Ophiolitic fragments scattered over a wide area of Central Anatolia exhibit varying degrees of metamorphism, from unmetamorphosed to upper amphibolite facies, although geochemical similarities suggest they are all part of the Central Anatolian Ophiolite (CAO). Magmatic crystallization of oceanic crust in the CAO at ~ 91 Ma coincided with high-grade metamorphism of rocks that underlie the southern, highest grade part of the CAO, raising questions about the tectonic relationship of the ophiolite to underlying metasedimentary and plutonic rocks. New geochronology results show that the 40Ar/39Ar hornblende age of amphibolite-facies metagabbro in the high-grade metamorphic part of the CAO is ~ 87 Ma, similar to hornblende ages from amphibolite in the underlying Niğde metamorphic/plutonic massif. Biotite in a deformed quartzofeldspathic rock associated with high-grade meta-ophiolitic rocks yielded an 40Ar–39Ar age of ~ 78 Ma, similar to biotite ages from the Niğde Massif. Hornblende in gabbro from unmetamorphosed CAO yielded an older 40Ar/39Ar age of ~ 90 Ma, similar to the previously determined crystallization age of the ophiolite. These data indicate that the southern part of the ophiolite was incorporated into and therefore metamorphosed and deformed with the orogenic mid-crust now exposed in the Niğde metamorphic–plutonic complex, whereas the northern, unmetamorphosed part of the ophiolite was obducted onto the continent. This distinct difference in different parts of the ophiolite may indicate oblique collision or irregularities in the continental margin, resulting in part of the ophiolite being incorporated into the orogenic crust and subsequently exhumed and cooled with it, and another part being obducted.
","language":"English","publisher":"Springer","doi":"10.1007/s00531-020-01908-7","usgsCitation":"Radwany, M.S., Morgan, L.E., and Whitney, D.L., 2020, Conditions and timing of high-grade metamorphism and ductile deformation of the southern segment of the Central Anatolian Ophiolite: International Journal of Earth Sciences, v. 109, p. 2393-2406, https://doi.org/10.1007/s00531-020-01908-7.","productDescription":"14 p.","startPage":"2393","endPage":"2406","ipdsId":"IP-114200","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":436869,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P930AI0C","text":"USGS data release","linkHelpText":"Argon data for Central Anatolian Ophiolite"},{"id":376659,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Turkey","otherGeospatial":"Anatolia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 34.1455078125,\n 38.324420427006544\n ],\n [\n 38.47412109375,\n 38.324420427006544\n ],\n [\n 38.47412109375,\n 40.12009038025332\n ],\n [\n 34.1455078125,\n 40.12009038025332\n ],\n [\n 34.1455078125,\n 38.324420427006544\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"109","noUsgsAuthors":false,"publicationDate":"2020-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Radwany, Molly S.","contributorId":201104,"corporation":false,"usgs":false,"family":"Radwany","given":"Molly","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":793663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":793665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitney, Donna L.","contributorId":187715,"corporation":false,"usgs":false,"family":"Whitney","given":"Donna","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":793664,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211361,"text":"70211361 - 2020 - Characterization of the unconventional Tuscaloosa marine shale reservoir in southwestern Mississippi, USA: Insights from optical and SEM petrography","interactions":[],"lastModifiedDate":"2020-07-28T17:54:08.531487","indexId":"70211361","displayToPublicDate":"2020-07-18T12:29:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2682,"text":"Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of the unconventional Tuscaloosa marine shale reservoir in southwestern Mississippi, USA: Insights from optical and SEM petrography","docAbstract":"This study presents new optical petrography and electron microscopy data, interpreted in the context of previously published petrophysical, geochemical, and mineralogical data, to further characterize the Tuscaloosa marine shale (TMS) as an unconventional reservoir in southwestern Mississippi. The basal high resistivity zone has a higher proportion of Type II sedimentary organic matter than the overlying TMS, indicating it is more prone to oil generation. Optical petrography and electron microscopy reveal a heterogeneous clay matrix with ubiquitous pyrite grains, quartz, feldspar, glaucony, foraminifera, shell fragments, and rarer occurrences of apatite and crinoid fragments as well as liptinite, alginite, inertinite, and vitrinite. Our petrographic observations suggest that higher abundances of detrital quartz grains coupled with minimal authigenic cements result in higher porosity and permeability. However, the TMS is also more clay-rich than other unconventional shale oil and gas plays, which can impair the effectiveness of hydraulic fracture stimulation. Thin section observations reveal alternating clay and calcium carbonate laminae that are interpreted to reflect changes in sediment flux. Planktonic foraminifera indicate an overlying oxygenated water column while benthic inoceramid fragments and pervasive authigenic pyrite suggest anoxic or dysoxic bottom water conditions. Apatite fragments in thin section suggest mixing events and an influx of nutrient-rich sediments. Overall, these observations suggest that a variety of paleodepositional environments occurred in the TMS and the lithofacies diversity resulting from these small-scale depositional cycles makes it difficult to determinatively identify areas conducive to enhanced economic hydrocarbon recovery.","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2020.104580","collaboration":"None","usgsCitation":"Lohr, C., Valentine, B.J., Hackley, P.C., and Dulong, F.T., 2020, Characterization of the unconventional Tuscaloosa marine shale reservoir in southwestern Mississippi, USA: Insights from optical and SEM petrography: Marine and Petroleum Geology, v. 121, 104580, 24 p., https://doi.org/10.1016/j.marpetgeo.2020.104580.","productDescription":"104580, 24 p.","ipdsId":"IP-112257","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":455967,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marpetgeo.2020.104580","text":"Publisher Index Page"},{"id":376788,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi, Lousianna","otherGeospatial":"Southwestern Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.021484375,\n 30.012030680358613\n ],\n [\n -88.41796875,\n 30.012030680358613\n ],\n [\n -88.41796875,\n 32.02670629333614\n ],\n [\n -92.021484375,\n 32.02670629333614\n ],\n [\n -92.021484375,\n 30.012030680358613\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lohr, Celeste D. 0000-0001-6287-9047 clohr@usgs.gov","orcid":"https://orcid.org/0000-0001-6287-9047","contributorId":3866,"corporation":false,"usgs":true,"family":"Lohr","given":"Celeste D.","email":"clohr@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":794040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Valentine, Brett J. 0000-0002-8678-2431 bvalentine@usgs.gov","orcid":"https://orcid.org/0000-0002-8678-2431","contributorId":3846,"corporation":false,"usgs":true,"family":"Valentine","given":"Brett","email":"bvalentine@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":794041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":794042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dulong, Frank T. 0000-0001-7388-647X fdulong@usgs.gov","orcid":"https://orcid.org/0000-0001-7388-647X","contributorId":650,"corporation":false,"usgs":true,"family":"Dulong","given":"Frank","email":"fdulong@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":794043,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211221,"text":"70211221 - 2020 - Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine","interactions":[],"lastModifiedDate":"2020-07-20T13:33:45.743932","indexId":"70211221","displayToPublicDate":"2020-07-18T07:32:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine","docAbstract":"Accurate and timely information on croplands is important for environmental, food security, and policy studies. Spatially explicit cropland datasets are also required to derive information on crop type, crop yield, cropping intensity, as well as irrigated areas. Large area defined as continental to global cropland mapping is challenging due to differential manifestation of croplands, wide range of cultivation practices and limited reference data availability. This study presents the results of a cropland extent mapping of 64 Countriescovering large parts of Europe, Middle East, Russia and Central Asia. To cover such a vast area, roughly 160,000 Landsat scenes from 3,351 footprints between 2014 and 2016 were processed within the Google Earth Engine (GEE) cloud-platform. We used the pixel-based supervised Random Forest (RF) machine learning algorithm with a set of satellite data inputs capturing diverse spectral, temporal and topographical characteristics across twelve agroecological zones (AEZs). The reference data to train the classification model were collected from very high spatial resolution imagery (VHRI) and ancillary datasets. The result is a binary map showing cultivated/non-cultivated areas ca. 2015. The map produced an overall accuracy of 94 percent with roughly 14 percent omission and commission errors for the cropland class based on a large set of independent validation samples. The map suggests the entire study area has a total 546 million hectares (Mha) of croplands occupying 18 percent of the land area. Comparison between national cropland area estimates from United Nations Food and Agricultural Organizations (FAO) and those derived from this work also showed an R-square value of 0.95. For the entire Landsat-derived 30-m product the overall accuracy was 93.8% with cropland class providing producers accuracy of 86.5% (errors of omissions = 13.5%) and users accuracy of 85.7% (errors of commissions = 14.3%). This Landsat-derived 30-m cropland product (GFSAD30) provided 10-30% greater cropland areas compared to UN FAO in the 64 Countries. Finally, the map-to-map comparison between GFSAD30 with several other cropland products revealed that the best similarity matrix was with the 30m global land cover (GLC30) product providing an overall accuracy of 88.8 percent (Kappa 0.7) with producers cropland similarity of 89.2 percent (errors of omissions = 10.8%) and users cropland similarity of 81.8 percent (errors of commissions = 8.1%). GFSAD30 captured the missing croplands in GLC30 product around significantly irrigated agricultural areas in Germany and Belgium and rainfed agriculture in Italy. This study also established that the real strength of GFSAD30 product, compared to other products, were in: 1. Identifying precise location of croplands, and 2. Capturing fragmented croplands. The cropland extent map dataset is available through NASAs Land Processes Distributed Active Archive Center (LP DAAC) at https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001, while the training and reference data as well as visualization are available at the Global CroplandsLake Sinai Viruses (LSV) are common RNA viruses of honey bees (Apis mellifera) that frequently reach high abundance but are not linked to overt disease. LSVs are genetically heterogeneous and collectively widespread, but despite frequent detection in surveys, the ecological and geographic factors structuring their distribution in A. mellifera are not understood. Even less is known about their distribution in other species. Better understanding of LSV prevalence and ecology have been hampered by high sequence diversity within the LSV clade.
