Carbon cycling in the coastal zone affects global carbon budgets and is critical for understanding the urgent issues of hypoxia, acidification, and tidal wetland loss. However, there are no regional carbon budgets spanning the three main ecosystems in coastal waters: tidal wetlands, estuaries, and shelf waters. Here we construct such a budget for eastern North America using historical data, empirical models, remote sensing algorithms, and process‐based models. Considering the net fluxes of total carbon at the domain boundaries, 59 ± 12% (± 2 standard errors) of the carbon entering is from rivers and 41 ± 12% is from the atmosphere, while 80 ± 9% of the carbon leaving is exported to the open ocean and 20 ± 9% is buried. Net lateral carbon transfers between the three main ecosystem types are comparable to fluxes at the domain boundaries. Each ecosystem type contributes substantially to exchange with the atmosphere, with CO2uptake split evenly between tidal wetlands and shelf waters, and estuarine CO2outgassing offsetting half of the uptake. Similarly, burial is about equal in tidal wetlands and shelf waters, while estuaries play a smaller but still substantial role. The importance of tidal wetlands and estuaries in the overall budget is remarkable given that they, respectively, make up only 2.4 and 8.9% of the study domain area. This study shows that coastal carbon budgets should explicitly include tidal wetlands, estuaries, shelf waters, and the linkages between them; ignoring any of them may produce a biased picture of coastal carbon cycling.
","language":"English","publisher":"AGU","doi":"10.1002/2017GB005790","usgsCitation":"Najjar, R., Herrmann, M., Alexander, R.B., Boyer, E., Burdige, D., Butman, D., Cai, W., Canuel, E., Chen, R., Friedrichs, M.A., Feagin, R., Griffith, P.C., Hinson, A., Holmquist, J., Hu, X., Kemp, W., Kroeger, K.D., Mannino, A., McCallister, S., McGillis, W., Mulholland, M., Pilskaln, C.H., Salisbury, J., Signorini, S., St. Laurent, P., Tian, H., Tzortziou, M., Vlahos, P., Wan, Z., and Zimmerman, R.C., 2018, Carbon budget of tidal wetlands, estuaries, and shelf waters of eastern North America: Global Biogeochemical Cycles, v. 32, no. 3, p. 389-416, https://doi.org/10.1002/2017GB005790.","productDescription":"28 p.","startPage":"389","endPage":"416","ipdsId":"IP-092980","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468939,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/2017gb005790","text":"External Repository"},{"id":360518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"32","issue":"3","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-11","publicationStatus":"PW","scienceBaseUri":"5c1a1534e4b0708288c23542","contributors":{"authors":[{"text":"Najjar, R.G.","contributorId":211647,"corporation":false,"usgs":false,"family":"Najjar","given":"R.G.","affiliations":[{"id":38295,"text":"Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania, USA.","active":true,"usgs":false}],"preferred":false,"id":754585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herrmann, M.","contributorId":211648,"corporation":false,"usgs":false,"family":"Herrmann","given":"M.","email":"","affiliations":[{"id":38295,"text":"Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania, USA.","active":true,"usgs":false}],"preferred":false,"id":754586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Richard B. 0000-0001-9166-0626 ralex@usgs.gov","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":541,"corporation":false,"usgs":true,"family":"Alexander","given":"Richard","email":"ralex@usgs.gov","middleInitial":"B.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":754587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyer, E.W.","contributorId":56358,"corporation":false,"usgs":false,"family":"Boyer","given":"E.W.","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":754588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burdige, D. 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Stream habitat and fish population characteristics, including biomass, abundance, growth, size, and movement, were assessed over a 9-year period (4 years pre- and 5 years postlogging). The logged catchment was located on private industrial forestland and had been previously logged in 1966. The reference catchment was covered by an unharvested, fire-regenerated forest approximately 150–160 years old, which was unroaded and managed as a Research Natural Area by the USDA Forest Service. A single clearcut harvest unit of the upper 40% of the treatment catchment was implemented following current forest practice regulations, including the retention of riparian buffer of standing trees adjacent to fish bearing channels. No statistically significant negative effects on coastal cutthroat trout or coho salmon occurred following logging, and in fact, both late-summer density and total biomass of age-1+ coastal cutthroat trout increased in the logged catchment following logging. Increases in age-1+ coastal cutthroat were greatest closest to the harvest area and declined downstream as distance from the logged area increased. In contrast to the previous timber harvest in the catchment when few logging regulations existed, current forest practice regulations and logging techniques appear to have reduced acute negative effects on coastal cutthroat trout.