The Near and InterMediate Range Order Diffractometer (NIMROD) was used to examine the potential impact of shale mineralogy on CO2 behavior within micropores. Two samples with varying mineral compositions were obtained from producing intervals in the dry gas window in the Middle Devonian Marcellus Shale. One of the samples contained relatively high amounts of quartz and clay and low carbonate, the other contained relatively equal amounts of quartz, carbonate, and clay. The samples were probed with CO2 at subcritical pressures (20–50 bar) and temperature (22 °C) and characterized over a neutron scattering vector (Q) range of 0.02 < Q < 50 Å–1. This Q range provides information from the atomistic length-scale up to pore radii of 10 nm. Mineralogy variations between the samples did not affect scattering ratios over the entire Q range accessible with the NIMROD. Q values for the minimum scattering ratios of both samples at similar pressures are remarkably similar, particularly for Q < ∼0.09 Å–1, and maximum scattering ratios are similar in both samples suggesting that mineral pores are so uncommon in the pore sizes examined that they cannot be resolved due to the overwhelming amounts of organic pores in these samples. Overall, these findings suggest that mineralogical variations have little effect on CO2 behavior within organic matter-hosted shale micropores at high thermal maturities and they lend support to the assertion that CO2 cannot be stored in the vast surface areas of micropores in organic material in shale formations. In addition, CO2 enhanced oil recovery (EOR) is unlikely to displace petroleum from some of the smaller mesopores (2.5 to ∼3.5 nm) and all of the micropores because they are effectively closed to CO2.
amples were probed with CO2 at subcritical pressures (20 50 bar) and temperature (22 oC) and characterized over a neutron scattering vector (Q) range of 0.02 < Q < 50 -1. This Q range provides information on nominal pore size radii of around 10 0.5 nm. Mineralogy variations between the samples did not affect scattering ratios over the entire Q range accessible with the NIMROD. Q values for the minimum scattering ratios of both samples at similar pressures are statistically indistinguishable and maximum scattering ratios are similar in both samples suggesting that mineral pores are either absent or are so uncommon that they cannot be resolved due to the overwhelming amounts of organic pores in these samples. Overall, these findings suggest that mineralogical variations have little effect on CO2 behavior within organic matter-hosted shale micropores at high thermal maturities and they lend support to the assertion that CO2 cannot be stored in the vast surface areas of micropores (<2.5 nm) in shale formations. In addition, CO2 enhanced oil recovery (EOR) is unlikely to displace petroleum from some of the smaller mesopores (2.5 10 nm) and all of the micropores because they are effectively closed to CO2.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.energyfuels.9b03744","usgsCitation":"Ruppert, L., Jubb, A., Headen, T.F., Youngs, T.G., and Bandli, B., 2020, Impacts of mineralogical variation on CO2 behavior in small pores from producing intervals of the Marcellus Shale: Results from neutron scattering: Energy & Fuels, v. 34, no. 3, p. 2765-2771, https://doi.org/10.1021/acs.energyfuels.9b03744.","productDescription":"7 p.","startPage":"2765","endPage":"2771","ipdsId":"IP-112608","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":379024,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruppert, Leslie F. 0000-0002-7453-1061","orcid":"https://orcid.org/0000-0002-7453-1061","contributorId":242600,"corporation":false,"usgs":true,"family":"Ruppert","given":"Leslie F.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":800461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":800462,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Headen, Thomas F 0000-0003-0095-5731","orcid":"https://orcid.org/0000-0003-0095-5731","contributorId":242601,"corporation":false,"usgs":false,"family":"Headen","given":"Thomas","email":"","middleInitial":"F","affiliations":[],"preferred":false,"id":800463,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Youngs, Tristan G. A.","contributorId":202502,"corporation":false,"usgs":false,"family":"Youngs","given":"Tristan","email":"","middleInitial":"G. A.","affiliations":[{"id":36465,"text":"Disordered Materials Group (ISIS), STFC Rutherford Appleton Laboratory, U.K.","active":true,"usgs":false}],"preferred":false,"id":800464,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bandli, Bryan","contributorId":242602,"corporation":false,"usgs":false,"family":"Bandli","given":"Bryan","email":"","affiliations":[],"preferred":false,"id":800465,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208829,"text":"70208829 - 2020 - Genes in space: What Mojave desert tortoise genetics can tell us about landscape connectivity","interactions":[],"lastModifiedDate":"2020-04-06T23:15:41.220637","indexId":"70208829","displayToPublicDate":"2020-02-08T08:46:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Genes in space: What Mojave desert tortoise genetics can tell us about landscape connectivity","docAbstract":"Habitat loss and fragmentation in the Mojave Desert have been increasing, which can create barriers to movement and gene flow leading to decreased populations of native species. Disturbance and degradation of Mojave desert tortoise habitat includes linear features (e.g. highways, railways, and a network of dirt roads), urbanized areas, and their associated infrastructure, mining activities, energy distribution systems, and most recently, utility-scale solar facilities. To evaluate the spatial genetic structure of tortoises in an area experiencing rapid habitat loss, we conducted field surveys from 2015-2017 and genotyped 299 tortoises at 20 microsatellite loci within and around Ivanpah Valley along the California/Nevada border. We used a Bayesian clustering analysis to examine population genetic structure across valley and mountain pass habitat. Spatial principal components analysis was included to further investigate population genetic structure with isolation-by-distance. To explicitly incorporate landscape features (e.g. habitat and anthropogenic linear barriers) we used maximum likelihood population effects. We assessed recent gene flow on the landscape through maximum likelihood pedigree analyses of relatedness. We detected three to four genetic clusters with high levels of admixture that generally corresponded to three valleys separated by mountain ranges, and a genetically distinguishable population in one mountain pass. Pedigree analyses showed second order relationships up to 60 km apart suggesting a greater range of interactions and inter-relatedness between individuals than previously suspected. Our results support historical gene flow with isolation-by-resistance, and reveal a genetic signal indicative of reduction in genetic connectivity across two parallel linear features (a railway and a highway). This work demonstrates the value of protecting connected tracts of functional habitat and the importance of connectivity research in conservation.","language":"English","publisher":"Springer","doi":"10.1007/s10592-020-01251-z","usgsCitation":"Dutcher, K.E., Vandergast, A.G., Esque, T., Mitelberg, A., Matocq, M.D., Heaton, J.S., and Nussear, K., 2020, Genes in space: What Mojave desert tortoise genetics can tell us about landscape connectivity: Conservation Genetics, v. 21, p. 289-303, https://doi.org/10.1007/s10592-020-01251-z.","productDescription":"15 p.","startPage":"289","endPage":"303","ipdsId":"IP-113961","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":437120,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90LIQRI","text":"USGS data release","linkHelpText":"Microsatellite genotypes for desert tortoise (Gopherus agassizii) in Ivanpah Valley (2015-2017)"},{"id":372836,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada ","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.3946533203125,\n 33.65578083204094\n ],\n [\n -114.70275878906249,\n 33.280027811732154\n ],\n [\n -114.40612792968749,\n 35.14686290675633\n ],\n [\n -115.77941894531249,\n 35.92464453144099\n ],\n [\n -116.70227050781249,\n 35.420391545750746\n ],\n [\n -117.32299804687499,\n 34.985003130171066\n ],\n [\n -116.83959960937499,\n 34.347971491244955\n ],\n [\n -116.3946533203125,\n 33.65578083204094\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Dutcher, Kirsten E.","contributorId":221063,"corporation":false,"usgs":false,"family":"Dutcher","given":"Kirsten","email":"","middleInitial":"E.