estimates suggest that much of the suspended load is derived from bed-load comminution. Dissolution, measured by basin-scale silicate-solute yield, constitutes a third of regional landscape denudation. Solute yield correlates with precipitation and is proportionally greatest in low-gradient and wet basins and for high parts of the Cascade Range, where undissected Quaternary volcanic rocks soak in 2−3 m of annual precipitation. Combined, these estimates provide basin-scale erosion rates ranging from ∼50 t ∙ km<sup>−2</sup><span>&nbsp;</span>∙ yr<sup>−1</sup><span>&nbsp;</span>(approximately equivalent to 0.02 mm ∙ yr<sup>−1</sup>) for low-gradient basins such as the Willamette River to ∼500 t ∙ km<sup>−2</sup><span>&nbsp;</span>∙ yr<sup>−1</sup><span>&nbsp;</span>(∼0.2 mm ∙ yr<sup>−1</sup>) for steep coastal drainages. The denudation rates determined here from modern measurements are less than those estimated by longer-term geologic assessments, suggesting episodic disturbances such as fire, flood, seismic shaking, and climate change significantly add to long-term landscape denudation.</p></div>","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35710.1","usgsCitation":"O'Connor, J., Mangano, J., Wise, D., and Roering, J.R., 2021, Eroding Cascadia— Sediment and solute transport and landscape denudation in western Oregon and northwestern California: Geological Society of America Bulletin, v. 133, no. 9-10, p. 1851-1874, https://doi.org/10.1130/B35710.1.","productDescription":"24 p.","startPage":"1851","endPage":"1874","ipdsId":"IP-118050","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":382127,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Cascade range","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -124.69482421875,\n              38.42777351132905\n            ],\n            [\n              -121.640625,\n              38.42777351132905\n            ],\n            [\n              -121.640625,\n              46.63435070293566\n            ],\n            [\n              -124.69482421875,\n              46.63435070293566\n            ],\n            [\n              -124.69482421875,\n              38.42777351132905\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"133","issue":"9-10","noUsgsAuthors":false,"publicationDate":"2021-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"O'Connor, Jim E. 0000-0002-7928-5883 oconnor@usgs.gov","orcid":"https://orcid.org/0000-0002-7928-5883","contributorId":140771,"corporation":false,"usgs":true,"family":"O'Connor","given":"Jim E.","email":"oconnor@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":808085,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mangano, Joseph F. 0000-0003-4213-8406","orcid":"https://orcid.org/0000-0003-4213-8406","contributorId":247673,"corporation":false,"usgs":true,"family":"Mangano","given":"Joseph F.","affiliations":[{"id":5072,"text":"Office of Communication and Publishing","active":true,"usgs":true}],"preferred":true,"id":808086,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wise, Daniel R. 0000-0002-1215-9612","orcid":"https://orcid.org/0000-0002-1215-9612","contributorId":210599,"corporation":false,"usgs":true,"family":"Wise","given":"Daniel R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808087,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roering, Joshua R.","contributorId":247674,"corporation":false,"usgs":false,"family":"Roering","given":"Joshua","email":"","middleInitial":"R.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":808088,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70236104,"text":"70236104 - 2021 - Globally prevalent land nitrogen memory amplifies water pollution following drought years","interactions":[],"lastModifiedDate":"2022-08-29T12:27:22.037665","indexId":"70236104","displayToPublicDate":"2021-01-11T07:26:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Globally prevalent land nitrogen memory amplifies water pollution following drought years","docAbstract":"<div class=\"article-text wd-jnl-art-abstract cf\"><p>Enhanced riverine delivery of terrestrial nitrogen (N) has polluted many freshwater and coastal ecosystems, degrading drinking water and marine resources. An emerging view suggests a contribution of land N memory effects—impacts of antecedent dry conditions on land N accumulation that disproportionately increase subsequent river N loads. To date, however, such effects have only been explored for several relatively small rivers covering a few episodes. Here we introduce an index for quantifying land N memory effects and assess their prevalence using regional observations and global terrestrial-freshwater ecosystem model outputs. Model analyses imply that land N memory effects are globally prevalent but vary widely in strength. Strong effects reflect large soil dissolved inorganic N (DIN) surpluses by the end of dry years. During the subsequent wetter years, the surpluses are augmented by soil net mineralization pulses, which outpace plant uptake and soil denitrification, resulting in disproportionately increased soil leaching and eventual river loads. These mechanisms are most prominent in areas with high hydroclimate variability, warm climates, and ecosystem disturbances. In 48 of the 118 basins analyzed, strong memory effects produce 43% (21%–88%) higher DIN loads following drought years than following average years. Such a marked influence supports close consideration of prevalent land N memory effects in water-pollution management efforts.</p></div>","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/abd1a0","usgsCitation":"Lee, M., Stock, C., Shevliakova, E., Malyshev, S., and Milly, P.C., 2021, Globally prevalent land nitrogen memory amplifies water pollution following drought years: Environmental Research Letters, v. 16, 014049, 12 p., https://doi.org/10.1088/1748-9326/abd1a0.","productDescription":"014049, 12 p.","ipdsId":"IP-097531","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":453872,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/abd1a0","text":"Publisher Index Page"},{"id":405787,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","noUsgsAuthors":false,"publicationDate":"2021-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Lee, Minjin","contributorId":177261,"corporation":false,"usgs":false,"family":"Lee","given":"Minjin","email":"","affiliations":[],"preferred":false,"id":850076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stock, Charles A.","contributorId":217586,"corporation":false,"usgs":false,"family":"Stock","given":"Charles A.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":850078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shevliakova, Elena","contributorId":201589,"corporation":false,"usgs":false,"family":"Shevliakova","given":"Elena","email":"","affiliations":[{"id":36211,"text":"GFDL/NOAA","active":true,"usgs":false}],"preferred":false,"id":850077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Malyshev, Sergey","contributorId":189177,"corporation":false,"usgs":false,"family":"Malyshev","given":"Sergey","affiliations":[],"preferred":false,"id":850080,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Milly, Paul C. D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":176836,"corporation":false,"usgs":true,"family":"Milly","given":"Paul","email":"cmilly@usgs.gov","middleInitial":"C. D.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":false,"id":850079,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70229141,"text":"70229141 - 2021 - Regal fritillary (Speyeria idalia) sex ratio in tallgrass prairie: Effects of survey timing and management regime","interactions":[],"lastModifiedDate":"2022-03-01T13:25:40.088538","indexId":"70229141","displayToPublicDate":"2021-01-11T07:22:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5153,"text":"The American Midland Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Regal fritillary (Speyeria idalia) sex ratio in tallgrass prairie: Effects of survey timing and management regime","docAbstract":"<div class=\"div0\"><div class=\"row ArticleContentRow\"><p id=\"ID0EF\" class=\"first\">The regal fritillary,<span>&nbsp;</span><i>Speyeria idalia</i><span>&nbsp;</span>(Drury), was once a common inhabitant of North American grassland communities. Regal fritillary populations are commonly reported to have a male biased adult sex ratio (ASR) throughout their range. We assessed the observed ASR of regal fritillary throughout an annual flight period, investigated how the overall density of both sexes changed, and tested effects of prescribed fire, grazing and haying management treatments on male and female density. We found that regal fritillary exhibited an observed 2:1 male biased ASR across the entire emergence period. Our analysis also revealed that male density peaked earlier than female density in the flight period. Point estimates of density indicated sites that received prescribed burning at the moderate fire-return interval supported ≥1.3 times greater density of males and ≥5.6 times greater density of females versus sites burned with short and long fire-return intervals. Additionally, this effect was enhanced when combined with grazing which showed males were ≥1.9 times and females had ≥1.2 times greater point estimates of density in sites that were grazed and burned at a moderate fire-return interval versus other sites. The relatively stable status of regal fritillary within our study region suggests that a 2:1 male to female ASR may be considered the model composition of populations throughout their range. Likewise, the dynamic nature of the ASR throughout the flight period highlights the importance of conducting surveys across the flight period. Finally, these results corroborate an increasing number of research results that reveal common prairie management practices, such as prescribed fire can be applied within sites that contain regal fritillary and continue to support stable populations.</p></div></div>","language":"English","publisher":"BioOne","doi":"10.1637/0003-0031-185.1.57","usgsCitation":"McCullough, K., Haukos, D.A., and Albanese, G., 2021, Regal fritillary (Speyeria idalia) sex ratio in tallgrass prairie: Effects of survey timing and management regime: The American Midland Naturalist, v. 185, no. 1, p. 57-76, https://doi.org/10.1637/0003-0031-185.1.57.","productDescription":"20 p.","startPage":"57","endPage":"76","ipdsId":"IP-122840","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396595,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://bioone.org/journals/the-american-midland-naturalist/volume-185/issue-1/0003-0031-185.1.57/Regal-Fritillary-Speyeria-idalia-Sex-Ratio-in-Tallgrass-Prairie/10.1637/0003-0031-185.1.57.full"}],"country":"United States","state":"Kansas","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.3388671875,\n              38.20365531807149\n            ],\n            [\n              -95.20751953125,\n              38.20365531807149\n            ],\n            [\n              -95.20751953125,\n              39.774769485295465\n            ],\n            [\n              -97.3388671875,\n              39.774769485295465\n            ],\n            [\n              -97.3388671875,\n              38.20365531807149\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"185","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCullough, Kelsey","contributorId":200244,"corporation":false,"usgs":false,"family":"McCullough","given":"Kelsey","email":"","affiliations":[],"preferred":false,"id":836760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836759,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Albanese, Gene","contributorId":287438,"corporation":false,"usgs":false,"family":"Albanese","given":"Gene","affiliations":[{"id":61586,"text":"mas","active":true,"usgs":false}],"preferred":false,"id":836761,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70217195,"text":"70217195 - 2021 - Upland burning and grazing as strategies to offset climate-change effects on wetlands","interactions":[],"lastModifiedDate":"2021-04-08T14:28:20.228474","indexId":"70217195","displayToPublicDate":"2021-01-11T07:11:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Upland burning and grazing as strategies to offset climate-change effects on wetlands","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Wetland ecosystems perform a multitude of services valued by society and provide critical habitat for migratory birds and other wildlife. Despite their importance, wetlands have been lost to different local, regional, and global drivers. Remaining wetlands are extremely sensitive to changing temperature and precipitation regimes. Management of grassland areas in wetland catchments may be an effective strategy for counteracting potentially negative impacts of climate change on wetlands. Our objective was to estimate the effects of climate changes on wetland hydrology, and to explore strategies for increasing surface-water inputs to wetlands. We coupled a field study with process-based simulation modeling of wetland-water levels. We found that climate change could decrease the number of wetlands that hold ponded water during the waterfowl breeding season by 14% under a hot wet scenario or 29% under a hot dry scenario if no upland-management actions were taken. Upland burning reduced pond losses to 9% (hot wet) and 26% (hot dry). Upland grazing resulted in the smallest loss of ponded wetlands, 6% loss under the hot-and-wet scenario and 22% loss under the hot-and-dry scenario. Overall, water inputs could be increased by either burning or grazing of upland vegetation thereby reducing pond losses during the waterfowl breeding season. While field results suggest that both grazing and burning can reduce the vegetative structure that could lead to increases in runoff in grassland catchments, our model simulations indicated that additional actions may be needed for managers to minimize future meteorologically driven water losses.