Here we report a new polymerase chain reaction (PCR) assay that is compatible with currently known lineages with minimal primer degeneracy, producing an expected 365 bp amplicon suitable for end-point PCR and metagenetic sequencing. Using the Illumina MiSeq platform, we performed pilot metagenetic assessments of three sample sets, each representing a distinct variable that might structure LSV diversity (geography, tissue, and species).
The first sample set in our pilot assessment compared cDNA pools from managed A. mellifera hives in California (n = 8) and Maryland (n = 6) that had previously been evaluated for LSV2, confirming that the primers co-amplify divergent lineages in real-world samples. The second sample set included cDNA pools derived from different tissues (thorax vs. abdomen, n = 24 paired samples), collected from managed A. mellifera hives in North Dakota. End-point detection of LSV frequently differed between the two tissue types; LSV metagenetic composition was similar in one pair of sequenced samples but divergent in a second pair. Overall, LSV1 and intermediate lineages were common in these samples whereas variants clustering with LSV2 were rare. The third sample set included cDNA from individual pollinator specimens collected from diverse landscapes in the vicinity of Lincoln, Nebraska. We detected LSV in the bee Halictus ligatus (four of 63 specimens tested, 6.3%) at a similar rate as A. mellifera (nine of 115 specimens, 7.8%), but only one H. ligatus sequencing library yielded sufficient data for compositional analysis. Sequenced samples often contained multiple divergent LSV lineages, including individual specimens. While these studies were exploratory rather than statistically powerful tests of hypotheses, they illustrate the utility of high-throughput sequencing for understanding LSV transmission within and among species.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.9424","usgsCitation":"Iwanowicz, D.D., Wu-Smart, J.Y., Olgun, T., Smart, A.H., Otto, C., Lopez, D., Evans, J.D., and Cornman, R.S., 2020, An updated genetic marker for detection of Lake Sinai Virus and metagenetic applications: PeerJ, v. 8, e9424, 18 p., https://doi.org/10.7717/peerj.9424.","productDescription":"e9424, 18 p.","ipdsId":"IP-117458","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455972,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.9424","text":"Publisher Index Page"},{"id":436871,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O9ZMA1","text":"USGS data release","linkHelpText":"Genetic detection of Lake Sinai Virus in honey bees (Apis mellifera) and other insects"},{"id":436870,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O9ZMA1","text":"USGS data release","linkHelpText":"Genetic detection of Lake Sinai Virus in honey bees (Apis mellifera) and other insects"},{"id":376462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Maryland, Nebraska","county":"San Joaquin County","city":"Beltsville, Lincoln","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.6241455078125,\n 37.22595454983972\n ],\n [\n -120.30578613281251,\n 37.22595454983972\n ],\n [\n -120.30578613281251,\n 38.10430528370985\n ],\n [\n -121.6241455078125,\n 38.10430528370985\n ],\n [\n -121.6241455078125,\n 37.22595454983972\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.3663330078125,\n 40.41767833585549\n ],\n [\n -96.26220703125,\n 40.41767833585549\n ],\n [\n -96.26220703125,\n 41.09591205639546\n ],\n [\n -97.3663330078125,\n 41.09591205639546\n ],\n [\n -97.3663330078125,\n 40.41767833585549\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.93244934082031,\n 39.00050945751261\n ],\n [\n -76.85434341430664,\n 39.00050945751261\n ],\n [\n -76.85434341430664,\n 39.05745045192533\n ],\n [\n -76.93244934082031,\n 39.05745045192533\n ],\n [\n -76.93244934082031,\n 39.00050945751261\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-07-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Iwanowicz, Deborah D. 0000-0002-9613-8594 diwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-9613-8594","contributorId":2253,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Deborah","email":"diwanowicz@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":792437,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wu-Smart, Judy Y.","contributorId":228876,"corporation":false,"usgs":false,"family":"Wu-Smart","given":"Judy","email":"","middleInitial":"Y.","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":792438,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olgun, Tugce","contributorId":228877,"corporation":false,"usgs":false,"family":"Olgun","given":"Tugce","email":"","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":792439,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smart, Autumn H. 0000-0003-0711-3035","orcid":"https://orcid.org/0000-0003-0711-3035","contributorId":228828,"corporation":false,"usgs":true,"family":"Smart","given":"Autumn","email":"","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792440,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792441,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lopez, Dawn","contributorId":228879,"corporation":false,"usgs":false,"family":"Lopez","given":"Dawn","email":"","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":792442,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Evans, Jay D.","contributorId":168966,"corporation":false,"usgs":false,"family":"Evans","given":"Jay","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":792443,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":792444,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211283,"text":"70211283 - 2020 - A comparative phylogeographic approach to facilitate recovery of an imperiled freshwater mussel (Bivalvia: Unionida: Potamilus inflatus)","interactions":[],"lastModifiedDate":"2020-07-22T15:30:11.619569","indexId":"70211283","displayToPublicDate":"2020-07-17T10:26:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1398,"text":"Diversity","active":true,"publicationSubtype":{"id":10}},"title":"A comparative phylogeographic approach to facilitate recovery of an imperiled freshwater mussel (Bivalvia: Unionida: Potamilus inflatus)","docAbstract":"North American freshwaters are among the world’s most threatened ecosystems, and freshwater mussels are among the most imperiled inhabiting these systems. A critical aspect of conservation biology is delineating patterns of genetic diversity, which can be difficult when a taxon has been extirpated from a significant portion of its historical range. In such cases, evaluating conservation and recovery options may benefit by using surrogate species as proxies when assessing overall patterns of genetic diversity. Here, we integrate the premise of surrogate species into a comparative phylogeographic framework to hypothesize genetic relationships between extant and extirpated populations of Potamilus inflatus by characterizing genetic structure in co-distributed congeners with similar life histories and dispersal capabilities. Our mitochondrial and nuclear sequence data exhibited variable patterns of genetic divergence between Potamilus spp. native to the Mobile and Pascagoula + Pearl + Pontchartrain (PPP) provinces. However, hierarchical Approximate Bayesian Computation indicated that the diversification between Mobile and PPP clades was synchronous and represents a genetic signature of a common history of vicariance. Recent fluctuations in sea-level appear to have caused Potamilus spp. in the PPP to form a single genetic cluster, providing justification for using individuals from the Amite River as a source of brood stock to re-establish extirpated populations of P. inflatus. Future studies utilizing eDNA and genome-wide molecular data are essential to better understand the distribution of P. inflatus and establish robust recovery plans. Given the imperilment status of freshwater mussels globally, our study represents a useful methodology for predicting relationships among extant and extirpated populations of imperiled species.","language":"English","publisher":"MDPI","doi":"10.3390/d12070281","usgsCitation":"Smith, C.H., and Johnson, N., 2020, A comparative phylogeographic approach to facilitate recovery of an imperiled freshwater mussel (Bivalvia: Unionida: Potamilus inflatus): Diversity, v. 12, no. 7, 281,21 p., https://doi.org/10.3390/d12070281.","productDescription":"281,21 p.","ipdsId":"IP-119251","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":455974,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/d12070281","text":"Publisher Index Page"},{"id":436872,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3CFL5","text":"USGS data release","linkHelpText":"Novel genetic resources to facilitate future molecular studies in freshwater mussels (Bivalvia: Unionidae)"},{"id":376638,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Alabama, Mississippi, Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.99951171875,\n 28.998531814051795\n ],\n [\n -85.69335937499999,\n 28.998531814051795\n ],\n [\n -85.69335937499999,\n 31.87755764334002\n ],\n [\n -91.99951171875,\n 31.87755764334002\n ],\n [\n -91.99951171875,\n 28.998531814051795\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-07-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Chase H. 0000-0002-1499-0311","orcid":"https://orcid.org/0000-0002-1499-0311","contributorId":225140,"corporation":false,"usgs":false,"family":"Smith","given":"Chase","email":"","middleInitial":"H.","affiliations":[{"id":13716,"text":"Baylor University","active":true,"usgs":false}],"preferred":false,"id":793502,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Nathan A. 0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":218986,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":793503,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70249355,"text":"70249355 - 2020 - Remotely sensed thermal decay rate: An index for vegetation monitoring","interactions":[],"lastModifiedDate":"2023-10-04T23:53:46.3028","indexId":"70249355","displayToPublicDate":"2020-07-17T10:04:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"Remotely sensed thermal decay rate: An index for vegetation monitoring","docAbstract":"Vegetation buffers local diurnal land surface temperatures, however, this effect has found limited applications for remote vegetation characterization. In this work, we parameterize diurnal temperature variations as the thermal decay rate derived by using satellite daytime and nighttime land surface temperatures and modeled using Newton’s law of cooling. The relationship between the thermal decay rate and vegetation depends on many factors including vegetation type, size, water content, location, and local conditions. The theoretical relationships are elucidated, and empirical relationships are presented. Results show that the decay rate summarizes both vegetation structure and function and exhibits a high correlation with other established vegetation-related observations. As proof of concept, we interpret 15-year spatially explicit trends in the annual thermal decay rates over Africa and discuss results. Given recent increases in availability of finer spatial resolution satellite thermal measurements, the thermal decay rate may be a useful index for monitoring vegetation.