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":783516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergast, Amy G. 0000-0002-7835-6571 avandergast@usgs.gov","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":3963,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"avandergast@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783515,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mitelberg, Anna amitelberg@usgs.gov","contributorId":173293,"corporation":false,"usgs":true,"family":"Mitelberg","given":"Anna","email":"amitelberg@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Matocq, Marjorie D","contributorId":222917,"corporation":false,"usgs":false,"family":"Matocq","given":"Marjorie","email":"","middleInitial":"D","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":783519,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heaton, Jill S.","contributorId":175155,"corporation":false,"usgs":false,"family":"Heaton","given":"Jill","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":783520,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nussear, Ken E","contributorId":221816,"corporation":false,"usgs":false,"family":"Nussear","given":"Ken E","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":783521,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211522,"text":"70211522 - 2020 - A random forest approach for bounded outcome variables","interactions":[],"lastModifiedDate":"2020-10-12T17:10:57.648419","indexId":"70211522","displayToPublicDate":"2020-02-07T10:57:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2229,"text":"Journal of Computational and Graphical Statistics","active":true,"publicationSubtype":{"id":10}},"title":"A random forest approach for bounded outcome variables","docAbstract":"Random forests have become an established tool for classication and regres-\nsion, in particular in high-dimensional settings and in the presence of non-additive\npredictor-response relationships. For bounded outcome variables restricted to the\nunit interval, however, classical modeling approaches based on mean squared error\nloss may severely suer as they do not account for heteroscedasticity in the data.\nTo address this issue, we propose a random forest approach for relating a beta dis-\ntributed outcome to a set of explanatory variables. Our approach explicitly makes\nuse of the likelihood function of the beta distribution for the selection of splits dur-\ning the tree-building procedure. In each iteration of the tree-building algorithm it\nchooses one explanatory variable in combination with a split point that maximizes\nthe log-likelihood function of the beta distribution with the parameter estimates de-\nrived from the nodes of the currently built tree. Results of several simulation studies\nand an application using data from the U.S.A. National Lakes Assessment Survey\ndemonstrate the properties and usefulness of the method, in particular when com-\npared to random forest approaches based on mean squared error loss and parametric\nregression models.","language":"English","publisher":"Taylor and Francis","doi":"10.1080/10618600.2019.1705310","usgsCitation":"Weinhold, L., Schmid, M., Mitchell, R., Maloney, K.O., Wright, M.N., and Berger, M., 2020, A random forest approach for bounded outcome variables: Journal of Computational and Graphical Statistics, v. 29, no. 3, p. 639-658, https://doi.org/10.1080/10618600.2019.1705310.","productDescription":"20 p.","startPage":"639","endPage":"658","ipdsId":"IP-107449","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":457792,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8193767","text":"External Repository"},{"id":376906,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Weinhold, Leonie","contributorId":236854,"corporation":false,"usgs":false,"family":"Weinhold","given":"Leonie","email":"","affiliations":[{"id":47552,"text":"University of Bonn, Germany","active":true,"usgs":false}],"preferred":false,"id":794489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmid, Matthias","contributorId":236855,"corporation":false,"usgs":false,"family":"Schmid","given":"Matthias","affiliations":[{"id":47552,"text":"University of Bonn, Germany","active":true,"usgs":false}],"preferred":false,"id":794490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitchell, Richard M.","contributorId":215406,"corporation":false,"usgs":false,"family":"Mitchell","given":"Richard M.","affiliations":[{"id":39239,"text":"USEPA, Washington D.C.","active":true,"usgs":false}],"preferred":false,"id":794491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794492,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Marvin N.","contributorId":236856,"corporation":false,"usgs":false,"family":"Wright","given":"Marvin","email":"","middleInitial":"N.","affiliations":[{"id":47553,"text":"Leibniz Institute for Prevention Research and Epidemiology, Germany","active":true,"usgs":false}],"preferred":false,"id":794493,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Berger, Moritz","contributorId":236857,"corporation":false,"usgs":false,"family":"Berger","given":"Moritz","email":"","affiliations":[{"id":47552,"text":"University of Bonn, Germany","active":true,"usgs":false}],"preferred":false,"id":794494,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208976,"text":"70208976 - 2020 - Hydrologic connectivity determines dissolved organic matter biogeochemistry in northern high-latitude lakes","interactions":[],"lastModifiedDate":"2020-08-27T15:06:56.802426","indexId":"70208976","displayToPublicDate":"2020-02-06T18:31:09","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic connectivity determines dissolved organic matter biogeochemistry in northern high-latitude lakes","docAbstract":"Northern high‐latitude lakes are undergoing climate‐induced changes including shifts in their hydrologic connectivity with terrestrial ecosystems. How this will impact dissolved organic matter (DOM) biogeochemistry remains uncertain. We examined the drivers of DOM composition for lakes in the Yukon Flats Basin in Alaska, an arid region of low relief that is characteristic of over one‐quarter of circumpolar lake area. Utilizing the vascular plant biomarker lignin, chromophoric dissolved organic matter (CDOM), and ultrahigh‐resolution mass spectrometry, we interpreted DOM compositional changes using lake‐water stable isotope (δ18O‐H2O) composition as a proxy for lake hydrologic connectivity with the landscape. We observed a relative decrease in CDOM in more hydrologically isolated lakes (enriched δ18O‐H2O) without a corresponding decrease in dissolved organic carbon (DOC) concentration. Although DOC and CDOM were weakly correlated, a significant positive relationship between lignin and CDOM (r2 = 0.67) demonstrates that optical parameters are useful for estimating lignin concentration and thus vascular plant contribution to lake DOM. Indicators of allochthonous DOM, including lignin carbon normalized yields, CDOM aromaticity proxies, and relative abundances of polyphenolic and condensed aromatic compound classes, were negatively correlated with δ18O‐H2O (r2 > 0.45), suggesting there is little allochthonous DOM supplied to many of these hydrologically isolated lakes. We conclude that decreased lake hydrologic connectivity, driven by ongoing climate change (i.e., decreased precipitation, warming temperatures), will reduce allochthonous DOM contributions and shift lakes toward lower CDOM systems with ecosystem‐scale ramifications for heat transfer, photochemical reactions, productivity, and ultimately their biogeochemical function.