</p></div></div><div id=\"Sec1-section\" class=\"c-article-section\"><br></div>","language":"English","publisher":"Springer","doi":"10.1007/s11273-020-09778-1","usgsCitation":"McKenna, O.P., Renton, D.A., Mushet, D.M., and DeKeyser, E.S., 2021, Upland burning and grazing as strategies to offset climate-change effects on wetlands: Wetlands Ecology and Management, v. 29, https://doi.org/10.1007/s11273-020-09778-1.","productDescription":"16 p.","startPage":"208","ipdsId":"IP-112405","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":453876,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11273-020-09778-1","text":"Publisher Index Page"},{"id":382084,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","edition":"193","noUsgsAuthors":false,"publicationDate":"2021-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":807933,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Renton, David A. drenton@usgs.gov","contributorId":247571,"corporation":false,"usgs":false,"family":"Renton","given":"David","email":"drenton@usgs.gov","middleInitial":"A.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":807934,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":807935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeKeyser, Edward S.","contributorId":247572,"corporation":false,"usgs":false,"family":"DeKeyser","given":"Edward","email":"","middleInitial":"S.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":807936,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70239361,"text":"70239361 - 2021 - The unsung success of injurious wildlife listing under the Lacey Act","interactions":[],"lastModifiedDate":"2023-01-11T13:10:41.69105","indexId":"70239361","displayToPublicDate":"2021-01-11T07:09:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"The unsung success of injurious wildlife listing under the Lacey Act","docAbstract":"<p>Previous papers discussing the effectiveness of injurious wildlife listings under 18 U.S.C. 42(a) of the Lacey Act have emphasized failures while ignoring the many successes. We looked at the 120-year history of injurious listing and then determined the effectiveness of the listings since the U.S. Fish and Wildlife Service (USFWS) gained the listing authority in 1940. We measured success by the effectiveness of listing relative to the stage of the invasion process – that is, whether or not a species was established at the time of listing, if it since established, and if it subsequently spread to other States. The USFWS started listing preemptively with its first rule in 1952 and has added the majority of species preemptively since then. We analyzed the 307 species that were listed for invasiveness (excluding species listed for other injurious reasons). Of those species, 288 (94%) were listed preemptively (before they became established). Although we acknowledge that other factors may play a role, we consider species that were listed before establishment and remained not established as “very effective” listings. All 288 remained not established – a 100% prevention rate when listed preemptively. Only 19 of the 307 species (6%) were listed after establishment, and they remain established. The listings are considered “effective” for the 4% that remained within the State(s) they were established in at listing and “ineffective” or “not applicable” for the 2% that spread to other States. The rationale for listing established species is explained herein. We conclude that injurious species listings can be effective at any stage, but prohibiting the importation into the United States of high-risk species prior to their introduction and establishment into U.S. environments is very effective in preventing invasions, and this success has heretofore been overlooked.</p>","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre","doi":"10.3391/mbi.2021.12.3.03","usgsCitation":"Jewell, S.D., and Fuller, P., 2021, The unsung success of injurious wildlife listing under the Lacey Act: Management of Biological Invasions, v. 12, no. 3, p. 527-545, https://doi.org/10.3391/mbi.2021.12.3.03.","productDescription":"19 p.","startPage":"527","endPage":"545","ipdsId":"IP-122472","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":453878,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2021.12.3.03","text":"Publisher Index Page"},{"id":411712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jewell, Susan D.","contributorId":244033,"corporation":false,"usgs":false,"family":"Jewell","given":"Susan","email":"","middleInitial":"D.","affiliations":[{"id":48806,"text":"U.S. Fish and Wildlife Service, Fish and Aquatic Conservation Program","active":true,"usgs":false}],"preferred":false,"id":861264,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Pam 0000-0002-9389-9144","orcid":"https://orcid.org/0000-0002-9389-9144","contributorId":213996,"corporation":false,"usgs":true,"family":"Fuller","given":"Pam","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":861265,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70217204,"text":"70217204 - 2021 - Thermal constraints on energy balance, behaviour and spatial distribution of grizzly bears","interactions":[],"lastModifiedDate":"2021-02-17T21:48:01.347238","indexId":"70217204","displayToPublicDate":"2021-01-10T07:08:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1711,"text":"Functional Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Thermal constraints on energy balance, behaviour and spatial distribution of grizzly bears","docAbstract":"1. Heat dissipation limit theory posits that energy available for growth and reproduction in endotherms is limited by their ability to dissipate heat. In mammals, endogenous heat production increases markedly during gestation and lactation, and thus female mammals may be subject to greater thermal constraints on energy expenditure than males. Such constraints likely have important implications for behaviour and population performance in a warming climate.\n2. We used a mechanistic simulation model based on the first principles of heat and mass transfer to study thermal constraints on activity (both timing and intensity) of captive female grizzly bears Ursus arctos in current and future climate scenarios. We then quantified the relative importance of regulatory behaviours for maintaining heat balance using GPS telemetry locations of lactating versus non-lactating female bears from Yellowstone National Park, and assessed the degree to which costs of thermoregulation constrained the distribution of sampled bears in space and time.\n3. Lactating female bears benefitted considerably more from behavioural cooling mechanisms (e.g. partial submersion in cool water or bedding on cool substrate) than non-lactating females in our simulations; the availability of water for thermoregulation increased the number of hours during which lactating females could be active by up to 60% under current climatic conditions and by up to 43% in the future climate scenario. Moreover, even in the future climate scenario, lactating bears were able to achieve heat balance 24 hr/day by thermoregulating behaviourally when water was available to facilitate cooling.\n4. The most important predictor of female grizzly bear distribution in Yellowstone, regardless of reproductive status, was elevation. However, variables associated with the thermal environment were relatively more important for predicting the distribution of lactating than non-lactating female bears.\n5. Our results suggest that the costs of heat dissipation, which are modulated by climate, may impose constraints on the behaviour and energetics of large endotherms like grizzly bears, and that access to water for cooling will likely be an increasingly important driver of grizzly bear distribution in Yellowstone as the climate continues to warm.","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2435.13727","usgsCitation":"Rogers, S.A., Robbins, C.T., Mathewson, P.D., Carnahan, A.M., van Manen, F.T., Haroldson, M.A., Porter, W., Rogers, T.R., Soule, T., and Long, R.A., 2021, Thermal constraints on energy balance, behaviour and spatial distribution of grizzly bears: Functional Ecology, v. 35, no. 2, p. 398-410, https://doi.org/10.1111/1365-2435.13727.","productDescription":"13 p.","startPage":"398","endPage":"410","ipdsId":"IP-117651","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":453881,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2435.13727","text":"Publisher Index Page"},{"id":382083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.20361328125,\n              43.58834891179792\n            ],\n            [\n              -108.91845703124999,\n              43.58834891179792\n            ],\n            [\n              -108.91845703124999,\n              45.0502402697946\n            ],\n            [\n              -111.20361328125,\n             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D.","contributorId":247594,"corporation":false,"usgs":false,"family":"Mathewson","given":"Paul","email":"","middleInitial":"D.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":807986,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carnahan, Anthony M.","contributorId":207641,"corporation":false,"usgs":false,"family":"Carnahan","given":"Anthony","email":"","middleInitial":"M.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":807987,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":807988,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haroldson, Mark A. 0000-0002-7457-7676 mharoldson@usgs.gov","orcid":"https://orcid.org/0000-0002-7457-7676","contributorId":1773,"corporation":false,"usgs":true,"family":"Haroldson","given":"Mark","email":"mharoldson@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":807989,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Porter, Warren P.","contributorId":247595,"corporation":false,"usgs":false,"family":"Porter","given":"Warren P.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":807990,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rogers, Taylor R.","contributorId":247596,"corporation":false,"usgs":false,"family":"Rogers","given":"Taylor","email":"","middleInitial":"R.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":807991,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Soule, Terrence","contributorId":247597,"corporation":false,"usgs":false,"family":"Soule","given":"Terrence","email":"","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":807992,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Long, Ryan A.","contributorId":236989,"corporation":false,"usgs":false,"family":"Long","given":"Ryan","email":"","middleInitial":"A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":807993,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70217093,"text":"ds1132 - 2021 - Quality of surface water in Missouri, water year 2019","interactions":[],"lastModifiedDate":"2021-01-11T12:55:18.624014","indexId":"ds1132","displayToPublicDate":"2021-01-08T12:15:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1132","displayTitle":"Quality of Surface Water in Missouri, Water Year 2019","title":"Quality of surface water in Missouri, water year 2019","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a network of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network (AWQMN). During water year 2019 (October 1, 2018, through September 30, 2019), water-quality data were collected at 73 stations: 71 AWQMN and alternate AWQMN stations, and 2 U.S. Geological Survey National Water Quality Monitoring Program stations. Among the stations in this report, four stations have data presented from additional sampling performed in cooperation with the U.S. Army Corps of Engineers. Summaries of the concentrations of dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, <i>Escherichia coli</i> bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and selected pesticides are presented. Most of the stations have been classified based on the physiographic province or primary land use in the watershed monitored by the station. Some stations have been classified based on the unique hydrologic characteristics of the waterbodies (springs, large rivers) they monitor. A summary of hydrologic conditions including peak streamflows, monthly mean streamflows, and 7-day low flows also are presented for representative streamflow-gaging stations in the State.