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Routes also overlapped and suggested unavoidable over-water crossings between Mindoro and Palawan, Negros and Zamboanga del Norte, and Leyte and Surigao. Our models suggest that the optimal migratory strategy for these birds is to find the shortest route to an exit point with the greatest possible access to stopover habitats and fewest open-water crossings under wind resistance. Understanding how each of these external factors affected the geography and characteristics of the migratory routes helps us to understand the context for different migratory strategies of birds that face dangerous open-water crossings on migration.
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We combine atmospheric reanalysis output and additional observations to show how the network allows: (1) the validation of record cool season precipitable water observations over southern California; (2) the identification of phenomena that produce natural hazards and present difficulties for short‐term weather forecast models, such as extreme precipitation amounts and snow level variability; (3) the use of soil moisture data to improve hydrologic model forecast skill in northern California's Russian River basin; and (4) the combination of meteorological data with seismic observations to identify when a large avalanche occurred on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of these networks to characterize climatological context and advance understanding of processes modulating extreme weather.
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\"}}]}","volume":"7","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Hatchett, Benjamin J. 0000-0003-1066-3601","orcid":"https://orcid.org/0000-0003-1066-3601","contributorId":214405,"corporation":false,"usgs":false,"family":"Hatchett","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":39033,"text":"Division of Atmospheric Sciences, Desert Research Institute, Reno, Nevada, USA","active":true,"usgs":false}],"preferred":false,"id":794839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cao, Q. 0000-0003-3262-2149","orcid":"https://orcid.org/0000-0003-3262-2149","contributorId":236966,"corporation":false,"usgs":false,"family":"Cao","given":"Q.","email":"","affiliations":[{"id":47576,"text":"Department of Geography, University of California, Los Angeles, California, USA","active":true,"usgs":false}],"preferred":false,"id":794840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dawson, Phillip B. 0000-0003-4065-0588 dawson@usgs.gov","orcid":"https://orcid.org/0000-0003-4065-0588","contributorId":206751,"corporation":false,"usgs":true,"family":"Dawson","given":"Phillip","email":"dawson@usgs.gov","middleInitial":"B.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":794841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ellis, C. J. 0000-0002-0901-1545","orcid":"https://orcid.org/0000-0002-0901-1545","contributorId":236968,"corporation":false,"usgs":false,"family":"Ellis","given":"C.","email":"","middleInitial":"J.","affiliations":[{"id":47577,"text":"Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego: San Diego, California, US","active":true,"usgs":false}],"preferred":false,"id":794842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hecht, C. W. 0000-0002-8357-3263","orcid":"https://orcid.org/0000-0002-8357-3263","contributorId":236985,"corporation":false,"usgs":false,"family":"Hecht","given":"C.","email":"","middleInitial":"W.","affiliations":[{"id":24837,"text":"Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":794843,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kawzenuk, B. 0000-0003-1194-4296","orcid":"https://orcid.org/0000-0003-1194-4296","contributorId":236969,"corporation":false,"usgs":false,"family":"Kawzenuk","given":"B.","email":"","affiliations":[{"id":24837,"text":"Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":794844,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lancaster, J. T. 0000-0003-3662-3181","orcid":"https://orcid.org/0000-0003-3662-3181","contributorId":236970,"corporation":false,"usgs":false,"family":"Lancaster","given":"J.","email":"","middleInitial":"T.","affiliations":[{"id":47579,"text":"California Geological Survey, Sacramento, California, USA","active":true,"usgs":false}],"preferred":false,"id":794845,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Osborne, T. C. 0000-0003-3279-4688","orcid":"https://orcid.org/0000-0003-3279-4688","contributorId":236971,"corporation":false,"usgs":false,"family":"Osborne","given":"T.","email":"","middleInitial":"C.","affiliations":[{"id":47578,"text":"Center for Western Weather and Water Extremes","active":true,"usgs":false}],"preferred":false,"id":794846,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wilson, A. M. 0000-0001-7342-1955","orcid":"https://orcid.org/0000-0001-7342-1955","contributorId":236972,"corporation":false,"usgs":false,"family":"Wilson","given":"A.","email":"","middleInitial":"M.","affiliations":[{"id":47578,"text":"Center for Western Weather and Water Extremes","active":true,"usgs":false}],"preferred":false,"id":794847,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Anderson, M. L.","contributorId":236973,"corporation":false,"usgs":false,"family":"Anderson","given":"M.","email":"","middleInitial":"L.","affiliations":[{"id":47580,"text":"California Department of Water Resources, Sacramento, California, USA","active":true,"usgs":false}],"preferred":false,"id":794848,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Dettinger, M. D. 0000-0002-7509-7332","orcid":"https://orcid.org/0000-0002-7509-7332","contributorId":236974,"corporation":false,"usgs":false,"family":"Dettinger","given":"M. D.","affiliations":[{"id":24837,"text":"Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":794849,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kalansky, J. F. 0000-0003-2562-7398","orcid":"https://orcid.org/0000-0003-2562-7398","contributorId":236975,"corporation":false,"usgs":false,"family":"Kalansky","given":"J.","email":"","middleInitial":"F.","affiliations":[{"id":24837,"text":"Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":794850,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kaplan, M. L. 0000-0003-0072-8758","orcid":"https://orcid.org/0000-0003-0072-8758","contributorId":236976,"corporation":false,"usgs":false,"family":"Kaplan","given":"M.","email":"","middleInitial":"L.","affiliations":[{"id":47581,"text":"Applied Meteorology Program, Embry-Riddle Aeronautical University, Prescott, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":794851,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Lettenmaier, D. P. 0000-0002-0914-0726","orcid":"https://orcid.org/0000-0002-0914-0726","contributorId":236977,"corporation":false,"usgs":false,"family":"Lettenmaier","given":"D.","email":"","middleInitial":"P.","affiliations":[{"id":47576,"text":"Department of Geography, University of California, Los Angeles, California, USA","active":true,"usgs":false}],"preferred":false,"id":794852,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Oakley, N. S. 0000-0001-5680-9296","orcid":"https://orcid.org/0000-0001-5680-9296","contributorId":236978,"corporation":false,"usgs":false,"family":"Oakley","given":"N.","email":"","middleInitial":"S.","affiliations":[{"id":47583,"text":"Desert Research Institute and Center for Western Weather and Water Extremes","active":true,"usgs":false}],"preferred":false,"id":794853,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Ralph, R. M. 0000-0002-0870-6396","orcid":"https://orcid.org/0000-0002-0870-6396","contributorId":236979,"corporation":false,"usgs":false,"family":"Ralph","given":"R. M.","affiliations":[{"id":24837,"text":"Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":794854,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Reynolds, D. W.","contributorId":236980,"corporation":false,"usgs":false,"family":"Reynolds","given":"D.","email":"","middleInitial":"W.","affiliations":[{"id":47584,"text":"Department of Atmospheric and Oceanic Sciences, Colorado University, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":794855,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"White, A. B. 0000-0001-8587-3481","orcid":"https://orcid.org/0000-0001-8587-3481","contributorId":236981,"corporation":false,"usgs":false,"family":"White","given":"A.","email":"","middleInitial":"B.","affiliations":[{"id":47585,"text":"NOAA/Earth System Research Laboratory/Physical Sciences Division, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":794856,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Sierks, M. 0000-0003-2438-1082","orcid":"https://orcid.org/0000-0003-2438-1082","contributorId":236982,"corporation":false,"usgs":false,"family":"Sierks","given":"M.","email":"","affiliations":[{"id":47578,"text":"Center for Western Weather and Water Extremes","active":true,"usgs":false}],"preferred":false,"id":794857,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Sumargo, E. 0000-0002-3671-7498","orcid":"https://orcid.org/0000-0002-3671-7498","contributorId":236983,"corporation":false,"usgs":false,"family":"Sumargo","given":"E.","email":"","affiliations":[{"id":47578,"text":"Center for Western Weather and Water Extremes","active":true,"usgs":false}],"preferred":false,"id":794858,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70212478,"text":"70212478 - 2020 - Behavioral response to high temperatures in a desert grassland bird: Use of shrubs as thermal refugia","interactions":[],"lastModifiedDate":"2020-08-17T14:33:37.700025","indexId":"70212478","displayToPublicDate":"2020-07-17T09:28:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3746,"text":"Western North American Naturalist","onlineIssn":"1944-8341","printIssn":"1527-0904","active":true,"publicationSubtype":{"id":10}},"title":"Behavioral response to high temperatures in a desert grassland bird: Use of shrubs as thermal refugia","docAbstract":"Birds inhabiting hot, arid ecosystems contend with trade-offs between heat dissipation and water conservation. As temperatures increase, passerines engage in various behaviors to reduce exposure to heat, solar radiation and insolation, and reradiation of heat from the ground. These responses to rising temperatures may result in subordination of reproductive urgency or nutrient acquisition to the need for thermoregulation. During studies on Arizona Grasshopper Sparrow (Ammodramus savannarum ammolegus) life history and ecology, we noted that these sparrows abandoned territoriality and foraging behaviors under certain circumstances in favor of cooler microsites. In this paper we document the extreme temperatures to which these and other ground-foraging and ground-nesting birds are exposed in southwestern desert grasslands, and we present evidence that A. s. ammolegus avoids exposure to extreme air and ground temperatures by using shrubs as thermal refugia. Our observations have implications for Arizona Grasshopper Sparrows and other desert grassland passerines in the southwestern United States, where the climate is projected to become hotter and drier. We provide some of the only behavioral data, and associated temperature data, associated with the use of thermal refugia by desert grassland birds. We encourage further studies that use more robust methods to supplement our observational data.