","language":"English","publisher":"Wiley","doi":"10.1002/lno.11417","usgsCitation":"Johnston, S.E., Striegl, R.G., Bogard, M.J., Dornblaser, M.M., Butman, D.E., Kellerman, A.M., Wickland, K.P., Podgorski, D.C., and Spencer, R., 2020, Hydrologic connectivity determines dissolved organic matter biogeochemistry in northern high-latitude lakes: Limnology and Oceanography, v. 65, no. 8, p. 1764-1780, https://doi.org/10.1002/lno.11417.","productDescription":"17 p.","startPage":"1764","endPage":"1780","ipdsId":"IP-114991","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":373035,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon Flats Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.40136718749997,\n 66.53076810915225\n ],\n [\n -142.49267578125,\n 66.53076810915225\n ],\n [\n -142.49267578125,\n 69.4960701797534\n ],\n [\n -156.40136718749997,\n 69.4960701797534\n ],\n [\n -156.40136718749997,\n 66.53076810915225\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"65","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnston, Sarah Ellen","contributorId":213256,"corporation":false,"usgs":false,"family":"Johnston","given":"Sarah","email":"","middleInitial":"Ellen","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":784249,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":784250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bogard, Matthew J. 0000-0001-9491-0328","orcid":"https://orcid.org/0000-0001-9491-0328","contributorId":213254,"corporation":false,"usgs":false,"family":"Bogard","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":784251,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dornblaser, Mark M. 0000-0002-6298-3757 mmdornbl@usgs.gov","orcid":"https://orcid.org/0000-0002-6298-3757","contributorId":1636,"corporation":false,"usgs":true,"family":"Dornblaser","given":"Mark","email":"mmdornbl@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":784252,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Butman, David E.","contributorId":145535,"corporation":false,"usgs":false,"family":"Butman","given":"David","email":"","middleInitial":"E.","affiliations":[{"id":16142,"text":"School of Environmental and Forest Sciences & Environmental Engineering, University of Washington, Seattle","active":true,"usgs":false}],"preferred":false,"id":784253,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kellerman, Anne M.","contributorId":204172,"corporation":false,"usgs":false,"family":"Kellerman","given":"Anne","email":"","middleInitial":"M.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":784254,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wickland, Kimberly P. 0000-0002-6400-0590 kpwick@usgs.gov","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":1835,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","email":"kpwick@usgs.gov","middleInitial":"P.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":784248,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Podgorski, David C.","contributorId":178153,"corporation":false,"usgs":false,"family":"Podgorski","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":784255,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Spencer, Robert G. M.","contributorId":139731,"corporation":false,"usgs":false,"family":"Spencer","given":"Robert G. M.","affiliations":[{"id":12894,"text":"Department of Land, Air, and Water Resources, University of California, One Shields Avenue, Davis, CA, 95616, USA","active":true,"usgs":false}],"preferred":false,"id":784256,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70211184,"text":"70211184 - 2020 - Northward migration of the Oregon forearc on the Gales Creek fault","interactions":[],"lastModifiedDate":"2020-07-16T15:42:10.873436","indexId":"70211184","displayToPublicDate":"2020-02-06T10:36:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Northward migration of the Oregon forearc on the Gales Creek fault","docAbstract":"The Gales Creek fault (GCF) is a 60-km-long, northwest-striking dextral fault system (west of Portland, Oregon) that accommodates northward motion and uplift of the Oregon Coast Range. New geologic mapping and geophysical models confirm inferred offsets from earlier geophysical surveys and document ∼12 km of right-lateral offset of a basement high in Eocene Siletz River Volcanics since ca. 35 Ma and ∼8.8 km of right-lateral separation of Miocene Columbia River Basalt at Newberg, Oregon, since 15 Ma (∼0.62 ± 0.12 mm/yr, average long-term rate). Relative uplift of Eocene Coast Range basalt basement west of the fault zone is at least 5 km based on depth to basement under the Tualatin Basin from a recent inversion of gravity data. West of the city of Forest Grove, the fault consists of two subparallel strands ∼7 km apart. The westernmost, Parsons Creek strand, forms a linear valley southward to Henry Hagg Lake, where it continues southward to Newberg as a series of en echelon strands forming both extensional and compressive step-overs. Compressive step-overs in the GCF occur at intersections with ESE-striking sinistral faults crossing the Coast Range, suggesting the GCF is the eastern boundary of an R′ Riedel shear domain that could accommodate up to half of the ∼45° of post–40 Ma clockwise rotation of the Coast Range documented by paleomagnetic studies. Gravity and magnetic anomalies suggest the western strands of the GCF extend southward beneath Newberg into the Northern Willamette Valley, where colinear magnetic anomalies have been correlated with the Mount Angel fault, the proposed source of the 1993 M 5.7 Scotts Mills earthquake. The potential-field data and water-well data also indicate the eastern, Gales Creek strand of the fault may link to the NNW-striking Canby fault through the E-W Beaverton fault to form a 30-km-wide compressive step-over along the south side of the Tualatin Basin. LiDAR data reveal right-lateral stream offsets of as much as 1.5 km, shutter ridges, and other youthful geomorphic features for 60 km along the geophysical and geologic trace of the GCF north of Newberg, Oregon. Paleoseismic trenches document Eocene bedrock thrust over 250 ka surficial deposits along a reverse splay of the fault system near Yamhill, Oregon, and Holocene motion has been recently documented on the GCF along Scoggins Creek and Parsons Creek. The GCF could produce earthquakes in excess of Mw 7, if the entire 60 km segment ruptured in one earthquake. The apparent subsurface links of the GCF to other faults in the Northern Willamette Valley suggest that other faults in the system may also be active.