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ds1132","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Kay, R.T., 2021, Quality of surface water in Missouri, water year 2019: U.S. Geological Survey Data Series 1132, 26 p., https://doi.org/10.3133/ds1132.","productDescription":"Report: v, 26 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119904","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":381906,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1132/ds1132.pdf","text":"Report","size":"1.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 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 \"}}]}","contact":"<p>Director, <a href=\"http://www.usgs.gov/centers/cm-water/\" data-mce-href=\"http://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>Abstract</li><li>Introduction</li><li>The Ambient Water-Quality Monitoring Network</li><li>Laboratory Reporting Conventions</li><li>Surface-Water Quality Data Analysis Methods</li><li>Station Classification for Data Analysis</li><li>Hydrologic Conditions</li><li>Distribution, Concentration, and Detection Frequency of Selected Constituents</li><li>Summary</li><li>References Cited</li></ul>","publishedDate":"2021-01-08","noUsgsAuthors":false,"publicationDate":"2021-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kay, Robert T. 0000-0002-6281-8997","orcid":"https://orcid.org/0000-0002-6281-8997","contributorId":205367,"corporation":false,"usgs":true,"family":"Kay","given":"Robert T.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807597,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70217756,"text":"70217756 - 2021 - Uranium(VI) attenuation in a carbonate-bearing oxic alluvial aquifer","interactions":[],"lastModifiedDate":"2021-02-01T17:26:10.71364","indexId":"70217756","displayToPublicDate":"2021-01-08T11:23:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Uranium(VI) attenuation in a carbonate-bearing oxic alluvial aquifer","docAbstract":"<p><span>Uranium minerals are commonly found in soils and sediment across the United States at an average concentration of 2–4&nbsp;mg/kg. Uranium occurs in the environment primarily in two forms, the oxidized, mostly soluble uranium(VI) form, or the reduced, sparingly soluble reduced uranium(IV) form. Here we describe subsurface geochemical conditions that result in low uranium concentrations in an alluvial aquifer with naturally occurring uranium in soils and sediments in the presence of complexing ligands under oxidizing conditions. Groundwater was saturated with respect to calcite and contained calcium (78–90&nbsp;mg/L) with elevated levels of carbonate alkalinity (291–416&nbsp;mg/L as HCO</span><sub>3</sub><sup>−</sup><span>). X-ray adsorption near edge structure (XANES) spectroscopy identified that sediment-associated uranium was oxidized as a uranium(VI) form (85%). Calcite was the predominant mineral by mass in the ultrafine fraction in uranium-bearing sediments (&gt;16&nbsp;mg/kg). Groundwater geochemical modeling indicated calcite and/or a calcium-uranyl-carbonate mineral such as liebigite in equilibrium with groundwater. The&nbsp;</span><i>δ</i><sup>13</sup><span>C (0.57‰&nbsp;±&nbsp;0.15‰) was indicative of abiotic carbonate deposition. Thus, solid-phase uranium(VI) associated with carbonate is likely maintaining uranium(VI) groundwater levels below the maximum contaminant level (MCL; 30&nbsp;µg/L), presenting a deposition mechanism for uranium attenuation rather than solely a means of mobilization.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhazmat.2021.125089","usgsCitation":"Nolan, P., Bone, S., Campbell, K.M., Pannell, D., Healy, O., Stange, M., Bargar, J., and Weber, K., 2021, Uranium(VI) attenuation in a carbonate-bearing oxic alluvial aquifer: Journal of Hazardous Materials, v. 412, 125089, 11 p., https://doi.org/10.1016/j.jhazmat.2021.125089.","productDescription":"125089, 11 p.","ipdsId":"IP-124037","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":453886,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1807547","text":"Publisher Index Page"},{"id":436589,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98GDZFV","text":"USGS data release","linkHelpText":"X-ray diffraction data of sediment samples from Hastings, Nebraska"},{"id":382853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","city":"Hastings","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -98.49929809570312,\n              40.49030405370212\n            ],\n            [\n              -98.27133178710938,\n              40.49030405370212\n            ],\n            [\n              -98.27133178710938,\n              40.65980593837852\n            ],\n            [\n              -98.49929809570312,\n              40.65980593837852\n            ],\n            [\n              -98.49929809570312,\n              40.49030405370212\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"412","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nolan, PJ","contributorId":248603,"corporation":false,"usgs":false,"family":"Nolan","given":"PJ","email":"","affiliations":[{"id":40562,"text":"Golder Associates","active":true,"usgs":false}],"preferred":false,"id":809493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bone, S","contributorId":248604,"corporation":false,"usgs":false,"family":"Bone","given":"S","email":"","affiliations":[{"id":36408,"text":"SLAC National Accelerator Laboratory","active":true,"usgs":false}],"preferred":false,"id":809494,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campbell, Kate M. 0000-0002-8715-5544 kcampbell@usgs.gov","orcid":"https://orcid.org/0000-0002-8715-5544","contributorId":1441,"corporation":false,"usgs":true,"family":"Campbell","given":"Kate","email":"kcampbell@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":809495,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pannell, David","contributorId":217709,"corporation":false,"usgs":false,"family":"Pannell","given":"David","email":"","affiliations":[{"id":16662,"text":"University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":809496,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Healy, O","contributorId":248605,"corporation":false,"usgs":false,"family":"Healy","given":"O","email":"","affiliations":[{"id":16602,"text":"University of Nebraska, Lincoln","active":true,"usgs":false}],"preferred":false,"id":809497,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stange, M","contributorId":248606,"corporation":false,"usgs":false,"family":"Stange","given":"M","email":"","affiliations":[{"id":49962,"text":"Hastings Utilities","active":true,"usgs":false}],"preferred":false,"id":809498,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bargar, J","contributorId":248607,"corporation":false,"usgs":false,"family":"Bargar","given":"J","affiliations":[{"id":36408,"text":"SLAC National Accelerator Laboratory","active":true,"usgs":false}],"preferred":false,"id":809499,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Weber, KA","contributorId":248608,"corporation":false,"usgs":false,"family":"Weber","given":"KA","email":"","affiliations":[{"id":16602,"text":"University of Nebraska, Lincoln","active":true,"usgs":false}],"preferred":false,"id":809500,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70217340,"text":"70217340 - 2021 - Multiple co-occurring and persistently detected cyanotoxins and associated cyanobacteria in adjacent California lakes","interactions":[],"lastModifiedDate":"2021-01-18T16:51:39.293319","indexId":"70217340","displayToPublicDate":"2021-01-08T10:44:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3614,"text":"Toxicon","active":true,"publicationSubtype":{"id":10}},"title":"Multiple co-occurring and persistently detected cyanotoxins and associated cyanobacteria in adjacent California lakes","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0010\" class=\"abstract author\" lang=\"en\"><div id=\"abssec0010\"><p id=\"abspara0010\">The global proliferation of toxin producing cyanobacterial blooms has been attributed to a wide variety of environmental factors with nutrient pollution, increased temperatures, and drought being three of the most significant. The current study is the first formal assessment of cyanotoxins in two impaired lakes, Canyon Lake and Lake Elsinore, in southern California that have a history of cyanobacterial blooms producing high biomass as measured by chl-a. Cyanotoxins in Lake Elsinore were detected at concentrations that persistently exceeded California recreational health thresholds, whereas Canyon Lake experienced persistent concentrations that only occasionally exceeded health thresholds. The study results are the highest recorded concentrations of microcystins, anatoxin-a, and cylindrospermopsin detected in southern California lakes. Concentrations exceeded health thresholds that caused both lakes to be closed for recreational activities. Cyanobacterial identifications indicated a high risk for the presence of potentially toxic genera and agreed with the cyanotoxin results that indicated frequent detection of multiple cyanotoxins simultaneously. A statistically significant correlation was observed between chlorophyll-a (chl-a) and microcystin concentrations for Lake Elsinore but not Canyon Lake, and chl-a was not a good indicator of cylindrospermopsin, anatoxin-a, or nodularin. Therefore, chl-a was not a viable screening indicator of cyanotoxin risk in these lakes. The study results indicate potential acute and chronic risk of exposure to cyanotoxins in these lakes and supports the need for future monitoring efforts to help minimize human and domestic pet exposure and to better understand potential effects to wildlife. The frequent co-occurrence of complex cyanotoxin mixtures further complicates the risk assessment process for these lakes given uncertainty in the toxicology of mixtures.</p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.toxicon.2020.12.019","usgsCitation":"Howard, M.D., Kudela, R.M., Hayashi, K., Tatters, A.O., Caron, D.A., Theroux, S., Oehrle, S., Roethler, M., Donovan, A., Loftin, K.A., and Laughrey, Z.R., 2021, Multiple co-occurring and persistently detected cyanotoxins and associated cyanobacteria in adjacent California lakes: Toxicon, v. 192, p. 1-14, https://doi.org/10.1016/j.toxicon.2020.12.019.","productDescription":"14 p.","startPage":"1","endPage":"14","ipdsId":"IP-104704","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":453889,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.toxicon.2020.12.019","text":"Publisher Index Page"},{"id":436590,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NNSI38","text":"USGS data release","linkHelpText":"Liquid Chromatography Triple Quadrupole Mass Spectrometry (LC/MS/MS) analysis of cyanobacteria cultures from Lake Elsinore and Canyon Lake (CA, USA, 2016) for cyanotoxins and algal toxins"},{"id":382275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Canyon Lake, Lake Elsinore","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.38204956054688,\n              33.62662677351111\n            ],\n            [\n              -117.2344207763672,\n              33.62662677351111\n            ],\n            [\n              -117.2344207763672,\n              33.72776616734189\n            ],\n            [\n              -117.38204956054688,\n              33.72776616734189\n            ],\n            [\n              -117.38204956054688,\n              33.62662677351111\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"192","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Howard, Meredith D. A. 0000-0002-1639-8143","orcid":"https://orcid.org/0000-0002-1639-8143","contributorId":247814,"corporation":false,"usgs":false,"family":"Howard","given":"Meredith","email":"","middleInitial":"D. A.","affiliations":[{"id":49658,"text":"Central Valley Regional Water Quality Control Board","active":true,"usgs":false}],"preferred":false,"id":808410,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kudela, Raphael M.","contributorId":205181,"corporation":false,"usgs":false,"family":"Kudela","given":"Raphael","email":"","middleInitial":"M.