We employ near‐field GPS data to determine the subsurface geometry of a collapsing caldera during the 2018 Kīlauea eruption. Collapse occurred in 62 discrete events, with “inflationary” deformation external to the collapse, similar to previous basaltic collapses. We take advantage of GPS data from the collapsing block and independent constraints on the magma chamber geometry from inversion of deflation prior to collapse onset. This provides an unparalleled opportunity to constrain the collapse geometry. Employing an axisymmetric finite element model, the co‐collapse displacements are best explained by piston‐like subsidence along a high angle (∼85°) normal ring fault that may steepen to vertical with depth. Reservoir magma has compressibility of 2→15 × 10−10 Pa−1, indicating bubble volume fractions from 1% to 7% (lower if fault steepens with depth). Magma pressure increases during collapses are 1 to 3 MPa, depending on compressibility. Depressurization of a triaxial point source in a homogeneous half‐space fits the data well but provides a biased representation of the source depth and process.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL088867","usgsCitation":"Segall, P., Anderson, K.R., Pulvirenti, F., Wang, T., and Johanson, I.A., 2020, Caldera collapse geometry revealed by near‐field GPS displacements at Kilauea Volcano in 2018: Geophysical Research Letters, v. 47, no. 15, e2020GL088867, 8 p., https://doi.org/10.1029/2020GL088867.","productDescription":"e2020GL088867, 8 p.","ipdsId":"IP-119131","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":378688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.3415298461914,\n 19.351315193191255\n ],\n [\n -155.19115447998047,\n 19.351315193191255\n ],\n [\n -155.19115447998047,\n 19.457528461729705\n ],\n [\n -155.3415298461914,\n 19.457528461729705\n ],\n [\n -155.3415298461914,\n 19.351315193191255\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"15","noUsgsAuthors":false,"publicationDate":"2020-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Segall, Paul","contributorId":241093,"corporation":false,"usgs":false,"family":"Segall","given":"Paul","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":799522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":799523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pulvirenti, Fabio","contributorId":241094,"corporation":false,"usgs":false,"family":"Pulvirenti","given":"Fabio","email":"","affiliations":[{"id":48203,"text":"JPL/Caltech","active":true,"usgs":false}],"preferred":false,"id":799524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Taiyi","contributorId":241095,"corporation":false,"usgs":false,"family":"Wang","given":"Taiyi","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":799525,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johanson, Ingrid A. 0000-0002-6049-2225","orcid":"https://orcid.org/0000-0002-6049-2225","contributorId":215613,"corporation":false,"usgs":true,"family":"Johanson","given":"Ingrid","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":799526,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211227,"text":"70211227 - 2020 - A 36-year record of rock avalanches in the Saint Elias Mountains of Alaska, with implications for future hazards","interactions":[],"lastModifiedDate":"2020-07-21T14:41:50.547495","indexId":"70211227","displayToPublicDate":"2020-07-16T15:45:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"A 36-year record of rock avalanches in the Saint Elias Mountains of Alaska, with implications for future hazards","docAbstract":"Glacial retreat and mountain-permafrost degradation resulting from rising global temperatures have the potential to impact the frequency and magnitude of landslides in glaciated environments. Several recent events, including the 2015 Taan Fiord rock avalanche, which triggered a tsunami with one of the highest wave runups ever recorded, have called attention to the hazards posed by landslides in regions like southern Alaska. In the Saint Elias Mountains, the presence of weak sedimentary and metamorphic rocks and active uplift resulting from the collision of the Yakutat and North American tectonic plates create landslide-prone conditions. To differentiate between the typical frequency of landsliding resulting from the geologic and tectonic setting of this region, and landslide processes that may be accelerated due to changes in climate, we used Landsat imagery to create an inventory of rock avalanches in a 3700 km2 area of the Saint Elias Mountains. During the period from 1984-2019, we identified 220 rock avalanches with a mean recurrence interval of 60 days. We compared our landslide inventory with a catalog of M ≥ 4 earthquakes to identify potential coseismic events, but only found three possible earthquake-triggered rock avalanches. We observed a distinct temporal cluster of 41 rock avalanches from 2013 through 2016 that correlated with above average air temperatures (including the three warmest years on record in Alaska, 2014-2016); this cluster was similar to a temporal cluster of recent rock avalanches in nearby Glacier Bay National Park and Preserve. The majority of rock avalanches initiated from bedrock ridges in probable permafrost zones, suggesting that ice loss due to permafrost degradation, as opposed to glacial thinning, could be a dominant factor contributing to rock-slope failures in the high elevation areas of the Saint Elias Mountains. Although earthquake-triggered landslides have episodically occurred in southern Alaska, evidence from our study suggests that area-normalized rates of non-coseismic rock avalanches were greater during the period from 1964 to 2019, and that the frequency of these events will continue to increase as the climate continues to warm. These findings highlight the need for hazard assessments in Alaska that address changes in landslide patterns related to climate change.","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2020.00293","usgsCitation":"Bessette-Kirton, E., and Coe, J.A., 2020, A 36-year record of rock avalanches in the Saint Elias Mountains of Alaska, with implications for future hazards: Frontiers in Earth Science, v. 8, 293, 24 p., https://doi.org/10.3389/feart.2020.00293.","productDescription":"293, 24 p.","ipdsId":"IP-119681","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":455984,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00293","text":"Publisher Index Page"},{"id":376528,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Saint Elias Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -146.085205078125,\n 59.712097173322924\n ],\n [\n -139.822998046875,\n 59.712097173322924\n ],\n [\n -139.822998046875,\n 63.342272727869\n ],\n [\n -146.085205078125,\n 63.342272727869\n ],\n [\n -146.085205078125,\n 59.712097173322924\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Bessette-Kirton, Erin K. 0000-0002-2797-0694","orcid":"https://orcid.org/0000-0002-2797-0694","contributorId":225097,"corporation":false,"usgs":false,"family":"Bessette-Kirton","given":"Erin K.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":793277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":793278,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70207151,"text":"sir20195139 - 2020 - Hydrogeologic and geochemical characterization of groundwater resources in Pine and Wah Wah Valleys, Iron, Beaver, and Millard Counties, Utah","interactions":[],"lastModifiedDate":"2020-07-20T12:40:49.172552","indexId":"sir20195139","displayToPublicDate":"2020-07-16T13:28:40","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5139","displayTitle":"Hydrogeologic and Geochemical Characterization of Groundwater Resources in Pine and Wah Wah Valleys, Iron, Beaver, and Millard Counties, Utah","title":"Hydrogeologic and geochemical characterization of groundwater resources in Pine and Wah Wah Valleys, Iron, Beaver, and Millard Counties, Utah","docAbstract":"Pine and Wah Wah Valleys are neighboring structural basins that encompass about 1,330 square miles in Beaver, Iron, and Millard Counties in Utah, approximately 50 miles northwest of Cedar City, Utah, and 50 miles southeast of Baker, Nevada. Perennial streamflow is limited and only exists in higher-altitude reaches of small mountain streams in both basins. Groundwater is in unconsolidated basin-fill aquifers and bedrock mountain aquifers. Groundwater in Pine and Wah Wah Valleys is being targeted for large-scale groundwater extraction and export to provide municipal supply to the growing population in Iron County, Utah. Concern about declining groundwater levels and spring flows from proposed groundwater withdrawals has increased interest in an improved understanding of the groundwater system. Previous studies have indicated that an average of 28,000 acre-feet per year of recharge occurs mostly as infiltration of precipitation in high-altitude regions in the two basins. Groundwater discharge in the mountain hydrologic systems was estimated to average 8,500 acre-feet per year and is assumed to be consumed before subsequently recharging the valley basin-fill aquifers. Subsurface groundwater outflow moves from basin-fill aquifers in Pine and Wah Wah Valleys northward to adjacent regional basins and was estimated to average 19,500 acre-feet per year.