1. Managing for threatened and endangered species under changing environmental conditions is a challenge faced by resource managers worldwide. Lack of basic knowledge of the biology and habitat requirements of these species can contribute to this difficulty, but is confounded by the limitations of working with rare (i.e. few individuals) species or unrefined methods for evaluating stress.
2. A weight of evidence approach was used to evaluate the thermal biology of the federally endangered dwarf wedgemussel (Alasmidonta heterodon), utilizing cumulative results from multiple experimental assessments, co-occurring species, and their host fish to begin defining thermal limits and optimal conditions for the species.
3. Results suggest that dwarf wedgemussel and its host fish are thermally sensitive species compared to other Atlantic-slope mussels, with lower critical thermal maximum and selection of reduced temperatures during choice experiments.
4. Physiological studies resulted in lack of statistical significance primarily due to low power which was a function of sample size, one unavoidable problem when studying rare species. Given these limitations, thermal choice and CTM may be more useful endpoints than physiological processes such as clearance and respiration rates when dealing with sample size limitations.
5. These results suggest that management strategies that avoid exposing dwarf wedgemussel and its thermally sensitive host fish to extreme temperatures could be important for species conservation.
","language":"English","publisher":"Wiley","doi":"10.1002/aqc.3287","collaboration":"","usgsCitation":"Galbraith, H., Blakeslee, C.J., Spooner, D.E., and Lellis, W.A., 2020, A weight-of-evidence approach for defining thermal sensitivity in a federally endangered species: Aquatic Conservation: Marine and Freshwater Ecosystems, v. 30, no. 3, p. 540-553, https://doi.org/10.1002/aqc.3287.","productDescription":"14 p.","startPage":"540","endPage":"553","ipdsId":"IP-098162","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":437123,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T7YVOW","text":"USGS data release","linkHelpText":"Laboratory studies on the thermal biology of freshwater mussels and their host fish species"},{"id":374188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey, Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.749755859375,\n 38.66835610151506\n ],\n [\n -73.95996093749999,\n 38.66835610151506\n ],\n [\n -73.95996093749999,\n 41.78769700539063\n ],\n [\n -79.749755859375,\n 41.78769700539063\n ],\n [\n -79.749755859375,\n 38.66835610151506\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Galbraith, Heather 0000-0003-3704-3517","orcid":"https://orcid.org/0000-0003-3704-3517","contributorId":207512,"corporation":false,"usgs":true,"family":"Galbraith","given":"Heather","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":787622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blakeslee, Carrie J. 0000-0002-0801-5325 cblakeslee@usgs.gov","orcid":"https://orcid.org/0000-0002-0801-5325","contributorId":5462,"corporation":false,"usgs":true,"family":"Blakeslee","given":"Carrie","email":"cblakeslee@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":787623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spooner, Daniel E. 0000-0002-5408-4364 dspooner@usgs.gov","orcid":"https://orcid.org/0000-0002-5408-4364","contributorId":4603,"corporation":false,"usgs":true,"family":"Spooner","given":"Daniel","email":"dspooner@usgs.gov","middleInitial":"E.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":787624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lellis, William A. 0000-0001-7806-2904 wlellis@usgs.gov","orcid":"https://orcid.org/0000-0001-7806-2904","contributorId":2369,"corporation":false,"usgs":true,"family":"Lellis","given":"William","email":"wlellis@usgs.gov","middleInitial":"A.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":787625,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250307,"text":"70250307 - 2020 - Discrimination of biological scatterers in polarimetric weather radar data: Opportunities and challenges","interactions":[],"lastModifiedDate":"2023-12-01T12:58:33.241741","indexId":"70250307","displayToPublicDate":"2020-02-06T06:56:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Discrimination of biological scatterers in polarimetric weather radar data: Opportunities and challenges","docAbstract":"The Devonian Period experienced significant fluctuations of atmospheric oxygen (O2) levels (∼25–13%), for which the extent and timing are debated. Also characteristic of the Devonian Period, at the Frasnian–Famennian (F–F) boundary, is one of the “big five” mass extinction events of the Phanerozoic. Fossilized charcoal (inertinite) provides a record of wildfire events, which in turn can provide insight into the evolution of terrestrial ecosystems and the atmospheric composition. Here, we report organic petrology, programmed pyrolysis analysis, major and trace element analyses, and initial osmium isotope (Osi) stratigraphy from five sections of Upper Devonian (F–F interval) from western New York, USA. These data are discussed to infer evidence of a wildfire event at the F–F boundary. Based on the evidence for a wildfire at the F–F boundary we also provide an estimate of atmospheric O2 levels of ∼23–25% at this interval, which is in agreement with the models that predict elevated pO2 levels during the Late Devonian. This, coupled with our Os isotope records, support the currently published Osi data that lacks any evidence for an extra-terrestrial impact or volcanic event at the F–F interval, and therefore to act as a trigger for the F–F mass extinction. The elevated O2 level at the F–F interval inferred from this study supports the hypothesis that pCO2 drawdown and associated climate cooling may have acted as a driving mechanism of the F–F mass extinction.