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":808411,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayashi, Kendra","contributorId":247815,"corporation":false,"usgs":false,"family":"Hayashi","given":"Kendra","email":"","affiliations":[{"id":49659,"text":"Department of Ocean Science, University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":808412,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tatters, Avery O.","contributorId":247816,"corporation":false,"usgs":false,"family":"Tatters","given":"Avery","email":"","middleInitial":"O.","affiliations":[{"id":49660,"text":"California NanoSystems Institute, University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":808413,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caron, David A.","contributorId":247817,"corporation":false,"usgs":false,"family":"Caron","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":49661,"text":"Department of Biological Sciences, University of Southern California","active":true,"usgs":false}],"preferred":false,"id":808414,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Theroux, Susanna","contributorId":244544,"corporation":false,"usgs":false,"family":"Theroux","given":"Susanna","affiliations":[],"preferred":false,"id":808415,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Oehrle, Stuart","contributorId":247818,"corporation":false,"usgs":false,"family":"Oehrle","given":"Stuart","email":"","affiliations":[{"id":49662,"text":"Waters Field Lab, Northern Kentucky University, Chemistry Department","active":true,"usgs":false}],"preferred":false,"id":808416,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roethler, Miranda","contributorId":247819,"corporation":false,"usgs":false,"family":"Roethler","given":"Miranda","email":"","affiliations":[{"id":49663,"text":"Biogeochemistry Department, Southern California Coastal Water Research Project","active":true,"usgs":false}],"preferred":false,"id":808417,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Donovan, Ariel 0000-0002-8480-2793","orcid":"https://orcid.org/0000-0002-8480-2793","contributorId":222474,"corporation":false,"usgs":true,"family":"Donovan","given":"Ariel","email":"","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":808418,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Loftin, Keith A. 0000-0001-5291-876X","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":221964,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":808419,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Laughrey, Zachary R. 0000-0002-7630-2078 zlaughrey@usgs.gov","orcid":"https://orcid.org/0000-0002-7630-2078","contributorId":198516,"corporation":false,"usgs":true,"family":"Laughrey","given":"Zachary","email":"zlaughrey@usgs.gov","middleInitial":"R.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":808420,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70220170,"text":"70220170 - 2021 - Visualization of schistosomiasis snail habitats using light unmanned aerial vehicles","interactions":[],"lastModifiedDate":"2021-04-22T15:23:48.802618","indexId":"70220170","displayToPublicDate":"2021-01-08T10:20:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8566,"text":"Geospatial Health","active":true,"publicationSubtype":{"id":10}},"title":"Visualization of schistosomiasis snail habitats using light unmanned aerial vehicles","docAbstract":"<p><span>Schistosomiasis, or “snail fever”, is a parasitic disease affecting over 200 million people worldwide. People become infected when exposed to water containing particular species of freshwater snails. Habitats for such snails can be mapped using lightweight, inexpensive and field-deployable consumer-grade Unmanned Aerial Vehicles (UAVs), also known as drones. Drones can obtain imagery in remote areas with poor satellite imagery. An unexpected outcome of using drones is public engagement. Whereas sampling snails exposes field technicians to infection risk and might disturb locals who are also using the water site, drones are novel and fun to watch, attracting crowds that can be educated about the infection risk.</span></p>","language":"English","publisher":"PAGEPress","doi":"10.4081/gh.2020.818","usgsCitation":"Chamberlin, A.J., Jones, I.J., Lund, A.J., Jouanard, N., Riveau, G., Ndione, R., Sokolow, S.H., Wood, C.L., Lafferty, K.D., and De Leo, G.A., 2021, Visualization of schistosomiasis snail habitats using light unmanned aerial vehicles: Geospatial Health, v. 15, no. 2, p. 382-385, https://doi.org/10.4081/gh.2020.818.","productDescription":"4 p.","startPage":"382","endPage":"385","ipdsId":"IP-116392","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":453891,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4081/gh.2020.818","text":"Publisher Index Page"},{"id":385281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Chamberlin, Andrew J","contributorId":221866,"corporation":false,"usgs":false,"family":"Chamberlin","given":"Andrew","email":"","middleInitial":"J","affiliations":[{"id":40446,"text":"Hopkins Marine Station, Stanford University","active":true,"usgs":false}],"preferred":false,"id":814619,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Isabel J.","contributorId":173135,"corporation":false,"usgs":false,"family":"Jones","given":"Isabel","email":"","middleInitial":"J.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":814620,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lund, Andrea J","contributorId":221868,"corporation":false,"usgs":false,"family":"Lund","given":"Andrea","email":"","middleInitial":"J","affiliations":[{"id":40447,"text":"Emmett Interdisciplinary Program in Environment and Resources, Stanford University","active":true,"usgs":false}],"preferred":false,"id":814621,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jouanard, Nicolas","contributorId":146316,"corporation":false,"usgs":false,"family":"Jouanard","given":"Nicolas","email":"","affiliations":[{"id":16664,"text":"20/20 Initiative","active":true,"usgs":false}],"preferred":false,"id":814622,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Riveau, Gilles","contributorId":146318,"corporation":false,"usgs":false,"family":"Riveau","given":"Gilles","email":"","affiliations":[{"id":16666,"text":"Institut Pasteur de Lille; laboratoire de Recherches Biomedicales","active":true,"usgs":false}],"preferred":false,"id":814623,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ndione, Raphael","contributorId":221876,"corporation":false,"usgs":false,"family":"Ndione","given":"Raphael","email":"","affiliations":[{"id":40451,"text":"Biomedical Research Center Espoir Pour La Santé, BP 226 Saint-Louis, Senegal","active":true,"usgs":false}],"preferred":false,"id":814624,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sokolow, Susanne H.","contributorId":52503,"corporation":false,"usgs":false,"family":"Sokolow","given":"Susanne","email":"","middleInitial":"H.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":814625,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wood, Chelsea L.","contributorId":192504,"corporation":false,"usgs":false,"family":"Wood","given":"Chelsea","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":814626,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":814627,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"De Leo, Giulio A.","contributorId":146323,"corporation":false,"usgs":false,"family":"De Leo","given":"Giulio","email":"","middleInitial":"A.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":814628,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70227448,"text":"70227448 - 2021 - Variation in species composition, size and fitness of two multi-species sea turtle assemblages using different neritic habitats","interactions":[],"lastModifiedDate":"2022-01-17T15:45:59.85159","indexId":"70227448","displayToPublicDate":"2021-01-08T09:25:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Variation in species composition, size and fitness of two multi-species sea turtle assemblages using different neritic habitats","docAbstract":"The neritic environment is rich in resources and as such plays a crucial role as foraging habitat for multi-species marine assemblages, including sea turtles. However, this habitat also experiences a wide array of anthropogenic threats. To prioritize conservation funds, targeting areas that support multi-species assemblages is ideal. This is particularly important in the Gulf of Mexico where restoration actions are currently ongoing following the Deepwater Horizon oil spill. To better understand these areas in the Gulf of Mexico, we characterized two multi-species aggregations of sea turtles captured in different neritic habitats. We described species composition and size classes of turtles, and calculated body condition index for 642 individuals of three species captured from 2011 to 2019: 13.6% loggerheads (Caretta caretta), 44.9% Kemp’s ridleys (Lepidochelys kempii) and 41.4% green turtles (Chelonia mydas). Species composition differed between the two sites with more loggerheads captured in seagrass and a greater proportion of green turtles captured in sand bottom. Turtles in sand bottom were smaller and weighed less than those captured in seagrass. Although small and large turtles were captured at both sites, the proportions differed between sites. Body condition index of green turtles was lower in sand habitat than seagrass habitat; there was no difference for Kemp’s ridleys or loggerheads. In general, smaller green turtles had a higher body condition index than larger green turtles. We have identified another habitat type used by juvenile sea turtle species in the northern Gulf of Mexico. In addition, we highlight the importance of habitat selection by immature turtles recruiting from the oceanic to the neritic environment, particularly for green turtles.","language":"English","publisher":"Frontiers Research Foundation","doi":"10.3389/fmars.2020.608740","usgsCitation":"Lamont, M.M., and Johnson, D., 2021, Variation in species composition, size and fitness of two multi-species sea turtle assemblages using different neritic habitats: Frontiers in Marine Science, v. 7, p. 1-11, https://doi.org/10.3389/fmars.2020.608740.","productDescription":"608740, 11 p.","startPage":"1","endPage":"11","ipdsId":"IP-122300","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":453893,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2020.608740","text":"Publisher Index Page"},{"id":394436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Choctawhatchee Bay, Destin Pass, Gulf of Mexico, Navarre Beach, Okaloosa Island, Santa Rosa Island, St. Joseph Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -85.35003662109374,\n              29.671349002200948\n            ],\n            [\n              -85.32669067382811,\n              29.683877073140415\n            ],\n            [\n              -85.308837890625,\n              29.686263195364013\n            ],\n            [\n              -85.30265808105469,\n              29.72145191669099\n            ],\n            [\n              -85.29922485351562,\n              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]\n}","volume":"7","noUsgsAuthors":false,"publicationDate":"2021-01-08","publicationStatus":"PW","contributors":{"editors":[{"text":"Putman, Nathan Freeman","contributorId":145423,"corporation":false,"usgs":false,"family":"Putman","given":"Nathan","email":"","middleInitial":"Freeman","affiliations":[{"id":16119,"text":"National Marine Fisheries Service, Miami, FL","active":true,"usgs":false}],"preferred":false,"id":831002,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Lamont, Margaret M. 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":218323,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","email":"","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":830939,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Darren 0000-0002-0502-6045","orcid":"https://orcid.org/0000-0002-0502-6045","contributorId":203921,"corporation":false,"usgs":true,"family":"Johnson","given":"Darren","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":830940,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70218764,"text":"70218764 - 2021 - Geochemistry of coastal permafrost and erosion-driven organic matter fluxes to the Beaufort Sea near Drew Point, Alaska","interactions":[],"lastModifiedDate":"2021-03-12T14:36:45.473522","indexId":"70218764","displayToPublicDate":"2021-01-08T08:31:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7753,"text":"Frontiers in  Earth Science","active":true,"publicationSubtype":{"id":10}},"title":"Geochemistry of coastal permafrost and erosion-driven organic matter fluxes to the Beaufort Sea near Drew Point, Alaska","docAbstract":"<div class=\"JournalAbstract\"><p class=\"mb15\">Accelerating erosion of the Alaska Beaufort Sea coast is increasing inputs of organic matter from land to the Arctic Ocean, and improved estimates of organic matter stocks in eroding coastal permafrost are needed to assess their mobilization rates under contemporary conditions. We collected three permafrost cores (4.5–7.5&nbsp;m long) along a geomorphic gradient near Drew Point, Alaska, where recent erosion rates average 17.2&nbsp;m&nbsp;year<sup>−1</sup>. Down-core patterns indicate that organic-rich soils and lacustrine sediments (12–45% total organic carbon; TOC) in the active layer and upper permafrost accumulated during the Holocene. Deeper permafrost (below 3&nbsp;m elevation) mainly consists of Late Pleistocene marine sediments with lower organic matter content (∼1% TOC), lower C:N ratios, and higher δ<sup>13</sup>C values. Radiocarbon-based estimates of organic carbon accumulation rates were 11.3 ± 3.6&nbsp;g TOC&nbsp;m<sup>−2</sup>&nbsp;year<sup>−1</sup><span>&nbsp;</span>during the Holocene and 0.5 ± 0.1&nbsp;g TOC&nbsp;m<sup>−2</sup>&nbsp;year<sup>−1</sup><span>&nbsp;</span>during the Late Pleistocene (12–38&nbsp;kyr BP). Within relict marine sediments, porewater salinities increased with depth. Elevated salinity near sea level (∼20–37 in thawed samples) inhibited freezing despite year-round temperatures below 0°C. We used organic matter stock estimates from the cores in combination with remote sensing time-series data to estimate carbon fluxes for a 9&nbsp;km stretch of coastline near Drew Point. Erosional fluxes of TOC averaged 1,369&nbsp;kg&nbsp;C&nbsp;m<sup>−1</sup>&nbsp;year<sup>−1</sup><span>&nbsp;</span>during the 21st century (2002–2018), nearly doubling the average flux of the previous half-century (1955–2002). Our estimate of the 21st century erosional TOC flux year<sup>−1</sup><span>&nbsp;</span>from this 9&nbsp;km coastline (12,318 metric tons C&nbsp;year<sup>−1</sup>) is similar to the annual TOC flux from the Kuparuk River, which drains a 8,107&nbsp;km<sup>2</sup><span>&nbsp;</span>area east of Drew Point and ranks as the third largest river on the North Slope of Alaska. Total nitrogen fluxes via coastal erosion at Drew Point were also quantified, and were similar to those from the Kuparuk River. This study emphasizes that coastal erosion represents a significant pathway for carbon and nitrogen trapped in permafrost to enter modern biogeochemical cycles, where it may fuel food webs and greenhouse gas emissions in the marine environment.