An updated water-level map for the basin-fill aquifers in Pine and Wah Wah Valleys indicates that groundwater moves northward along the lengths of both valleys toward adjacent basins. Measured depths to water range from about 210 to 750 feet below land surface in Wah Wah Valley, and from about 300 to 620 feet below land surface in Pine Valley. Long-term water levels at seven wells completed in the basin-fill aquifers of Pine and Wah Wah Valleys with records spanning more than 40 years are generally stable with observed fluctuations of less than 5 feet. Observed discharge from two springs monitored between 2013 and 2016 also is generally stable.
Groundwater leaving Pine and Wah Wah Valleys through the subsurface moves northward, converges with regional groundwater flow, and discharges by evapotranspiration at regional groundwater discharge areas, likely Tule Valley, Utah. In this study, basin-scale groundwater discharge was estimated by (1) mapping the groundwater discharge areas in each valley; (2) evaluating the 2005–11 summer multispectral satellite images against the Basin and Range carbonate-rock aquifer system study evapotranspiration measurements to select scenes broadly representative of average conditions in the study area and partitioning the groundwater discharge areas into evapotranspiration units using the selected satellite images and field reconnaissance; and (3) scaling evapotranspiration to the evapotranspiration units using evapotranspiration-rate estimates from several studies in the Great Basin. The resulting updated estimates of average annual groundwater evapotranspiration in the Tule Valley and Sevier Lake groundwater discharge areas were 35,000 and 10,500 acre-feet per year, respectively, with a likely uncertainty of plus or minus 35 percent.
Groundwater samples from 13 sites in Pine Valley and 11 sites in Wah Wah Valley were analyzed for major ions and nutrients, to characterize geochemistry and water quality. Groundwater samples also were analyzed for the stable isotopes of oxygen, hydrogen, and carbon, the radioactive isotopes of carbon and hydrogen, and dissolved noble gases including helium-3, helium-4, neon, argon, krypton and xenon. Groundwater sampling sites included 12 wells and 12 springs. Carbon-14 and tritium/helium groundwater age dating indicate that groundwater in the basin-fill aquifers is typically thousands to tens of thousands of years older than groundwater in the shallow mountain aquifers. Dissolved-solids concentrations are lower and noble-gas temperatures are warmer in the valley wells compared to almost all groundwater sampled from wells and springs in the surrounding mountains. These results indicate a hydraulic discontinuity between the mountain and valley aquifers throughout much of the study area, and that much of the valley recharge is not derived from direct infiltration of precipitation in the mountains.
Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
801-908-5000
The Allegheny woodrat (Neotoma magister) is a species of high conservation concern and relatively well-studied with respect to habitat use/associations, food habits, conservation genetics, and population trends. However, with the exception of raccoon roundworm (Baylisascaris procyonis) occurrence and etiology in woodrats, most disease and parasite ecology aspects for the woodrat are unknown. Herein, we examined the prevalence of bot flies (Cuterebra) over nearly three decades of woodrat surveys (1990–2018) in the central Appalachian Mountains of western Virginia. We use genetic analyses to identify recent bot fly specimen collections from a woodrat captured in 2017. Though highly variable from year to year, the overall prevalence of parasitism was low (typically < 4% of captures). As such, bot flies do not appear to be a widespread parasitic burden to Allegheny woodrats in Virginia. Genetic analysis of four collected bot fly larvae was inconclusive, as the genetic signature of these woodrat bots did not match any of the six bot species known to parasitize rodents and lagomorphs in the eastern United States. Further collections and genetic analyses will be needed to determine if the genetic database is incomplete or incorrect, or if our find is a new species of bot fly not yet taxonomically recognized.
The U.S. Geological Survey (USGS) is revising the Chesapeake Bay-based science plan to align it with recent U.S. Department of Interior and USGS science priorities that include, as stated in the plan, providing “an integrated understanding of the factors affecting fish habitat, fish health, and landscape conditions” in Chesapeake Bay and its watershed. A report of partner agencies’ needs and priorities related to aquatic invasive species (AIS) science was identified as an informational gap; a report would help to further development of the science program related to aquatic animal health and habitat. This objective was addressed through review of pertinent documentation and conversations with representatives of State, Federal, and regional agencies with vested interests in AIS management in Chesapeake Bay and the Chesapeake Bay drainage area, and this document was produced to summarize the related findings.
All agencies and organizations (13) reported that AIS are of general concern, with most stakeholder groups reporting AIS-related issues to be of high priority, including invasive fishes and invertebrates, invasive plants, and microbes including aquatic animal pathogens.
Science needs that were recurrently indicated by stakeholders to support management of invasive species include
Potential next steps to address the science needs include
Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Relatively few North American anurans overwinter in water and information is sparse on their movement from overwintering habitat to breeding sites. Oregon spotted frogs (Rana pretiosa) breed explosively in early spring and often overwinter submerged at sites that are distanced from breeding habitats. In montane parts of their range, wintering and breeding habitats can remain frozen for months. We investigated timing, duration, and potential cues for R. pretiosa migrations from a wintering lake near the Cascade Mountains in central Oregon, U.S.A. First and median migrant males moved slightly earlier than females. Onset of migration was as early as February 12 (males) and as late as April 4 (females) in years of mild and extended winters, respectively. Frogs were active at water temperatures below those associated with early breeding activities in one lowland R. pretiosa population. Higher proportions of frogs migrated before ice-out in years of prolonged winter conditions. Migrations were temporally compressed in years of later movement. This migration ‘rush’, along with the ability to move at cold temperatures and to vary timing of migrations likely helps montane R. pretiosa deal with colder and more variable spring conditions than lowland populations.