Management agencies are often charged with providing fisheries that lead to angler participation. Catch rate is one of the primary drivers of angler participation but can be influenced by a suite of factors, including population structure (e.g., density and size structure). The complexity of understanding how population structure influences angler catch rate is typified in kokanee Oncorhynchus nerka fisheries. Previous research suggests that angler catch rates of kokanee are positively influenced by fish density and total length. However, that research was based on data collected using size-selective midwater trawls. Due to the potential limitation of previous research, we sought to (1) understand the relative bias of midwater trawls and gill nets for describing the size structure of kokanee available to anglers and (2) re-evaluate the influence of fish density and fish length on angler catch rates in kokanee fisheries. Midwater trawl, gill-net, and creel data were collected on five prominent kokanee fisheries throughout Idaho in 2016 and 2017. Catch composition and percent overlap of midwater trawls, gill nets, and angler-caught fish were compared to understand the efficacy of midwater trawls and gill nets for representing the size structure of kokanee available to anglers. In addition, the influence of kokanee density and length on angler catch rates was evaluated. Midwater trawls primarily sampled small kokanee (<330 mm) and exhibited little overlap with angler-caught fish, whereas gill nets sampled more large fish (>330 mm) and exhibited higher overlap with angler-caught fish when compared to midwater trawls. Fish length was not positively associated with angler catch rates. However, fish density exhibited a positive relationship with angler catch rates. Our results highlight the importance of gear choice for understanding how kokanee populations function and elucidate the tradeoffs associated with population density, fish length, and resulting kokanee fisheries.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10395","usgsCitation":"Klein, Z.B., Quist, M., Schill, D., Dux, A.M., and Corsi, M.P., 2020, Influence of population density and length structure on angler catch rate in kokanee fisheries: North American Journal of Fisheries Management, v. 40, no. 1, p. 182-189, https://doi.org/10.1002/nafm.10395.","productDescription":"8 p.","startPage":"182","endPage":"189","ipdsId":"IP-110218","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395628,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Anderson Ranch Reservoir, Dworshak Reservoir, Lake Pend Oreille, Lucky Peak Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 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Seattle","active":true,"usgs":true}],"preferred":true,"id":833568,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schill, Daniel J.","contributorId":274498,"corporation":false,"usgs":false,"family":"Schill","given":"Daniel J.","affiliations":[{"id":56023,"text":"idfg","active":true,"usgs":false}],"preferred":false,"id":833569,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dux, Andrew M.","contributorId":212798,"corporation":false,"usgs":false,"family":"Dux","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":833570,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corsi, Matthew P.","contributorId":212797,"corporation":false,"usgs":false,"family":"Corsi","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":833571,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228272,"text":"70228272 - 2020 - Movement dynamics of Smallmouth Bass in a large western river system","interactions":[],"lastModifiedDate":"2022-02-08T16:21:40.939488","indexId":"70228272","displayToPublicDate":"2020-02-01T10:11:51","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":"Movement dynamics of Smallmouth Bass in a large western river system","docAbstract":"The Snake River, Idaho, between Swan Falls and Brownlee dams supports a popular fishery for Smallmouth Bass Micropterus dolomieu. Recently, anglers have expressed concern about harvest of Smallmouth Bass associated with seasonal congregations in and near the lower reaches of several major tributaries. Little is known about Smallmouth Bass movement in the system, and a better understanding of movement dynamics will help to guide future management. From March to August 2016, Smallmouth Bass (≥260 mm; n = 1,131) were tagged with T-bar anchor tags to evaluate large-scale movement patterns. Movement was estimated from 63 angler-reported tags for which area descriptions provided sufficient detail to assign a recapture location. Extent of fish movement varied among segments and tributaries from 0 to 128 river kilometers (rkm). From March to May 2017, Smallmouth Bass (≥305 mm; n = 149) in the Snake, Boise, Payette, and Weiser rivers and in Brownlee Reservoir were implanted with radio transmitters. Of the 149 Smallmouth Bass that were released with radio transmitters, 107 were relocated at least once. Additionally, 79.6% of fish with radio transmitters had a maximum extent of movement of 5 rkm or greater and 42.6% had a maximum extent of 30 rkm or greater; one radio-tagged fish moved 167 rkm upstream. Average daily movement of Smallmouth Bass varied among river segments and was greatest in the spring and summer. Fish from the Snake River, tributaries (e.g., Boise River), and Brownlee Reservoir moved all around the study area, indicating an absence of clear population boundaries. As such, Smallmouth Bass in the study area appear to function as one large population as opposed to multiple subpopulations, thereby indicating that management as one population is likely appropriate.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10389","usgsCitation":"McClure, C., Quist, M., Kozfkay, J., Peterson, M., and Schill, D.J., 2020, Movement dynamics of Smallmouth Bass in a large western river system: North American Journal of Fisheries Management, v. 40, no. 1, p. 154-162, https://doi.org/10.1002/nafm.10389.","productDescription":"9 p.","startPage":"154","endPage":"162","ipdsId":"IP-097796","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395627,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Boise River, Brownlee Reservoir, Payette River, Snake River, Swan Falls, Weiser River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.83911132812499,\n 43.83452678223682\n ],\n [\n -114.97192382812499,\n 43.83452678223682\n ],\n [\n -114.97192382812499,\n 45.66012730272194\n ],\n [\n -118.83911132812499,\n 45.66012730272194\n ],\n [\n -118.83911132812499,\n 43.83452678223682\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-12-24","publicationStatus":"PW","contributors":{"authors":[{"text":"McClure, Conor","contributorId":275013,"corporation":false,"usgs":false,"family":"McClure","given":"Conor","email":"","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":833578,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":270713,"corporation":false,"usgs":true,"family":"Quist","given":"Michael C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kozfkay, Joseph","contributorId":275014,"corporation":false,"usgs":false,"family":"Kozfkay","given":"Joseph","email":"","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":833579,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peterson, Michael","contributorId":275015,"corporation":false,"usgs":false,"family":"Peterson","given":"Michael","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":833580,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schill, Daniel