</p></div>","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2020.598933","usgsCitation":"Bristol, E.M., Connolly, C.T., Lorenson, T., Richmond, B., Ilgen, A.G., Choens, C.R., Bull, D.L., Kanevskiy, M.Z., Iwahana, G., Jones, B., and McClelland, J., 2021, Geochemistry of coastal permafrost and erosion-driven organic matter fluxes to the Beaufort Sea near Drew Point, Alaska: Frontiers in  Earth Science, v. 8, 598933, 13 p., https://doi.org/10.3389/feart.2020.598933.","productDescription":"598933, 13 p.","ipdsId":"IP-123906","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453895,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.598933","text":"Publisher Index Page"},{"id":384352,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Drew Point","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -158.09326171875,\n              70.52123408593832\n            ],\n            [\n              -151.072998046875,\n              70.52123408593832\n            ],\n            [\n              -151.072998046875,\n              71.37812702610609\n            ],\n            [\n              -158.09326171875,\n              71.37812702610609\n            ],\n            [\n              -158.09326171875,\n              70.52123408593832\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2021-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Bristol, Emily M.","contributorId":255060,"corporation":false,"usgs":false,"family":"Bristol","given":"Emily","email":"","middleInitial":"M.","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":811740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connolly, Craig T.","contributorId":255063,"corporation":false,"usgs":false,"family":"Connolly","given":"Craig","email":"","middleInitial":"T.","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":811741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lorenson, Thomas 0000-0001-7669-2873 tlorenson@usgs.gov","orcid":"https://orcid.org/0000-0001-7669-2873","contributorId":174599,"corporation":false,"usgs":true,"family":"Lorenson","given":"Thomas","email":"tlorenson@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":811742,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richmond, Bruce M.","contributorId":255065,"corporation":false,"usgs":false,"family":"Richmond","given":"Bruce M.","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":811743,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ilgen, Anastasia G.","contributorId":255069,"corporation":false,"usgs":false,"family":"Ilgen","given":"Anastasia","email":"","middleInitial":"G.","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":811744,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Choens, Charles R.","contributorId":255072,"corporation":false,"usgs":false,"family":"Choens","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":811745,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bull, Diana L.","contributorId":208628,"corporation":false,"usgs":false,"family":"Bull","given":"Diana","email":"","middleInitial":"L.","affiliations":[{"id":37851,"text":"Sandia National Laboratories, Albuquerque, New Mexico, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":811746,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kanevskiy, Mikhail Z.","contributorId":199153,"corporation":false,"usgs":false,"family":"Kanevskiy","given":"Mikhail","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":811747,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Iwahana, Go 0000-0003-4628-1074","orcid":"https://orcid.org/0000-0003-4628-1074","contributorId":208638,"corporation":false,"usgs":false,"family":"Iwahana","given":"Go","email":"","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":811748,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jones, Benjamin M. 0000-0002-1517-4711","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":208625,"corporation":false,"usgs":false,"family":"Jones","given":"Benjamin M.","affiliations":[{"id":37848,"text":"Water and Environmental Research Center, University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":true,"id":811749,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McClelland, James W.","contributorId":255074,"corporation":false,"usgs":false,"family":"McClelland","given":"James W.","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":811750,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70227201,"text":"70227201 - 2021 - Radiometric constraints on the timing, tempo, and effects of large igneous province emplacement","interactions":[],"lastModifiedDate":"2022-01-04T14:36:08.660114","indexId":"70227201","displayToPublicDate":"2021-01-08T08:28:39","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"2","title":"Radiometric constraints on the timing, tempo, and effects of large igneous province emplacement","docAbstract":"<p><span>There is an apparent temporal correlation between large igneous province (LIP) emplacement and global environmental crises, including mass extinctions. Advances in the precision and accuracy of geochronology in the past decade have significantly improved estimates of the timing and duration of LIP emplacement, mass extinction events, and global climate perturbations, and in general have supported a temporal link between them. In this chapter, we review available geochronology of LIPs and of global extinction or climate events. We begin with an overview of the methodological advances permitting improved precision and accuracy in LIP geochronology. We then review the characteristics and geochronology of 12 LIP/event couplets from the past 700 Ma of Earth history, comparing the relative timing of magmatism and global change, and assessing the chronologic support for LIPs playing a causal role in Earth's climatic and biotic crises. We find that (1) improved geochronology in the last decade has shown that nearly all well-dated LIPs erupted in &lt; 1 Ma, irrespective of tectonic setting; (2) for well-dated LIPs with correspondingly well-dated mass extinctions, the LIPs began several hundred ka prior to a relatively short duration extinction event; and (3) for LIPs with a convincing temporal connection to mass extinctions, there seems to be no single characteristic that makes a LIP deadly. Despite much progress, higher precision geochronology of both eruptive and intrusive LIP events and better chronologies from extinction and climate proxy records will be required to further understand how these catastrophic volcanic events have changed the course of our planet's surface evolution.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Large igneous provinces: A driver of global environmental and biotic changes","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Wiley","doi":"10.1002/9781119507444.ch2","usgsCitation":"Kasbohm, J., Schoene, B., and Burgess, S.D., 2021, Radiometric constraints on the timing, tempo, and effects of large igneous province emplacement, chap. 2 <i>of</i> Large igneous provinces: A driver of global environmental and biotic changes, p. 27-82, https://doi.org/10.1002/9781119507444.ch2.","productDescription":"56 p.","startPage":"27","endPage":"82","ipdsId":"IP-113395","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":393846,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kasbohm, Jennifer","contributorId":270796,"corporation":false,"usgs":false,"family":"Kasbohm","given":"Jennifer","email":"","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":830063,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoene, Blair","contributorId":270797,"corporation":false,"usgs":false,"family":"Schoene","given":"Blair","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":830064,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burgess, Seth D. 0000-0002-4238-3797 sburgess@usgs.gov","orcid":"https://orcid.org/0000-0002-4238-3797","contributorId":200371,"corporation":false,"usgs":true,"family":"Burgess","given":"Seth","email":"sburgess@usgs.gov","middleInitial":"D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":830065,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70222486,"text":"70222486 - 2021 - Using high sample rate lidar to measure debris-flow velocity and surface geometry","interactions":[],"lastModifiedDate":"2021-07-30T13:28:47.272777","indexId":"70222486","displayToPublicDate":"2021-01-08T08:27:26","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7559,"text":"Environmental and Engineering Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Using high sample rate lidar to measure debris-flow velocity and surface geometry","docAbstract":"<div class=\"article-section-wrapper js-article-section js-content-section  \"><p>Debris flows evolve in both time and space in complex ways, commonly starting as coherent failures but then quickly developing structures such as roll waves and surges. These processes are readily observed but difficult to study or quantify because of the speed at which they evolve. Many methods for studying debris flows consist of point measurements (e.g., flow height or basal stresses), which are inherently limited in spatial coverage and cannot fully characterize the spatiotemporal evolution of a flow. In this study, we use terrestrial lidar to measure debris-flow profiles at high sampling rates to examine debris-flow movement with high temporal and spatial precision and accuracy. We acquired measurements during gate-release experiments at the U.S. Geological Survey debris-flow flume, a unique experimental facility where debris flows can be artificially generated at a large scale. A lidar scanner was used to record repeat topographic profiles of the moving debris flows along the length of the flume with a narrow swath width (∼1 mm) at a rate of 60 Hz. The high-resolution lidar profiles enabled us to quantify flow front velocity of the debris flows and provided an unprecedented record of the development and evolution of the flow structure with a sub-second time resolution. The findings of this study demonstrate how to obtain quantitative measurements of debris-flow movement. In addition, the data help us to quantitatively define the development of a saltating debris-flow front and roll waves behind the debris-flow front. Such measurements may help constrain future modeling efforts.</p></div>","language":"English","publisher":"Association of Environmental and Engineering Geologists","doi":"10.2113/EEG-D-20-00045","usgsCitation":"Rengers, F.K., Rapstine, T.D., Olsen, M., Allstadt, K.E., Iverson, R.M., Leshchinsky, B., Obryk, M., and Smith, J., 2021, Using high sample rate lidar to measure debris-flow velocity and surface geometry: Environmental and Engineering Geoscience, v. 27, no. 1, p. 113-126, https://doi.org/10.2113/EEG-D-20-00045.","productDescription":"14 p.","startPage":"113","endPage":"126","ipdsId":"IP-122361","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":436591,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OU3U4P","text":"USGS data release","linkHelpText":"Lidar data for gate release experiment at the USGS Debris-Flow Flume 24 and 25 May 2017"},{"id":387585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820187,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rapstine, Thomas D 0000-0001-5939-9587","orcid":"https://orcid.org/0000-0001-5939-9587","contributorId":224777,"corporation":false,"usgs":true,"family":"Rapstine","given":"Thomas","email":"","middleInitial":"D","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820188,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olsen, Michael","contributorId":215348,"corporation":false,"usgs":false,"family":"Olsen","given":"Michael","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":820189,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820190,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Iverson, Richard M. 0000-0002-7369-3819 riverson@usgs.gov","orcid":"https://orcid.org/0000-0002-7369-3819","contributorId":536,"corporation":false,"usgs":true,"family":"Iverson","given":"Richard","email":"riverson@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":820191,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leshchinsky, Ben","contributorId":215350,"corporation":false,"usgs":false,"family":"Leshchinsky","given":"Ben","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":820192,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Obryk, Maciej K. 0000-0002-8182-8656","orcid":"https://orcid.org/0000-0002-8182-8656","contributorId":203477,"corporation":false,"usgs":true,"family":"Obryk","given":"Maciej","middleInitial":"K.