","language":"English","publisher":"University of Notre Dame","doi":"10.1637/0003-0031-184.1.87","usgsCitation":"Bowerman, J., and Pearl, C., 2020, Oregon spotted frog (Rana pretiosa) migration from an aquatic overwintering site: Timing, duration, and potential environmental cues: The American Midland Naturalist, v. 184, no. 1, p. 87-97, https://doi.org/10.1637/0003-0031-184.1.87.","productDescription":"11 p.","startPage":"87","endPage":"97","ipdsId":"IP-114580","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":387810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":387809,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://bioone.org/journals/the-american-midland-naturalist/volume-184/issue-1/0003-0031-184.1.87/Oregon-Spotted-Frog-Rana-pretiosa-Migration-from-an-Aquatic-Overwintering/10.1637/0003-0031-184.1.87.full"}],"country":"United States","state":"Oregon","county":"Deschutes County","otherGeospatial":"Lake Aspen","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.44827842712402,\n 43.882892462492705\n ],\n [\n -121.44312858581543,\n 43.882892462492705\n ],\n [\n -121.44312858581543,\n 43.886758784865066\n ],\n [\n -121.44827842712402,\n 43.886758784865066\n ],\n [\n -121.44827842712402,\n 43.882892462492705\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"184","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bowerman, Jay","contributorId":57024,"corporation":false,"usgs":false,"family":"Bowerman","given":"Jay","email":"","affiliations":[],"preferred":false,"id":820874,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearl, Christopher 0000-0003-2943-7321 christopher_pearl@usgs.gov","orcid":"https://orcid.org/0000-0003-2943-7321","contributorId":172669,"corporation":false,"usgs":true,"family":"Pearl","given":"Christopher","email":"christopher_pearl@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":820875,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218791,"text":"70218791 - 2020 - Evaluating the utility of principal component analysis on EDS x-ray maps to determine bulk mineralogy","interactions":[],"lastModifiedDate":"2021-03-15T12:12:45.435328","indexId":"70218791","displayToPublicDate":"2020-07-16T07:26:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1822,"text":"Geostandards and Geoanalytical Research","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the utility of principal component analysis on EDS x-ray maps to determine bulk mineralogy","docAbstract":"Due to advances in EDS technology, electron microscopy techniques have become an important tool to determine the relative abundance of mineral phases. However, few studies have directly compared EDS X‐ray mineralogy with traditional techniques for assessing bulk mineralogy and elemental composition. We show that analysing a limited area (~ 0.5–3.2 mm2) of fine‐grained metal extraction samples using EDS X‐ray principal component analysis phase mapping yields results that agree within 10% with more traditional techniques for mineral phases present at greater than 5% m/m. Electron beam sensitive minerals, such as the carbonates, have poor correlations between EDS and X‐ray Diffraction (XRD) and/or WD‐XRF. Likewise, poor correlations between methods can be expected for particles that are smaller than the interaction volume of the electron beam (~ 1.5 µm); this strongly affected the phyllosilicates. One strength of EDS phase mapping is that it can identify phases present below the detection limit of powder XRD (< 1%). Our results demonstrate that EDS phase mapping is sufficient to estimate bulk sample mineralogy. If polished thin sections have been prepared, this approach may save time and/or money relative to the more traditional approaches of preparing separate subsamples for XRD and/or WD‐XRF.
Variations in atmospheric pressure have long been known to introduce noise in long-period (>10 s) seismic records. This noise can overwhelm signals of interest such as normal modes and surface waves. Generally, this noise is most pronounced on the horizontal components where it arises due to tilting of the seismometer in response to changes in atmospheric pressure. Several studies have suggested methodologies for correcting unwanted pressure-induced noise using collocated microbarograph records. However, how applicable these corrections are to varying geologic settings and installation types (e.g. vault versus post-hole) is unclear. Using coefficients obtained by solving for the residuals of these corrections, we can empirically determine the sensitivity of instruments in a specific location to the influences of pressure. To better understand how long-period, pressure-induced noise changes with time and emplacement, we examine horizontal seismic records along with barometric pressure at five different Global Seismographic Network stations, all with multiple broadband seismometers. We also analyse three Streckeisen STS-2 broadband seismometers, which are collocated with a microbarograph, at the Albuquerque Seismological Laboratory. We observe periods of high magnitude-squared-coherence (γ2-coherence; γ2 > 0.8) between the seismic and pressure signals which fluctuate through time, frequency, and even between seismic instruments in the same vault. These observations suggest that these tilt-generated signals are highly sensitive to very local (<10 m) site effects. However, we find that in cases where instruments are not located at a large depth (<100 m), the pressure-induced noise is polarized in a nearly constant direction that is consistent with local topographic features or the geometry of the vault. We also find that borehole instruments at a large depth (>100 m) appear to be unaffected by pressure-loading mechanisms outlined by Sorrells (1971) but possibly by Newtonian attraction. Correlating the induced-noise polarization direction with times of high coherence, we work to identify sensors that are ultimately limited by pressure-induced horizontal noise as well as period bands that can benefit from pressure corrections. We find that while the situation is complex, each sensor appears to have its own unique response to pressure. Our findings suggest that we can determine empirical relationships between pressure and induced tilt on a case by case basis.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggaa340","usgsCitation":"Alejandro, A.C., Ringler, A.T., Wilson, D.C., Anthony, R.E., and Moore, S., 2020, Towards understanding relationships between atmospheric pressure variations and long-period horizontal seismic data: A case study: Geophysical Journal International, v. 223, no. 1, p. 676-691, https://doi.org/10.1093/gji/ggaa340.","productDescription":"16 p.","startPage":"676","endPage":"691","ipdsId":"IP-117969","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":455992,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggaa340","text":"Publisher Index Page"},{"id":382087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"223","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Alejandro, Alexis Casondra Bianca 0000-0002-3401-9303","orcid":"https://orcid.org/0000-0002-3401-9303","contributorId":246023,"corporation":false,"usgs":true,"family":"Alejandro","given":"Alexis","email":"","middleInitial":"Casondra Bianca","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807918,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807919,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807920,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anthony, Robert 0000-0001-7089-8846 reanthony@usgs.gov","orcid":"https://orcid.org/0000-0001-7089-8846","contributorId":202829,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert","email":"reanthony@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807921,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moore, S.V. 0000-0003-3059-8261","orcid":"https://orcid.org/0000-0003-3059-8261","contributorId":247564,"corporation":false,"usgs":false,"family":"Moore","given":"S.V.","affiliations":[{"id":49580,"text":"UNLV, Las Vegas, NV","active":true,"usgs":false}],"preferred":false,"id":807922,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247977,"text":"70247977 - 2020 - Kinematics of fault slip associated with the July 4-6 2019 Ridgecrest, Californai earthquakes sequence","interactions":[],"lastModifiedDate":"2023-08-30T11:54:27.06699","indexId":"70247977","displayToPublicDate":"2020-07-16T06:50:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Kinematics of fault slip associated with the July 4-6 2019 Ridgecrest, Californai earthquakes sequence","docAbstract":"The 2019 Ridgecrest, California, earthquake sequence produced observable crustal deformation over much of central and southern California, as well as surface rupture over several tens of kilometers. To obtain a detailed picture of the fault slip involved in the 4 July M 6.4 foreshock and 6 July M 7.1 mainshock, we combine strong‐motion seismic waveforms with crustal deformation observations to obtain kinematic and static slip models of both events. We sample the regional seismic wavefield for both the foreshock and mainshock with three‐component records from 31 stations of the California Integrated Seismic Network. The deformation observations include Global Positioning System (GPS), Interferometric Synthetic Aperture Radar (InSAR), and borehole strainmeter recordings of the dynamic strain field. These data collectively constrain the kinematic coseismic slip distributions of the events, with measurements variously observing coseismic slip from one event (e.g., seismic waveforms, kinematic solutions from continuous GPS, and strainmeter time series) or coseismic slip from both events combined (InSAR). We find that the foreshock ruptured two separate faults, one with left‐lateral strike slip on a northeast–southwest‐trending fault and the other with right‐lateral strike slip on an orthogonal fault, with unilateral rupture propagation along both. The mainshock ruptured a series of northwest–southeast‐trending faults with right‐lateral strike slip concentrated in the uppermost 6 km with exceptionally low‐rupture velocity averaging