J.","contributorId":195886,"corporation":false,"usgs":false,"family":"Schill","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":833581,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208285,"text":"70208285 - 2020 - Overall results and key findings on the use of UAV visible-color, multispectral, and thermal infrared imagery to map agricultural drainage pipes","interactions":[],"lastModifiedDate":"2020-02-03T09:46:34","indexId":"70208285","displayToPublicDate":"2020-02-01T09:40:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":680,"text":"Agricultural Water Management","active":true,"publicationSubtype":{"id":10}},"title":"Overall results and key findings on the use of UAV visible-color, multispectral, and thermal infrared imagery to map agricultural drainage pipes","docAbstract":"Effective and efficient methods are needed to map agricultural subsurface drainage systems. Visible-color (VIS-C), multispectral (MS), and thermal infrared (TIR) imagery obtained by unmanned aerial vehicles (UAVs) may provide a means for determining drainage pipe locations. Aerial surveys using a UAV with VIS-C, MS, and TIR cameras were conducted at 29 agricultural field sites in the Midwest U.S.A. to evaluate the potential of this technology for mapping buried drainage pipes. Overall results show VIS-C imagery detected at least some drain lines at 48 % of the sites (14 out of 29), MS imagery detected drain lines at 59 % of the sites (17 out of 29), and TIR imagery detected drain lines at 69 % of the sites (20 out of 29). Three key findings, listed as follows and emphasized in this article by site examples, were extracted from the overall results. (1) Although TIR generally worked best, there were sites where either VIS-C or MS proved more effective than TIR for mapping subsurface drainage systems. Consequently, to ensure the greatest chance for successfully determining drainage pipe patterns in a field, UAV surveys need to be carried out with all three types of cameras, VIS-C, MS, and TIR. (2) Timing of UAV surveys relative to recent rainfall can sometimes have an important impact on drainage pipe detection results. (3) Linear features representing drain lines and farm field operations can be confused with one another and are often both depicted on site aerial imagery. Knowledge of subsurface drainage system installation and farm field operations can be employed to distinguish linear features representing drain lines from those representing farm field operations. The overall results and extracted key findings from this study clearly indicate that VIS-C, MS, and TIR imagery obtained with UAVs have significant potential for use in mapping agricultural drainage pipe systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agwat.2020.106036","usgsCitation":"Allred, B.J., Martinez, L., Fessehazion, M., Rouse, G., Williamson, T.N., Wishart, D., Koganti, T., Freeland, R., Eash, N., Batschelet, A., and Featheringill, R., 2020, Overall results and key findings on the use of UAV visible-color, multispectral, and thermal infrared imagery to map agricultural drainage pipes: Agricultural Water Management, v. 232, 106036, 19 p., https://doi.org/10.1016/j.agwat.2020.106036.","productDescription":"106036, 19 p.","ipdsId":"IP-112452","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":457912,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agwat.2020.106036","text":"Publisher Index Page"},{"id":371912,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana, Iowa, Michigan, Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.240234375,\n 42.79540065303723\n ],\n [\n -95.16357421875,\n 42.79540065303723\n ],\n [\n -95.16357421875,\n 43.45291889355465\n ],\n [\n -96.240234375,\n 43.45291889355465\n ],\n [\n -96.240234375,\n 42.79540065303723\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.4951171875,\n 38.42777351132902\n ],\n [\n -81.76025390625,\n 38.42777351132902\n ],\n [\n -81.76025390625,\n 42.09822241118974\n ],\n [\n -87.4951171875,\n 42.09822241118974\n ],\n [\n -87.4951171875,\n 38.42777351132902\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"232","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Allred, Barry J.","contributorId":212023,"corporation":false,"usgs":false,"family":"Allred","given":"Barry","email":"","middleInitial":"J.","affiliations":[{"id":38388,"text":"USDA, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":781251,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martinez, Luis","contributorId":222112,"corporation":false,"usgs":false,"family":"Martinez","given":"Luis","email":"","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":781252,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fessehazion, Melake","contributorId":222113,"corporation":false,"usgs":false,"family":"Fessehazion","given":"Melake","email":"","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":781253,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rouse, Greg","contributorId":169158,"corporation":false,"usgs":false,"family":"Rouse","given":"Greg","email":"","affiliations":[{"id":6728,"text":"Scripps Inst Oceanography","active":true,"usgs":false}],"preferred":false,"id":781254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williamson, Tanja N. 0000-0002-7639-8495 tnwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-8495","contributorId":198329,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja","email":"tnwillia@usgs.gov","middleInitial":"N.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781250,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wishart, DeBonne","contributorId":222114,"corporation":false,"usgs":false,"family":"Wishart","given":"DeBonne","email":"","affiliations":[{"id":40490,"text":"Central State University - Ohio","active":true,"usgs":false}],"preferred":false,"id":781255,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Koganti, Triven 0000-0001-5351-7618","orcid":"https://orcid.org/0000-0001-5351-7618","contributorId":222115,"corporation":false,"usgs":false,"family":"Koganti","given":"Triven","email":"","affiliations":[{"id":40491,"text":"Aarhus University - Denmark","active":true,"usgs":false}],"preferred":false,"id":781256,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Freeland, Robert 0000-0002-5243-9774","orcid":"https://orcid.org/0000-0002-5243-9774","contributorId":222116,"corporation":false,"usgs":false,"family":"Freeland","given":"Robert","email":"","affiliations":[{"id":37419,"text":"University of Tennessee Institute of Agriculture","active":true,"usgs":false}],"preferred":false,"id":781257,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Eash, Neal 0000-0001-9141-4302","orcid":"https://orcid.org/0000-0001-9141-4302","contributorId":222117,"corporation":false,"usgs":false,"family":"Eash","given":"Neal","email":"","affiliations":[{"id":37419,"text":"University of Tennessee Institute of Agriculture","active":true,"usgs":false}],"preferred":false,"id":781258,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Batschelet, Adam","contributorId":222118,"corporation":false,"usgs":false,"family":"Batschelet","given":"Adam","email":"","affiliations":[{"id":40492,"text":"Green Aero Tech USA","active":true,"usgs":false}],"preferred":false,"id":781259,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Featheringill, Robert","contributorId":222119,"corporation":false,"usgs":false,"family":"Featheringill","given":"Robert","email":"","affiliations":[{"id":40493,"text":"Farmer