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":820193,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Smith, Joel B. 0000-0001-7219-7875","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":242670,"corporation":false,"usgs":false,"family":"Smith","given":"Joel B.","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":820194,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70217215,"text":"70217215 - 2021 - Groundwater discharge impacts marine isotope budgets of Li, Mg, Ca, Sr, and Ba","interactions":[],"lastModifiedDate":"2021-01-13T13:34:23.992154","indexId":"70217215","displayToPublicDate":"2021-01-08T07:26:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater discharge impacts marine isotope budgets of Li, Mg, Ca, Sr, and Ba","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Groundwater-derived solute fluxes to the ocean have long been assumed static and subordinate to riverine fluxes, if not neglected entirely, in marine isotope budgets. Here we present concentration and isotope data for Li, Mg, Ca, Sr, and Ba in coastal groundwaters to constrain the importance of groundwater discharge in mediating the magnitude and isotopic composition of terrestrially derived solute fluxes to the ocean. Data were extrapolated globally using three independent volumetric estimates of groundwater discharge to coastal waters, from which we estimate that groundwater-derived solute fluxes represent, at a minimum, 5% of riverine fluxes for Li, Mg, Ca, Sr, and Ba. The isotopic compositions of the groundwater-derived Mg, Ca, and Sr fluxes are distinct from global riverine averages, while Li and Ba fluxes are isotopically indistinguishable from rivers. These differences reflect a strong dependence on coastal lithology that should be considered a priority for parameterization in Earth-system models.</p></div></div><div id=\"Sec1-section\" class=\"c-article-section\"><br></div>","language":"English","publisher":"Nature","doi":"10.1038/s41467-020-20248-3","usgsCitation":"Mayfield, K., Eisenhauer, A., Santiago Ramos, D.P., Higgins, J.A., Horner, T., Auro, M., Magna, T., Moosdorf, N., Charette, M., Gonneea Eagle, M., Brady, C., Komar, N., Peucker-Ehrenbrink, B., and Paytan, A., 2021, Groundwater discharge impacts marine isotope budgets of Li, Mg, Ca, Sr, and Ba: Nature Communications, v. 12, 148, 9 p., https://doi.org/10.1038/s41467-020-20248-3.","productDescription":"148, 9 p.","ipdsId":"IP-115760","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453901,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-020-20248-3","text":"Publisher Index Page"},{"id":382125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2021-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Mayfield, Kimberly","contributorId":247615,"corporation":false,"usgs":false,"family":"Mayfield","given":"Kimberly","email":"","affiliations":[{"id":49595,"text":"University of California at Santa Cruz, Santa Cruz, USA","active":true,"usgs":false}],"preferred":false,"id":808038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eisenhauer, Anton","contributorId":247616,"corporation":false,"usgs":false,"family":"Eisenhauer","given":"Anton","email":"","affiliations":[{"id":49597,"text":"GEOMAR Helmholtz Center for Ocean Research, Kiel, Germany","active":true,"usgs":false}],"preferred":false,"id":808039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Santiago Ramos, Danielle P.","contributorId":199530,"corporation":false,"usgs":false,"family":"Santiago Ramos","given":"Danielle","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":808040,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Higgins, John A.","contributorId":199534,"corporation":false,"usgs":false,"family":"Higgins","given":"John","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":808041,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Horner, Tristan","contributorId":199943,"corporation":false,"usgs":false,"family":"Horner","given":"Tristan","email":"","affiliations":[],"preferred":false,"id":808042,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Auro, Maureen","contributorId":247617,"corporation":false,"usgs":false,"family":"Auro","given":"Maureen","affiliations":[{"id":49599,"text":"Woods Hole Oceanographic Institution, Woods Hole, USA","active":true,"usgs":false}],"preferred":false,"id":808043,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Magna, Tomas","contributorId":247618,"corporation":false,"usgs":false,"family":"Magna","given":"Tomas","email":"","affiliations":[{"id":49600,"text":"Czech Geological Survey, Prague, Czech Republic","active":true,"usgs":false}],"preferred":false,"id":808044,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moosdorf, Nils","contributorId":191149,"corporation":false,"usgs":false,"family":"Moosdorf","given":"Nils","email":"","affiliations":[],"preferred":false,"id":808045,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Charette, Matthew","contributorId":247619,"corporation":false,"usgs":false,"family":"Charette","given":"Matthew","affiliations":[{"id":49599,"text":"Woods Hole Oceanographic Institution, Woods Hole, USA","active":true,"usgs":false}],"preferred":false,"id":808046,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gonneea Eagle, Meagan 0000-0001-5072-2755 mgonneea@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":174590,"corporation":false,"usgs":true,"family":"Gonneea Eagle","given":"Meagan","email":"mgonneea@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":808047,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Brady, Carolyn","contributorId":247620,"corporation":false,"usgs":false,"family":"Brady","given":"Carolyn","email":"","affiliations":[{"id":49595,"text":"University of California at Santa Cruz, Santa Cruz, USA","active":true,"usgs":false}],"preferred":false,"id":808048,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Komar, Nemanja","contributorId":247621,"corporation":false,"usgs":false,"family":"Komar","given":"Nemanja","email":"","affiliations":[{"id":49601,"text":"University of Hawai`i at Manoa, Manoa, HI, USA","active":true,"usgs":false}],"preferred":false,"id":808049,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Peucker-Ehrenbrink, Bernhard","contributorId":247622,"corporation":false,"usgs":false,"family":"Peucker-Ehrenbrink","given":"Bernhard","affiliations":[{"id":49599,"text":"Woods Hole Oceanographic Institution, Woods Hole, USA","active":true,"usgs":false}],"preferred":false,"id":808050,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Paytan, Adina","contributorId":140909,"corporation":false,"usgs":false,"family":"Paytan","given":"Adina","affiliations":[{"id":13611,"text":"Institute of Marine Sciences, University of California, Santa Cruz.","active":true,"usgs":false}],"preferred":false,"id":808051,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}}
,{"id":70225599,"text":"70225599 - 2021 - Creel surveys for social-ecological systems focused fisheries management","interactions":[],"lastModifiedDate":"2021-10-27T12:27:04.364683","indexId":"70225599","displayToPublicDate":"2021-01-08T07:26:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5040,"text":"Reviews in Fisheries Science & Aquaculture","onlineIssn":"2330-8257","printIssn":"2330-8249","active":true,"publicationSubtype":{"id":10}},"title":"Creel surveys for social-ecological systems focused fisheries management","docAbstract":"<div class=\"hlFld-Abstract test\"><div class=\"abstractSection abstractInFull\"><p>Recreational fisheries are social-ecological systems (SES), and knowledge of human dimensions coupled with ecology are critically needed to understand their system dynamics. Creel surveys, which typically occur in-person and on-site, serve as an important tool for informing fisheries management. Recreational fisheries creel data have the potential to inform large-scale understanding of social and ecological dynamics, but applications are currently limited by a disconnect between the questions posed by social-ecological researchers and the methods in which surveys are conducted. Although innovative use of existing data can increase understanding of recreational fisheries as SES, creel surveys should also adapt to changing information needs. These opportunities include using the specific temporal and spatial scope of creel survey data, integrating these data with alternative data sources, and increasing human dimensions understanding. This review provides recommendations for adapting survey design, implementation, and analysis for SES-focused fisheries management. These recommendations are: (1) increasing human dimensions knowledge; (2) standardization of surveys and data; (3) increasing tools and training available to fisheries scientists; and (4) increasing accessibility and availability of data. Incorporation of human dimensions information into creel surveys will increase the ability of fisheries management to regulate these important systems from an integrated SES standpoint.</p></div></div>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/23308249.2020.1869696","usgsCitation":"Nieman, C.L., Iwicki, C., Lynch, A., Sass, G.G., Solomon, C.T., Trudeau, A., and van Poorten, B., 2021, Creel surveys for social-ecological systems focused fisheries management: Reviews in Fisheries Science & Aquaculture, v. 29, no. 4, p. 739-752, https://doi.org/10.1080/23308249.2020.1869696.","productDescription":"14 p.","startPage":"739","endPage":"752","ipdsId":"IP-122914","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":391007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Nieman, Chelsey L.","contributorId":268059,"corporation":false,"usgs":false,"family":"Nieman","given":"Chelsey","email":"","middleInitial":"L.","affiliations":[{"id":55543,"text":"Cary Institute","active":true,"usgs":false}],"preferred":false,"id":825779,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iwicki, Carolyn","contributorId":268060,"corporation":false,"usgs":false,"family":"Iwicki","given":"Carolyn","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":825780,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lynch, Abigail 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":220490,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":825781,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sass, Greg G.","contributorId":207135,"corporation":false,"usgs":false,"family":"Sass","given":"Greg","email":"","middleInitial":"G.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":825782,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Solomon, Christopher T.","contributorId":34014,"corporation":false,"usgs":false,"family":"Solomon","given":"Christopher","email":"","middleInitial":"T.","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":825783,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Trudeau, Ashley","contributorId":245555,"corporation":false,"usgs":false,"family":"Trudeau","given":"Ashley","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":825784,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"van Poorten, Brett","contributorId":268061,"corporation":false,"usgs":false,"family":"van Poorten","given":"Brett","email":"","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":825785,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70217205,"text":"70217205 - 2021 - Muted responses to chronic experimental nitrogen deposition on the Colorado Plateau","interactions":[],"lastModifiedDate":"2021-02-17T21:57:48.74055","indexId":"70217205","displayToPublicDate":"2021-01-08T07:04:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Muted responses to chronic experimental nitrogen deposition on the Colorado Plateau","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Anthropogenic nitrogen (N) deposition is significantly altering both community structure and ecosystem processes in terrestrial ecosystems across the globe. However, our understanding of the consequences of N deposition in dryland systems remains relatively poor, despite evidence that drylands may be particularly vulnerable to increasing N inputs. In this study, we investigated the influence of 7 years of multiple levels of simulated N deposition (0, 2, 5, and 8&nbsp;kg&nbsp;N&nbsp;ha<sup>−1</sup>&nbsp;year<sup>−1</sup>) on plant community structure and biological soil crust (biocrust) cover at three semi-arid grassland sites spanning a soil texture gradient. Biocrusts are a surface community of mosses, lichens, cyanobacteria, and/or algae, and have been shown to be sensitive to N inputs. We hypothesized that N additions would decrease plant diversity, increase abundance of the invasive annual grass<span>&nbsp;</span><i>Bromus tectorum,</i><span>&nbsp;</span>and decrease biocrust cover. Contrary to our expectations, we found that N additions did not affect plant diversity or<span>&nbsp;</span><i>B. tectorum</i><span>&nbsp;</span>abundance. In partial support of our hypotheses, N additions negatively affected biocrust cover in some years, perhaps driven in part by inter-annual differences in precipitation. Soil inorganic N concentrations showed rapid but ephemeral responses to N additions and plant foliar N concentrations showed no response, indicating that the magnitude of plant and biocrust responses to N fertilization may be buffered by endogenous N cycling. More work is needed to determine N critical load thresholds for plant community and biocrust dynamics in semi-arid systems and the factors that determine the fate of N inputs.