Geophysical models characterize the exposed and interpreted buried extent of the Stillwater Complex, critical for understanding the origin of the layered mafic intrusion and its associated high-grade platinum group element resources. The 3D models, constrained by gravity, magnetic, xenolith, seismic, borehole, and rock property data indicate that the likely maximum extent of the Stillwater Complex beneath Phanerozoic cover is ~10 times greater than its outcrop, ~2240 km2. The thickness values are poorly constrained but vary from ~7000 to 12,000 m, depending on crustal and mantle density variations and depths to the top of the lower crust and mantle. This thickness may include dense metasedimentary units of the basin into which the Stillwater Complex intruded. Using the modeled thickness results in a volume estimate of ~24,700 km3, albeit poorly constrained. New analyses of xenoliths from the Cretaceous Sliderock and Suzie Peak intrusions produce ages of 2706–2716 Ma, corresponding to the age of the Stillwater Complex, and 2813 Ma, corresponding to the age of Archean gneissic basement. Seismic reflectors in inferred Archean crystalline basement, possibly including the Stillwater Complex, dip ~25–30° north, with segments dipping as much as 70° north. Layered reflectors beneath the Phanerozoic sedimentary section and above the inferred Archean crystalline basement may represent metasedimentary units, perhaps a southern extension of the Mesoproterozoic Belt Basin. The potential field models and the seismic reflection data suggest that the Stillwater Complex was dipping northward prior to deposition of Cambrian strata, perhaps uplifted in the late Archean or Proterozoic as previously proposed, and that during Laramide times, faulting and intrusions highly disrupted the complex. Temperature measurements from boreholes help constrain the depths of feasible mining of the complex.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.precamres.2020.105860","usgsCitation":"Finn, C., Zientek, M., Parks, H.L., and Peterson, D.E., 2020, Mapping the 3-D extent of the Stillwater Complex, Montana—Implications for potential platinum group element exploration and development: Precambrian Research, v. 348, 105860, 13 p., https://doi.org/10.1016/j.precamres.2020.105860.","productDescription":"105860, 13 p.","ipdsId":"IP-114827","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":455995,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.precamres.2020.105860","text":"Publisher Index Page"},{"id":377138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Stillwater Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.70898437499999,\n 45.0502402697946\n ],\n [\n -108.7646484375,\n 45.0502402697946\n ],\n [\n -108.7646484375,\n 45.98169518512228\n ],\n [\n -111.70898437499999,\n 45.98169518512228\n ],\n [\n -111.70898437499999,\n 45.0502402697946\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"348","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Finn, Carol A. 0000-0002-6178-0405","orcid":"https://orcid.org/0000-0002-6178-0405","contributorId":205010,"corporation":false,"usgs":true,"family":"Finn","given":"Carol A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":795058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zientek, Michael L. 0000-0002-8522-9626","orcid":"https://orcid.org/0000-0002-8522-9626","contributorId":210763,"corporation":false,"usgs":true,"family":"Zientek","given":"Michael L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Parks, Heather L. 0000-0002-5917-6866 hparks@usgs.gov","orcid":"https://orcid.org/0000-0002-5917-6866","contributorId":4989,"corporation":false,"usgs":true,"family":"Parks","given":"Heather","email":"hparks@usgs.gov","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795060,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peterson, Dana E. 0000-0002-1941-265X","orcid":"https://orcid.org/0000-0002-1941-265X","contributorId":225536,"corporation":false,"usgs":true,"family":"Peterson","given":"Dana","email":"","middleInitial":"E.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":795061,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70262551,"text":"70262551 - 2020 - A GT-seq panel for walleye (Sander vitreus) provides important insights for efficient development and implementation of amplicon panels in non-model organisms","interactions":[],"lastModifiedDate":"2025-01-23T17:56:36.098172","indexId":"70262551","displayToPublicDate":"2020-07-15T11:52:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"A GT-seq panel for walleye (Sander vitreus) provides important insights for efficient development and implementation of amplicon panels in non-model organisms","docAbstract":"Targeted amplicon sequencing methods, such as genotyping-in-thousands by sequencing (GT-seq), facilitate rapid, accurate, and cost-effective analysis of hundreds of genetic loci in thousands of individuals. Development of GT-seq panels is nontrivial, but studies describing trade-offs associated with different steps of GT-seq panel development are rare. Here, we construct a dual-purpose GT-seq panel for walleye (Sander vitreus), discuss trade-offs associated with different development and genotyping approaches, and provide suggestions for researchers constructing their own GT-seq panels. Our GT-seq panel was developed using an ascertainment set consisting of restriction site-associated DNA data from 954 individuals sampled from 23 populations in Minnesota and Wisconsin, USA. We conducted simulations to test the utility of all loci for parentage analysis and genetic stock identification and designed 600 primer pairs to maximize joint accuracy for these analyses. We then performed three rounds of primer optimization to remove loci that overamplified and our final panel consisted of 436 loci. We also explored different approaches for DNA extraction, multiplexed polymerase chain reaction (PCR) amplification, and cleanup steps during the GT-seq process and discovered the following: (i) inexpensive Chelex extractions performed well for genotyping; (ii) the exonuclease I and shrimp alkaline phosphatase (ExoSAP) procedure included in some current protocols did not improve results substantially and was probably unnecessary; and (iii) it was possible to PCR amplify panels separately and combine them prior to adapter ligation. Well-optimized GT-seq panels are valuable resources for conservation genetics and our findings and suggestions should aid in their construction in myriad taxa.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.13226","usgsCitation":"Bootsma, M., Gruenthal, K., McKinney, G., Simmons, L., Miller, L., Sass, G., and Larson, W., 2020, A GT-seq panel for walleye (Sander vitreus) provides important insights for efficient development and implementation of amplicon panels in non-model organisms: Molecular Ecology Resources, v. 20, no. 6, p. 1706-1722, https://doi.org/10.1111/1755-0998.13226.","productDescription":"17 p.","startPage":"1706","endPage":"1722","ipdsId":"IP-115320","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481106,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/2020.02.13.948331","text":"External Repository"},{"id":481021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, 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\"}}]}","volume":"20","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Bootsma, Matthew L.","contributorId":349232,"corporation":false,"usgs":false,"family":"Bootsma","given":"Matthew L.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":924528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gruenthal, Kristen","contributorId":349610,"corporation":false,"usgs":false,"family":"Gruenthal","given":"Kristen","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":924529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKinney, Garrett","contributorId":270641,"corporation":false,"usgs":false,"family":"McKinney","given":"Garrett","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":924530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simmons, Levi","contributorId":349636,"corporation":false,"usgs":false,"family":"Simmons","given":"Levi","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":924531,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Loren","contributorId":349233,"corporation":false,"usgs":false,"family":"Miller","given":"Loren","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":924532,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sass, Greg G.","contributorId":279948,"corporation":false,"usgs":false,"family":"Sass","given":"Greg G.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":924533,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Larson, Wesley 0000-0003-4473-3401 wlarson@usgs.gov","orcid":"https://orcid.org/0000-0003-4473-3401","contributorId":199509,"corporation":false,"usgs":true,"family":"Larson","given":"Wesley","email":"wlarson@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924527,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211326,"text":"70211326 - 2020 - Distribution of earthquakes on a branching fault system using integer programming and greedy sequential methods","interactions":[],"lastModifiedDate":"2020-09-10T20:13:38.032211","indexId":"70211326","displayToPublicDate":"2020-07-15T10:40:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Distribution of earthquakes on a branching fault system using integer programming and greedy sequential methods","docAbstract":"A new global optimization method is used to determine the distribution of earthquakes on a complex, connected fault system. The method, integer programming, has been advanced in the field of operations research, but has not been widely applied to geophysical problems until recently. In this application, we determine the optimal distribution of earthquakes on mapped faults to minimize the global misfit in slip rates for multi-fault ruptures. Integer programming solves for a decision vector composed of every possible location that a sample of earthquakes can occur on every fault, subject to slip-rate uncertainty constraints. Step over connections are straightforward to include, whereas branching fault connections are not. To include branching ruptures, we distinguish between individual multi-fault rupture paths, as opposed to formulating the integer-programming problem based on individual faults as in previous studies. The new method is applied to the complex fault system in the San Francisco Bay Area as a case study. Results from the integer-programming method are compared to those from a local optimization method, termed the greedy-sequential method. Several experiments using these two methods indicate that shape of the on-fault magnitude distributions and which branching faults are involved in multi-fault ruptures depend on how much emphasis is placed on fitting the target slip rate. In cases where the underlying data are not strong enough to warrant chasing the target slip rate, it is better to focus on the distribution of feasible results that better represents the uncertainty in the solutions imposed by the data.","language":"English","publisher":"AGU","doi":"10.1029/2020GC008964","usgsCitation":"Geist, E.L., and Parsons, T.E., 2020, Distribution of earthquakes on a branching fault