and former drainage contractor","active":true,"usgs":false}],"preferred":false,"id":781260,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70228237,"text":"70228237 - 2020 - Eastern oyster clearance and respiration rates in response to acute and chronic exposure to suspended sediment loads","interactions":[],"lastModifiedDate":"2022-02-08T15:47:02.571742","indexId":"70228237","displayToPublicDate":"2020-02-01T09:31:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2449,"text":"Journal of Sea Research","active":true,"publicationSubtype":{"id":10}},"title":"Eastern oyster clearance and respiration rates in response to acute and chronic exposure to suspended sediment loads","docAbstract":"Coastal Louisiana supports some of the most productive areas for the eastern oyster, Crassostrea virginica. Changing conditions from restoration and climate change alter freshwater and sediment inflows into critical estuarine areas affecting water quality, including salinity and concentrations of suspended sediment. This study examined the effects of acute (1 h) and chronic (8 weeks) exposure of suspended sediment concentrations on the eastern oyster's respiration and clearance rates. Acute exposure at six sediment concentrations (0, 10, 50, 200, 500, 1000 mg L−1) and one salinity (15) indicated that sediment concentration significantly affected oyster clearance rates, with increasing clearance rates as suspended sediment concentrations increased up to 500 mg L−1. Respiration rates were not affected by sediment concentration (p = .12). Chronic exposure at two salinities (6 and 15) and three sediment concentrations (0, 50, 400 mg L−1) found no significant effect of sediment, salinity or their interaction on clearance rates. Respiration rate was reduced at higher sediment concentrations (50 and 400 mg L−1 versus 0 mg L−1) and lower salinity. As clearance and oxygen consumption rates critically inform oyster energetic models, these data provide valuable insight to more accurately predict eastern oyster population dynamics and inform harvest models in the face of changing estuarine conditions. Changes in rates of growth through altered energetic demands ultimately can impact not just the economic viability of the industry, but also the ability for the populations to maintain sustainable reefs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.seares.2019.101831","usgsCitation":"La Peyre, M., Bernasconi, S.K., Lavaud, R., Casas, S.M., and La Peyre, J.F., 2020, Eastern oyster clearance and respiration rates in response to acute and chronic exposure to suspended sediment loads: Journal of Sea Research, v. 157, p. 1-7, https://doi.org/10.1016/j.seares.2019.101831.","productDescription":"101831, 7 p.","startPage":"1","endPage":"7","ipdsId":"IP-109899","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499828,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.lsu.edu/animalsciences_pubs/794","text":"External Repository"},{"id":395621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Bay Gardene","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.68508720397949,\n 29.56188581810685\n ],\n [\n -89.6129035949707,\n 29.56188581810685\n ],\n [\n -89.6129035949707,\n 29.609804580144143\n ],\n [\n -89.68508720397949,\n 29.609804580144143\n ],\n [\n -89.68508720397949,\n 29.56188581810685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"157","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833502,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernasconi, S. K.","contributorId":274906,"corporation":false,"usgs":false,"family":"Bernasconi","given":"S.","email":"","middleInitial":"K.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":833503,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lavaud, R.","contributorId":273051,"corporation":false,"usgs":false,"family":"Lavaud","given":"R.","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":833504,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Casas, S. M.","contributorId":272906,"corporation":false,"usgs":false,"family":"Casas","given":"S.","email":"","middleInitial":"M.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":833506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"La Peyre, J. F.","contributorId":273052,"corporation":false,"usgs":false,"family":"La Peyre","given":"J.","email":"","middleInitial":"F.","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":833505,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218239,"text":"70218239 - 2020 - Infrasound generated by the 2016-2017 shallow submarine eruption of Bogoslof volcano, Alaska","interactions":[],"lastModifiedDate":"2021-02-19T16:31:08.740362","indexId":"70218239","displayToPublicDate":"2020-01-31T10:19:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Infrasound generated by the 2016-2017 shallow submarine eruption of Bogoslof volcano, Alaska","docAbstract":"The 2016–2017 shallow submarine eruption of Bogoslof volcano produced numerous infrasound signals over 9 months that were recorded on six Alaska Volcano Observatory (AVO) arrays at ranges of 59 to over 800 km from the volcano. The lack of geophysical monitoring near Bogoslof and the repeated production of volcanic clouds to flight levels made monitoring by remote infrasound critical during the eruption; for the first time, AVO relied extensively on automated infrasound detections from regional arrays to dispatch timely notifications of the ongoing activity. Most of the 70 eruptive events were detected on at least one array, but no array detected all of the events mainly because atmospheric conditions were highly variable during the eruption. Acoustic propagation modeling helps explain some of the variation in array detections but also highlights limitations in regional propagation models. To our knowledge, this is the first example of well-recorded infrasound from an explosive eruption occurring in shallow seawater, providing extensive insights into eruption dynamics in this unique environment. The dominance of low-frequency infrasound (0.1–1 Hz) is attributed to eruptions occurring beneath tens of meters of seawater. Higher-frequency infrasound signals were mostly limited to eruptions where the vent was isolated from major interaction with seawater or in several cases where a lava dome grew above sea level.