</p></div></div><div id=\"cobranding-and-download-availability-text\" class=\"note test-pdf-link\"><br></div>","language":"English","publisher":"Springer","doi":"10.1007/s00442-020-04841-3","usgsCitation":"Phillips, M.L., Winkler, D.E., Reibold, R.H., Osborne, B.B., and Reed, S., 2021, Muted responses to chronic experimental nitrogen deposition on the Colorado Plateau: Oecologia, v. 195, p. 513-524, https://doi.org/10.1007/s00442-020-04841-3.","productDescription":"12 p.","startPage":"513","endPage":"524","ipdsId":"IP-124433","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":382081,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112.4560546875,\n              32.84267363195431\n            ],\n            [\n              -105.4248046875,\n              32.84267363195431\n            ],\n            [\n              -105.4248046875,\n              40.3130432088809\n            ],\n            [\n              -112.4560546875,\n              40.3130432088809\n            ],\n            [\n              -112.4560546875,\n              32.84267363195431\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"195","noUsgsAuthors":false,"publicationDate":"2021-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Phillips, Michala Lee 0000-0001-7005-8740","orcid":"https://orcid.org/0000-0001-7005-8740","contributorId":245186,"corporation":false,"usgs":true,"family":"Phillips","given":"Michala","email":"","middleInitial":"Lee","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":807994,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":807995,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reibold, Robin H. 0000-0002-3323-487X","orcid":"https://orcid.org/0000-0002-3323-487X","contributorId":207499,"corporation":false,"usgs":true,"family":"Reibold","given":"Robin","email":"","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":807996,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Osborne, Brooke Bossert 0000-0003-4771-7677","orcid":"https://orcid.org/0000-0003-4771-7677","contributorId":247600,"corporation":false,"usgs":true,"family":"Osborne","given":"Brooke","email":"","middleInitial":"Bossert","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":807997,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":807998,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70217135,"text":"sir20205131 - 2021 - The use of continuous water-quality time-series data to compute total phosphorus loadings for the Turkey River at Garber, Iowa, 2018–20","interactions":[],"lastModifiedDate":"2021-01-11T12:51:51.34034","indexId":"sir20205131","displayToPublicDate":"2021-01-07T17:25:00","publicationYear":"2021","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":"2020-5131","displayTitle":"The Use of Continuous Water-Quality Time-Series Data to Compute Total Phosphorus Loadings for the Turkey River at Garber, Iowa, 2018–20","title":"The use of continuous water-quality time-series data to compute total phosphorus loadings for the Turkey River at Garber, Iowa, 2018–20","docAbstract":"<p>In support of nutrient reduction efforts, total phosphorus loads and yields were computed for the Turkey River at Garber, Iowa (U.S. Geological Survey station 05412500), for January 1, 2018, to April 30, 2020, based on continuously monitored turbidity sensor data. Sample data were used to create a total phosphorus turbidity-surrogate model. Streamflow-based total phosphorus models were used during periods of missing sensor data to obtain a more complete annual total phosphorus load. This report presents methods needed to accurately compute site-specific loads and track annual progress toward nutrient reduction goals within the State.</p><p>Annual total phosphorus loads for the Turkey River at Garber, Iowa, were 1,740 and 1,490 U.S. short tons for 2018 and 2019, respectively, with annual yields ranging from 3.01 to 3.53 pounds per acre per year, compared to a mean statewide yield of 0.73 pound per acre per year needed to achieve the total phosphorus-reduction goal.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205131","collaboration":"Prepared in cooperation with the Iowa Department of Natural Resources","usgsCitation":"Garrett, J.D., 2021, The use of continuous water-quality time-series data to compute total phosphorus loadings for the Turkey River at Garber, Iowa, 2018–20: U.S. Geological Survey Scientific Investigations Report 2020–5131, 13 p., https://doi.org/10.3133/sir20205131.","productDescription":"Report: vi, 13 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119794","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":381971,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5131/sir20205131.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5131"},{"id":381970,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5131/coverthb.jpg"},{"id":382022,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"National Water Information System"}],"country":"United States","state":"Iowa","city":"Garber","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -91.27939224243163,\n              42.73276565598371\n            ],\n            [\n              -91.24471664428711,\n              42.73276565598371\n            ],\n            [\n              -91.24471664428711,\n              42.74953333969568\n            ],\n            [\n              -91.27939224243163,\n              42.74953333969568\n            ],\n            [\n              -91.27939224243163,\n              42.73276565598371\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/cm-water/\" data-mce-href=\"https://www.usgs.gov/centers/cm-water/\">Central Midwest Water Science Center</a><br>U.S. Geological Survey<br>400 South Clinton Street, Suite 269<br>Iowa City, IA 52240</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods for Data Collection and Computation</li><li>Sample Water-Quality and Sensor Data</li><li>Continuous Water-Quality Time-Series Data to Compute Nutrient Loadings</li><li>Summary</li><li>References Cited</li></ul>","publishedDate":"2021-01-07","noUsgsAuthors":false,"publicationDate":"2021-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Garrett, Jessica D. 0000-0002-4466-3709 jgarrett@usgs.gov","orcid":"https://orcid.org/0000-0002-4466-3709","contributorId":4229,"corporation":false,"usgs":true,"family":"Garrett","given":"Jessica","email":"jgarrett@usgs.gov","middleInitial":"D.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807718,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70217094,"text":"sir20205119 - 2021 - Trends in groundwater levels in and near the Rosebud Indian Reservation, South Dakota, water years 1956–2017","interactions":[],"lastModifiedDate":"2021-01-08T12:48:31.039196","indexId":"sir20205119","displayToPublicDate":"2021-01-07T15:35:00","publicationYear":"2021","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":"2020-5119","displayTitle":"Trends in Groundwater Levels in and near the Rosebud Indian Reservation, South Dakota, Water Years 1956–2017","title":"Trends in groundwater levels in and near the Rosebud Indian Reservation, South Dakota, water years 1956–2017","docAbstract":"<p>The U.S. Geological Survey (USGS), in cooperation with the Rosebud Sioux Tribe, completed a study to characterize water-level fluctuations in observation wells to examine driving factors that affect water levels in and near the Rosebud Indian Reservation, which comprises all of Todd County. The study investigates concerns regarding potential effects of groundwater withdrawals and climate conditions on groundwater levels within an area that includes Todd County and a surrounding area that extends 10 miles north, east, and west of the county border. Characterization of water-level fluctuations in observation wells and relative driving factors was accomplished by statistical trend analysis.</p><p>Two statistical methods were used for analysis of temporal trends for climatic and hydrologic data. To determine which trend analysis to use, applicable datasets were tested for statistically significant short-term persistence (STP). In the absence of significant STP, existence of statistical trends was determined using the standard Mann-Kendall test for probability values less than or equal to 0.10 (90-percent confidence level); however, a modified Mann-Kendall test was used for datasets where statistically significant STP was detected. Trend magnitudes were computed using the Sen’s slope estimator.</p><p>Monthly data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) were aggregated to obtain annual and seasonal datasets for total precipitation, minimum air temperature (<i>T<sub>min</sub></i>), and maximum air temperature (<i>T<sub>max</sub></i>) for the study area and a surrounding buffer area. Trend tests for total precipitation,<i> T<sub>min</sub></i>, and <i>T<sub>max</sub></i> were completed for annual and seasonal time series for water years 1956–2017, which is about 2 years before the earliest available water-level measurements. A 2-year offset was arbitrarily selected because scrutiny of water-level and precipitation data indicated that responses of groundwater levels for many of the observation wells lagged major changes in precipitation patterns by about 2 years. Statistically significant upward trends were detected for annual precipitation and annual <i>T<sub>min</sub></i> for almost all of the study area and the surrounding buffer area. Statistically significant downward trends in <i>T<sub>max</sub></i> were detected for a very small part of the study area; however, the sparse spatial coverage reduces confidence that these are true trends. Spatial distributions of statistically significant trends in seasonal climate data were generally similar to the annual trends, but with substantial differences in the spatial density of the trends.</p><p>Groundwater trends for 58 observation wells were analyzed for three separate water-level parameters (minimum, median, and maximum) because wells are measured sporadically and data are biased towards more frequent measurements during periods of heaviest irrigation demand. Trends in the time series of annual precipitation (from PRISM) starting 2 years earlier than for the associated water-level trend also were analyzed for the location of each individual observation well. Sen’s slope and Mann-Kendall probability values (p-values) were computed for the three water-level parameters and for the annual precipitation time series. Graphs showing results of trend analyses for each observation well also showed changes over time in the sum of licensed groundwater withdrawals within six specified radii (0.5, 1, 2, 3, 4, and 5 miles) of each well as a qualitative indicator of proximal groundwater demand.</p><p>Of all 58 observation wells considered, 28 wells had significant upward trends for at least one of the three water-level parameters, 11 wells had significant downward trends for at least one water-level parameter, and 19 wells did not have any significant trends. Significant upward trends in annual precipitation were detected for 48 of the 58 wells.</p><p>Results of trend analyses likely show the effects of groundwater withdrawals on water levels in the Ogallala aquifer in areas of substantial demand. Precipitation trends are significantly upward for 43 of the 48 wells completed in the Ogallala aquifer that were analyzed. Of the 48 Ogallala aquifer wells, 24 had significant upward trends for at least one water-level parameter (17 with all 3); however, 10 wells had statistically significant downward trends for at least one water-level parameter (8 with all 3 parameters). All but one of the wells with significant downward trends are located in the south-central part of the study area where licensed irrigation withdrawals are concentrated.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205119","collaboration":"Prepared in cooperation with the Rosebud Sioux Tribe","usgsCitation":"Valseth, K.J., and Driscoll, D.G., 2021, Trends in groundwater levels in and near the Rosebud Indian Reservation, South Dakota, water years 1956–2017: U.S. Geological Survey Scientific Investigations Report 2020–5119, 46 p., https://doi.org/10.3133/sir20205119.","productDescription":"Report: v, 46 p.; 2 Appendixes; Data Release","onlineOnly":"Y","ipdsId":"IP-111377","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":382008,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"National Water Information System"},{"id":381910,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5119/sir20205119_appendix2.pdf","text":"Appendix 2","size":"132 kB","description":"SIR 2020-5119 Appendix 2"},{"id":381909,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5119/sir20205119_appendix1.pdf","text":"Appendix 1","size":"404 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5119 Appendix 1"},{"id":381908,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5119/sir20205119.pdf","text":"Report","size":"4.