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2018.01.030","usgsCitation":"Bateman, D.S., Gresswell, R.E., Warren, D., Hockman-Wert, D., Leer, D.W., Light, J.T., and Stednick, J.D., 2018, Fish response to contemporary timber harvest practices in a second-growth forest from the central Coast Range of Oregon: Forest Ecology and Management, v. 411, p. 142-157, https://doi.org/10.1016/j.foreco.2018.01.030.","productDescription":"16 p.","startPage":"142","endPage":"157","ipdsId":"IP-084981","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":468940,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2018.01.030","text":"Publisher Index Page"},{"id":356934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","volume":"411","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a2eae4b0702d0e84300c","contributors":{"authors":[{"text":"Bateman, Douglas S. 0000-0002-5609-2085 doug_bateman@usgs.gov","orcid":"https://orcid.org/0000-0002-5609-2085","contributorId":207396,"corporation":false,"usgs":false,"family":"Bateman","given":"Douglas","email":"doug_bateman@usgs.gov","middleInitial":"S.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":743727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gresswell, Robert E. 0000-0003-0063-855X bgresswell@usgs.gov","orcid":"https://orcid.org/0000-0003-0063-855X","contributorId":152031,"corporation":false,"usgs":true,"family":"Gresswell","given":"Robert","email":"bgresswell@usgs.gov","middleInitial":"E.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":743726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warren, Dana","contributorId":192215,"corporation":false,"usgs":false,"family":"Warren","given":"Dana","affiliations":[],"preferred":false,"id":743728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hockman-Wert, David 0000-0003-2436-6237 dhockman-wert@usgs.gov","orcid":"https://orcid.org/0000-0003-2436-6237","contributorId":3891,"corporation":false,"usgs":true,"family":"Hockman-Wert","given":"David","email":"dhockman-wert@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":743729,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leer, David W.","contributorId":207397,"corporation":false,"usgs":false,"family":"Leer","given":"David","email":"","middleInitial":"W.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":743730,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Light, Jeffrey T.","contributorId":207398,"corporation":false,"usgs":false,"family":"Light","given":"Jeffrey","email":"","middleInitial":"T.","affiliations":[{"id":37530,"text":"Weyerhaeuser","active":true,"usgs":false}],"preferred":false,"id":743731,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stednick, John D.","contributorId":207399,"corporation":false,"usgs":false,"family":"Stednick","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":37531,"text":"Colorado Statte University","active":true,"usgs":false}],"preferred":false,"id":743732,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202014,"text":"70202014 - 2018 - Sensitivity of Na+/K+-ATPase isoforms to acid and aluminum explains differential effects on Atlantic salmon osmoregulation in fresh water and seawater","interactions":[],"lastModifiedDate":"2019-02-05T14:32:53","indexId":"70202014","displayToPublicDate":"2018-03-01T14:32:44","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of Na+/K+-ATPase isoforms to acid and aluminum explains differential effects on Atlantic salmon osmoregulation in fresh water and seawater","docAbstract":"Atlantic salmon (Salmo salar) smolts are sensitive to acid rain and associated increases in dissolved inorganic aluminum (Al) resulting in decreased seawater tolerance at this critical life stage. Salmon have two major isoforms of the catalytic alpha subunit of Na+/K+-ATPase (NKA), with NKAα1a being the major freshwater (FW) isoform and NKAα1b the major seawater (SW) isoform. Here we evaluate physiological markers of SW preparedness and NKAα1a and NKAα1b isoforms after short-term exposure to acidified water and acidified water with added Al (acid–Al). Atlantic salmon smolts were exposed to low ion FW (Control), low ion acidic water (pH 5.2; acid), and low ion acidic water (pH 5.2) with moderate levels of added inorganic Al (35 μg·L−1; acid–Al) for 4 days. Acid exposure resulted in loss of salinity tolerance (higher plasma chloride (Cl) after SW exposure) and significantly decreased the levels of gill NKAα1b but not NKAα1a protein abundance. Acid–Al exposure resulted in loss of plasma Cl in FW and higher plasma Cl in SW and decreased NKAα1a and NKAα1b abundance. The loss of salinity tolerance in smolts can be explained by the differential sensitivity of NKA isoforms to acid.