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-019-1355-0","usgsCitation":"Lyons, J.J., Iezzi, A., Fee, D., Schwaiger, H., Wech, A., and Haney, M.M., 2020, Infrasound generated by the 2016-2017 shallow submarine eruption of Bogoslof volcano, Alaska: Bulletin of Volcanology, v. 82, 19, 14 p., https://doi.org/10.1007/s00445-019-1355-0.","productDescription":"19, 14 p.","ipdsId":"IP-112505","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":383363,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bogoslof Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -178.63769531249997,\n 48.86471476180277\n ],\n [\n -155.0390625,\n 48.86471476180277\n ],\n [\n -155.0390625,\n 61.39671887310411\n ],\n [\n -178.63769531249997,\n 61.39671887310411\n ],\n [\n -178.63769531249997,\n 48.86471476180277\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2020-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":810600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":196436,"corporation":false,"usgs":false,"family":"Iezzi","given":"Alexandra M.","affiliations":[],"preferred":false,"id":810601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fee, David","contributorId":199660,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[],"preferred":false,"id":810602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schwaiger, Hans 0000-0001-7397-8833","orcid":"https://orcid.org/0000-0001-7397-8833","contributorId":214983,"corporation":false,"usgs":true,"family":"Schwaiger","given":"Hans","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810603,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wech, Aaron 0000-0003-4983-1991","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":202561,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810604,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":810605,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236098,"text":"70236098 - 2020 - SPEAR: The next generation GFDL modeling system for seasonal to multidecadal prediction and projection","interactions":[],"lastModifiedDate":"2022-08-29T11:53:33.588643","indexId":"70236098","displayToPublicDate":"2020-01-31T06:49:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5407,"text":"Journal of Advances in Modeling Earth Systems","active":true,"publicationSubtype":{"id":10}},"title":"SPEAR: The next generation GFDL modeling system for seasonal to multidecadal prediction and projection","docAbstract":"We document the development and simulation characteristics of the next generation modeling system for seasonal to decadal prediction and projection at the Geophysical Fluid Dynamics Laboratory (GFDL). SPEAR (Seamless System for Prediction and EArth System Research) is built from component models recently developed at GFDL—the AM4 atmosphere model, MOM6 ocean code, LM4 land model, and SIS2 sea ice model. The SPEAR models are specifically designed with attributes needed for a prediction model for seasonal to decadal time scales, including the ability to run large ensembles of simulations with available computational resources. For computational speed SPEAR uses a coarse ocean resolution of approximately 1.0° (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1° to 0.25°. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM4 models but with design choices geared toward seasonal to multidecadal physical climate prediction and projection. We document simulation characteristics for the time mean climate, aspects of internal variability, and the response to both idealized and realistic radiative forcing change. We describe in greater detail one focus of the model development process that was motivated by the importance of the Southern Ocean to the global climate system. We present sensitivity tests that document the influence of the Antarctic surface heat budget on Southern Ocean ventilation and deep global ocean circulation. These findings were also useful in the development processes for the GFDL CM4 and ESM4 models.
We used molar measurements from 136 known-age red tree voles (Arborimus longicaudus) to develop regression models that could estimate tree vole age from skeletonized remains. The best regression included a quadratic structure of the ratio between two measurements, crown height and anterior height, and natural log-transformed age in days. The regression predicted that molar roots begin to develop at 40 days of age and that molar crowns are worn completely away at 1,177 days of age. We used the regression to estimate the age distribution of 1,703 red tree voles found in northern spotted owl (Strix occidentalis caurina) pellets collected in western Oregon during 1970–2009. The age distribution of red tree voles in pellets was dominated by young individuals, with 81% younger than one year and only 0.5% older than two years. The proportion of individuals 61–120 days old was particularly high relative to other age classes. The proportion of subadult (52–120 days old) individuals exhibited regional variation between the Oregon Cascades and the Coast Range. Localized annual variation in age distribution was low, exhibited no evidence of cyclic variation, and was positively associated with local precipitation rates during the spotted owl nesting season (March–June). We hypothesize that the age distribution of tree voles in owl pellets may be similar to the age structure of tree vole populations in the wild, but acknowledge that this is virtually impossible to test because tree voles cannot be adequately sampled using conventional small mammal capture methods.
","language":"English","publisher":"BioOne","doi":"10.3955/046.093.0304","usgsCitation":"Marks-Fife, C.A., Forsman, E.D., and Dugger, K., 2020, Age distribution of red tree voles in northern spotted owl pellets estimated from molar tooth development: Northwest Science, v. 93, no. 3-4, p. 193-208, https://doi.org/10.3955/046.093.0304.","productDescription":"16 p.","startPage":"193","endPage":"208","ipdsId":"IP-092934","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395244,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","issue":"3-4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marks-Fife, Chad A.","contributorId":272849,"corporation":false,"usgs":false,"family":"Marks-Fife","given":"Chad","email":"","middleInitial":"A.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":832335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Forsman, Eric D.","contributorId":96792,"corporation":false,"usgs":false,"family":"Forsman","given":"Eric","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":832336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832334,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223371,"text":"70223371 - 2020 - Effects of temperature on hatching rate and early development of alligator gar and spotted gar in a laboratory setting","interactions":[],"lastModifiedDate":"2021-08-25T13:11:18.644277","indexId":"70223371","displayToPublicDate":"2020-01-27T08:08:10","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":"Effects of temperature on hatching rate and early development of alligator gar and spotted gar in a laboratory setting","docAbstract":"Water temperature influences both morphological and physiological development in fishes. However, the effects of water temperature on the early development of Alligator Gar Atractosteus spatula and Spotted Gar Lepisosteus oculatus are not well understood. Both gar species were collected from natural environments and spawned in a hatchery setting. After spawning, fertilized embryos were collected and transferred to the Oklahoma Fishery Research Laboratory, where the embryos (50–72 embryos/treatment) were placed into one of five water temperature treatments (15.5, 20.0, 23.8, 27.5, and 32.2°C) and observed over time to estimate the time to hatch and the time to reach the free-swimming stage. Both species showed an inverse relationship between temperature and the timing of hatch and advancement to free-swimming fingerlings for all treatments. In addition, Alligator Gar embryos did not develop at the coldest water temperature tested, and Alligator Gar juveniles held at the warmest temperature tested were observed with developmental abnormalities, potentially affecting their survival. The same temperature extremes had no comparable negative effect on Spotted Gar. The results of this study are useful for understanding early life history dynamics of these two species in their natural environments and can also be used by hatchery managers who are seeking to optimize their production protocols.