system using integer programming and greedy sequential methods: Geochemistry, Geophysics, Geosystems, v. 21, no. 9, e2020GC008964, 22 p., https://doi.org/10.1029/2020GC008964.","productDescription":"e2020GC008964, 22 p.","ipdsId":"IP-115931","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":499871,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/b65c6f112d5f46ac9575e46a20357731","text":"External Repository"},{"id":376688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Geist, Eric L. 0000-0003-0611-1150 egeist@usgs.gov","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":1956,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"egeist@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":793799,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":793800,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211196,"text":"70211196 - 2020 - Legacy and current-use toxic contaminants in Pacific sand lance (Ammodytes personatus) from Puget Sound, Washington","interactions":[],"lastModifiedDate":"2020-07-17T15:35:00.620543","indexId":"70211196","displayToPublicDate":"2020-07-15T10:27:32","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Legacy and current-use toxic contaminants in Pacific sand lance (Ammodytes personatus) from Puget Sound, Washington","title":"Legacy and current-use toxic contaminants in Pacific sand lance (Ammodytes personatus) from Puget Sound, Washington","docAbstract":"Forage fish are primary prey for seabirds, fish and marine mammals. Elevated levels of pollutants in Puget Sound, Washington salmon and killer whale tissues potentially could be sufficiently high to elicit adverse effects and hamper population recovery efforts. Contaminant transfer and biomagnification of the toxic compounds measured in this study likely contribute to those elevated concentrations. Pacific sand lance tissues from nine locations were analyzed for a suite of legacy and emerging contaminants including polychlorinated biphenyls, polybrominated diphenyl ethers, chlorinated pesticides, polycyclic aromatic hydrocarbons, alkylphenols, and chlorinated paraffins. Chemicals were detected at all sites generally below available health effect levels for the host. However, sub-lethal effects are known to occur and additive effects from exposure to multiple compounds, like this study’s mixture, are not well understood. Biomagnification calculations suggest that, in some locations, concentrations of polychlorinated biphenyls in forage fish could result in predator tissue concentrations that exceed effect levels.","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpolbul.2020.111287","usgsCitation":"Conn, K., Liedtke, T.L., Takesue, R.K., and Dinicola, R., 2020, Legacy and current-use toxic contaminants in Pacific sand lance (Ammodytes personatus) from Puget Sound, Washington: Marine Pollution Bulletin, v. 158, 111287, 14 p., https://doi.org/10.1016/j.marpolbul.2020.111287.","productDescription":"111287, 14 p.","ipdsId":"IP-113548","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":455998,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marpolbul.2020.111287","text":"Publisher Index Page"},{"id":376461,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.53027343749999,\n 47.03082254778662\n ],\n [\n -122.11578369140626,\n 47.03082254778662\n ],\n [\n -122.11578369140626,\n 48.33616902211533\n ],\n [\n -123.53027343749999,\n 48.33616902211533\n ],\n [\n -123.53027343749999,\n 47.03082254778662\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"158","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Conn, Kathleen E. 0000-0002-2334-6536 kconn@usgs.gov","orcid":"https://orcid.org/0000-0002-2334-6536","contributorId":3923,"corporation":false,"usgs":true,"family":"Conn","given":"Kathleen E.","email":"kconn@usgs.gov","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liedtke, Theresa L. 0000-0001-6063-9867 tliedtke@usgs.gov","orcid":"https://orcid.org/0000-0001-6063-9867","contributorId":2999,"corporation":false,"usgs":true,"family":"Liedtke","given":"Theresa","email":"tliedtke@usgs.gov","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":793092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Takesue, Renee K. 0000-0003-1205-0825 rtakesue@usgs.gov","orcid":"https://orcid.org/0000-0003-1205-0825","contributorId":2159,"corporation":false,"usgs":true,"family":"Takesue","given":"Renee","email":"rtakesue@usgs.gov","middleInitial":"K.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":793093,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dinicola, Richard S. 0000-0003-4222-294X dinicola@usgs.gov","orcid":"https://orcid.org/0000-0003-4222-294X","contributorId":352,"corporation":false,"usgs":true,"family":"Dinicola","given":"Richard S.","email":"dinicola@usgs.gov","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":793094,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212715,"text":"70212715 - 2020 - Does fecundity of cisco vary in the Upper Great Lakes?","interactions":[],"lastModifiedDate":"2025-02-07T15:17:31.094305","indexId":"70212715","displayToPublicDate":"2020-07-15T09:55:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Does fecundity of cisco vary in the Upper Great Lakes?","docAbstract":"Fecundity of fish is influenced by several factors, including body length, condition, population density, and environmental conditions. It follows that fecundity of fish populations can exhibit spatiotemporal variability; thus, periodic quantification of length–fecundity relationships is important for management. We hypothesized that average fecundity of Cisco Coregonus artedi in the upper Laurentian Great Lakes would be lower in Lake Superior than in Lakes Huron and Michigan. The trophic status of these lakes recently converged, but Lakes Huron and Michigan currently support lower Cisco densities; thus, we expected that they would reach larger sizes and have greater fecundity owing to lower intraspecific competition. Ovaries were collected from prespawn Cisco during 2008–2010 to test this hypothesis. We also compared length–fecundity relationships for 2008–2010 to those of precollapse (1930s–1950s) populations to explore how relationships have changed. Average fecundity of Cisco during 2008–2010 was lower in Lake Superior compared to Lakes Huron and Michigan; length–fecundity relationships in the latter two lakes did not vary significantly, so they were combined. Body condition was highest in Lakes Huron and Michigan. We used otoliths to determine age and found that body condition was domed shaped with respect to age in Lakes Huron and Superior. There were no females older than age 5 in our samples from Lake Michigan because that population was just beginning to recover from very low levels. Females of intermediate age had the highest fecundities in both Lake Huron (ages 7–13) and Lake Superior (ages 8–18). We hypothesize that differences in body morphometry may also influence fecundity, with deeper‐bodied C. artedi albus, the predominant form in Lakes Michigan and Huron, having greater fecundity than shallower‐bodied C. artedi artedi in Lake Superior. Moreover, varying Cisco diets and seasonal movement patterns across lakes may have also contributed to differences. Females in Lakes Superior and Michigan are currently more fecund than their precollapse counterparts.
Hypoxic conditions in both freshwater and marine habitats have a significant effect on the distribution of fish in the water column, resulting in some fishes aggregating near the edges of the hypoxic zone. These aggregations may increase fish susceptibility to fishing gears, with attendant effects on stock assessment inferences. We investigated how hypoxic conditions influenced catch rates of yellow perch (Perca flavescens) in both fishery independent bottom trawls and stationary commercial trap nets. Specifically, we examined how the presence of hypoxia affected trap net catch rates and how hypoxia interacted with hypolimnion thickness to modify trawl catch rates. Bottom trawl catch rates were significantly higher in hypoxic conditions than in normoxic conditions, and in each of these scenarios catch rates declined as hypolimnion thickness increased. By comparison, trap net catch rates had a dome-shaped response to the duration of hypoxia with the highest catch rates occurring at intermediate levels. Increased catch rates in hypoxic conditions potentially causes yellow perch population models, which rely on both trap net and trawl indices, to overestimate abundance and could result in overfishing.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2020.105684","usgsCitation":"Chamberlin, D.W., Knight, C., Kraus, R., Gorman, A.M., Xu, W., and Collingsworth, P.D., 2020, Hypoxia augments edge effects of water column stratification on fish distribution: Fisheries Research, v. 231, 105684, 8 p., https://doi.org/10.1016/j.fishres.2020.105684.","productDescription":"105684, 8 p.","ipdsId":"IP-106559","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":376425,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.49609375,\n 41.31082388091818\n ],\n [\n -79.29931640625,\n 41.902277040963696\n ],\n [\n -78.59619140625,\n 42.52069952914966\n ],\n [\n -78.79394531249999,\n 43.11702412135048\n ],\n [\n -83.49609375,\n 42.65012181368022\n ],\n [\n -83.49609375,\n 41.31082388091818\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"231","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chamberlin, Derek W.","contributorId":229361,"corporation":false,"usgs":false,"family":"Chamberlin","given":"Derek","email":"","middleInitial":"W.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":792981,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knight, Carey","contributorId":216161,"corporation":false,"usgs":false,"family":"Knight","given":"Carey","affiliations":[{"id":16232,"text":"Ohio Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":792982,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":792983,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gorman, Ann Marie","contributorId":145525,"corporation":false,"usgs":false,"family":"Gorman","given":"Ann","email":"","middleInitial":"Marie","affiliations":[],"preferred":false,"id":792984,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xu, Wenzhao","contributorId":200526,"corporation":false,"usgs":false,"family":"Xu","given":"Wenzhao","email":"","affiliations":[],"preferred":false,"id":792985,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collingsworth, Paris D.","contributorId":145526,"corporation":false,"usgs":false,"family":"Collingsworth","given":"Paris","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":792986,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}