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5119"},{"id":381907,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5119/coverthb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Rosebud Indian Reservation","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -101.612548828125,\n              43.01268088642034\n            ],\n            [\n              -99.8492431640625,\n              43.01268088642034\n            ],\n            [\n              -99.8492431640625,\n              43.600284023536325\n            ],\n            [\n              -101.612548828125,\n              43.600284023536325\n            ],\n            [\n              -101.612548828125,\n              43.01268088642034\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/dakota-water/\" data-mce-href=\"https://www.usgs.gov/centers/dakota-water/\">Dakota Water Science Center</a><br>U.S. Geological Survey<br>821 East Interstate Avenue, Bismarck, ND 58503<br>1608 Mountain View Road, Rapid City, SD 57702</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Data Sources and Analytical Methods</li><li>Analysis of Trends</li><li>Summary</li><li>References Cited</li><li>Appendix 1</li><li>Appendix 2</li></ul>","publishedDate":"2021-01-07","noUsgsAuthors":false,"publicationDate":"2021-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Valseth, Kristen J. 0000-0003-4257-6094","orcid":"https://orcid.org/0000-0003-4257-6094","contributorId":203447,"corporation":false,"usgs":true,"family":"Valseth","given":"Kristen","email":"","middleInitial":"J.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807598,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driscoll, Daniel G. 0000-0003-0016-8535 dgdrisco@usgs.gov","orcid":"https://orcid.org/0000-0003-0016-8535","contributorId":207583,"corporation":false,"usgs":true,"family":"Driscoll","given":"Daniel","email":"dgdrisco@usgs.gov","middleInitial":"G.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807599,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70217126,"text":"sir20205136 - 2021 - Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM)","interactions":[],"lastModifiedDate":"2021-01-07T19:55:25.469018","indexId":"sir20205136","displayToPublicDate":"2021-01-07T15:05:00","publicationYear":"2021","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":"2020-5136","displayTitle":"Statistical Methods for Simulating Structural Stormwater Runoff Best Management Practices (BMPs) With the Stochastic Empirical Loading and Dilution Model (SELDM)","title":"Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM)","docAbstract":"<p>This report documents statistics for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM). The U.S. Geological Survey developed SELDM and the statistics documented in this report in cooperation with the Federal Highway Administration to indicate the risk for stormwater flows, concentrations, and loads to exceed user-selected water-quality goals and the potential effectiveness of mitigation measures to reduce such risks. In SELDM, three treatment variables—hydrograph extension, volume reduction, and water-quality treatment—are simulated by using the trapezoidal distribution and the rank correlation with the associated runoff variables. This report describes methods for calculating the trapezoidal distribution statistics and rank correlation coefficients for these treatment variables and methods for estimating the minimum irreducible concentration (MIC), which is the lowest expected effluent concentration from a BMP site or a category of BMPs. These statistics are different from the statistics commonly used to characterize or compare BMPs; they are designed to provide a stochastic transfer function to approximate the quantity, duration, and quality of BMP effluent given the associated inflow values for a population of storm events.</p><p>Analyses for this study were done with data extracted from a modified copy of the December 2019 version of the International Stormwater Best Management Practices Database. Statistics for volume reduction, hydrograph extension, and water-quality treatment were developed with selected data. The medians of the best-fit statistics for selected constituents were used to construct generalized cumulative distribution functions for the three treatment variables. For volume reduction and hydrograph extension, selection of a Spearman’s rank correlation coefficient (rho) value that is the average of the median and maximum values for the BMP category may help generate realistic simulation results in SELDM. The median rho value may be selected to help generate realistic simulation results for water-quality treatment variables.</p><p>Water-quality treatment statistics, including trapezoidal ratios and MIC values, were developed for 51 runoff-quality constituents commonly measured in highway and urban runoff studies. Statistics were calculated for water-quality properties, sediment and solids, nutrients, major and trace inorganic elements, organic compounds, and biologic constituents.</p><p>Analysis of MIC values provides information to guide professional judgement for selecting values for simulating water quality at sites of interest. The MIC is a lower bound for BMP discharge concentrations and will therefore replace simulated BMP discharge concentrations below the selected value. A new method for estimating MIC values, the lognormal variate of inflow concentrations, was developed in this report and these statistics were calculated for individual constituents and constituent categories. Inflow quality is correlated to MIC values for some constituents, but regional soil concentrations were not strongly correlated to MIC values.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205136","collaboration":"Prepared in cooperation with the Federal Highway Administration","usgsCitation":"Granato, G.E., Spaetzel, A.B., and Medalie, L., 2021, Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2020–5136, 41 p., https://doi.org/10.3133/sir20205136.","productDescription":"Report: 41 p.; 4 Tables; Data Release; Software Release","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119618","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":381933,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.04.txt","text":"Table 1.4","size":"89.4 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Estimates of correlations between the geometric mean concentration of inflows and selected minimum irreducible concentration estimates"},{"id":381930,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.01.txt","text":"Table 1.1","size":"91.2 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Median of selected treatment statistics for individual constituents"},{"id":381932,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.03.txt","text":"Table 1.3","size":"89.2 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Estimates of the lognormal variate values of selected minimum irreducible concentrations"},{"id":381929,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X3ECTD","text":"USGS data release","linkHelpText":"Statistics for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM)"},{"id":381927,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136.pdf","text":"Report","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5136"},{"id":381928,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9XBPIOB","text":"USGS software release","linkHelpText":"- Best Management Practices Statistical Estimator (BMPSE) Version 1.2.0"},{"id":381931,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.02.txt","text":"Table 1.2","size":"87.5 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Estimates of the minimum irreducible concentration"},{"id":381926,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5136/coverthb.jpg"}],"contact":"<p><a href=\"mailto:dc_nweng@usgs.gov\" data-mce-href=\"mailto:dc_nweng@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-england-water\" data-mce-href=\"https://www.usgs.gov/centers/new-england-water\">New England Water Science Center</a><br>U.S. Geological Survey<br>10 Bearfoot Road<br>Northborough, MA 01532</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results of Analyses</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Water-Quality Treatment Statistics for Individual Constituents</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-01-07","noUsgsAuthors":false,"publicationDate":"2021-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Granato, Gregory E. 0000-0002-2561-9913 ggranato@usgs.gov","orcid":"https://orcid.org/0000-0002-2561-9913","contributorId":197631,"corporation":false,"usgs":true,"family":"Granato","given":"Gregory","email":"ggranato@usgs.gov","middleInitial":"E.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807671,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spaetzel, Alana B. 0000-0002-9871-812X","orcid":"https://orcid.org/0000-0002-9871-812X","contributorId":240935,"corporation":false,"usgs":true,"family":"Spaetzel","given":"Alana","email":"","middleInitial":"B.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807672,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Medalie, Laura 0000-0002-2440-2149 lmedalie@usgs.gov","orcid":"https://orcid.org/0000-0002-2440-2149","contributorId":3657,"corporation":false,"usgs":true,"family":"Medalie","given":"Laura","email":"lmedalie@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807673,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70217128,"text":"ofr20201132 - 2021 - U.S. Geological Survey Community for Data Integration 2019 Workshop Proceedings—From big data to smart data","interactions":[],"lastModifiedDate":"2021-01-08T12:52:02.874636","indexId":"ofr20201132","displayToPublicDate":"2021-01-07T13:30:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1132","displayTitle":"U.S. Geological Survey Community for Data Integration 2019 Workshop Proceedings—From Big Data to Smart Data","title":"U.S. Geological Survey Community for Data Integration 2019 Workshop Proceedings—From big data to smart data","docAbstract":"The U.S. Geological Survey (USGS) Community for Data Integration (CDI) Workshop was held during June 3–7, 2019, at Center Green in Boulder, Colo. The theme of the workshop was “From Big Data to Smart Data” with the purpose of bringing together the community to discuss current topics, shared challenges, and steps forward to advance twenty-first century science at the USGS. The workshop agenda was driven by the needs of the CDI with topics highlighting current resources and technologies that could help attendees in their daily work. Workshop-session categories included enabling integrated science, computing in the cloud, advancing data management, releasing and preserving science outputs, and improving usability and communication. These proceedings provide documentation of the plenary talks, topical-session content and notes, posters, live demonstrations, and attendee comments from the 2019 CDI Workshop.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201132","usgsCitation":"Hsu, L., 2021, U.S. Geological Survey Community for Data Integration 2019 Workshop Proceedings—From big data to smart data: U.S. Geological Survey Open-File Report 2020–1132, 48 p., https://doi.org/10.3133/ofr20201132.","productDescription":"ix, 48 p.","onlineOnly":"Y","ipdsId":"IP-122707","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":381962,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1132/coverthb.jpg"},{"id":381963,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1132/ofr20201132.pdf","text":"Report","size":"7.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1132"}],"contact":"<p>Director, <a href=\"http://www.usgs.gov/core-science-systems/science-analytics-and-synthesis//\" data-mce-href=\"http://www.usgs.gov/core-science-systems/science-analytics-and-synthesis//\">Science Analytics and Synthesis</a><br>U.S. Geological Survey<br>P.O. Box 25046, Mail Stop 302<br>Denver, CO 80225</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Presentations</li><li>Topical Sessions</li><li>Trainings</li><li>DataBlast</li><li>Summary of Workshop Outcomes</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1. Agenda</li><li>Appendix 2. Attendees</li><li>Appendix 3. Key Take-aways</li><li>Appendix 4. Interactive Questions and Comments</li><li>Appendix 5. Community for Data Integration and Science Support Framework</li></ul>","publishedDate":"2021-01-07","noUsgsAuthors":false,"publicationDate":"2021-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Hsu, Leslie 0000-0002-5353-807X lhsu@usgs.gov","orcid":"https://orcid.org/0000-0002-5353-807X","contributorId":191745,"corporation":false,"usgs":true,"family":"Hsu","given":"Leslie","email":"lhsu@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":807675,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
]}