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Alaskan boreal forest is underlain by discontinuous permafrost, and wildfires are one of the most influential agents negatively impacting the condition of permafrost in the arctic region. Using interferometric synthetic aperture radar (InSAR) of Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images, we mapped extensive permafrost degradation over interior Alaskan boreal forest in Yukon Flats, induced by the 2009 Big Creek wildfire. Our analyses showed that fire-induced permafrost degradation in the second post-fire thawing season contributed up to 20 cm of ground surface subsidence. We generated post-fire deformation time series and introduced a model that exploited the deformation time series to estimate fire-induced permafrost degradation and changes in active layer thickness. The model showed a wildfire-induced increase of up to 80 cm in active layer thickness in the second post-fire year due to pore-ice permafrost thawing. The model also showed up to 15 cm of permafrost degradation due to excess-ice thawing with little or no increase in active layer thickness. The uncertainties of the estimated change in active layer thickness and the thickness of thawed excess ice permafrost are 27.77 and 1.50 cm, respectively. Our results demonstrate that InSAR-derived deformation measurements along with physics models are capable of quantifying fire-induced permafrost degradation in Alaskan boreal forests underlain by discontinuous permafrost. Our results also have illustrated that fire-induced increase of active layer thickness and excess ice thawing contributed to ground surface subsidence.
","language":"English","publisher":"MDPI","doi":"10.3390/rs10030405","usgsCitation":"Molan, Y.E., Kim, J., Lu, Z., Wylie, B.K., and Zhu, Z., 2018, Modeling wildfire-induced permafrost deformation in an Alaskan boreal forest using InSAR observations: Remote Sensing, v. 10, no. 3, 405, 17 p., https://doi.org/10.3390/rs10030405.","productDescription":"405, 17 p.","ipdsId":"IP-090332","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":468942,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs10030405","text":"Publisher Index Page"},{"id":360566,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -147.25,\n 65.67\n ],\n [\n -146,\n 65.67\n ],\n [\n -146,\n 66.33\n ],\n [\n -147.25,\n 66.33\n ],\n [\n -147.25,\n 65.67\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-06","publicationStatus":"PW","scienceBaseUri":"5c1b66e7e4b0708288c71d3a","contributors":{"authors":[{"text":"Molan, Yusef Eshqi","contributorId":211707,"corporation":false,"usgs":false,"family":"Molan","given":"Yusef","email":"","middleInitial":"Eshqi","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":754668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kim, Jin-Woo","contributorId":211708,"corporation":false,"usgs":false,"family":"Kim","given":"Jin-Woo","email":"","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":754669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lu, Zhong","contributorId":199794,"corporation":false,"usgs":false,"family":"Lu","given":"Zhong","affiliations":[],"preferred":false,"id":754670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":754671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":754667,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202409,"text":"70202409 - 2018 - DEEP SEARCH: Deep sea exploration to advance research on coral/canyon/cold seep habitats","interactions":[],"lastModifiedDate":"2019-03-01T13:03:16","indexId":"70202409","displayToPublicDate":"2018-03-01T13:03:08","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2929,"text":"Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"DEEP SEARCH: Deep sea exploration to advance research on coral/canyon/cold seep habitats","docAbstract":"Launched in August 2017, Deep Sea Exploration to Advance Research on Coral/Canyon/Cold seep Habitats (DEEP SEARCH) is a multiyear, multi-agency study to characterize the deep-sea ecosystems of the US Mid- and South Atlantic (Figure 1). The study is funded through an interagency partnership between NOAA, the Bureau of Ocean Energy Management (BOEM), and the US Geological Survey, and it is sponsored by the National Oceanographic Partnership Program. DEEP SEARCH will spend four and a half years (from 2017 to 2021) researching the habitats of the US Atlantic to better understand the distribution of sensitive seafloor communities to inform potential offshore energy development and other deep-sea management needs.","language":"English","publisher":"The Oceanography Society","doi":"10.5670/oceanog.2018.supplement.01","usgsCitation":"Cordes, E.E., Demopoulos, A.W., Boland, G., and Adams, C., 2018, DEEP SEARCH: Deep sea exploration to advance research on coral/canyon/cold seep habitats: Oceanography, v. 31, no. 1, p. 97-98, https://doi.org/10.5670/oceanog.2018.supplement.01.","productDescription":"2 p.","startPage":"97","endPage":"98","ipdsId":"IP-092245","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468943,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5670/oceanog.2018.supplement.01","text":"Publisher Index Page"},{"id":361646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.5,\n 29\n ],\n [\n -71,\n 29\n ],\n [\n -71,\n 37.5\n ],\n [\n -81.5,\n 37.5\n ],\n [\n -81.5,\n 29\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"1","edition":"Supplement","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cordes, Erik E.","contributorId":37623,"corporation":false,"usgs":false,"family":"Cordes","given":"Erik","email":"","middleInitial":"E.","affiliations":[{"id":16710,"text":"Temple University, Department of Biology","active":true,"usgs":false}],"preferred":false,"id":758294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Demopoulos, Amanda W. J. 0000-0003-2096-4694","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":206536,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","email":"","middleInitial":"W. J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":758293,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boland, Gregory","contributorId":213692,"corporation":false,"usgs":false,"family":"Boland","given":"Gregory","email":"","affiliations":[{"id":25296,"text":"BOEM","active":true,"usgs":false}],"preferred":false,"id":758295,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adams, Caitlin","contributorId":213693,"corporation":false,"usgs":false,"family":"Adams","given":"Caitlin","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":758296,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70198481,"text":"70198481 - 2018 - A phylogenetic overview of the genus Vertigo O. F. Müller, 1773 (Gastropoda: Pulmonata: Pupillidae: Vertigininae)","interactions":[],"lastModifiedDate":"2018-08-06T12:41:18","indexId":"70198481","displayToPublicDate":"2018-03-01T12:41:11","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2648,"text":"Malacologia","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A phylogenetic overview of the genus Vertigo O. F. Müller, 1773 (Gastropoda: Pulmonata: Pupillidae: Vertigininae)","title":"A phylogenetic overview of the genus Vertigo O. F. Müller, 1773 (Gastropoda: Pulmonata: Pupillidae: Vertigininae)","docAbstract":"We document global phylogenetic pattern in the pupillid land snail genus Vertigo by analyses of nDNA (ITS1 and ITS2) and mtDNA (CytB and 16S) sequence from 424 individuals representing 91 putative specific and subspecific Vertigo taxa. nDNA and mtDNA data were separately subjected to neighbor-joining, minimum evolution, maximum likelihood and Bayesian reconstruction methods, with conclusions being drawn from shared topological structures. Six highly supported, reciprocally monophyletic subgeneric level clades were identified: Vertigo, Alaea, Boreovertigo new subgenus, Isthmia, Staurodon and Vertilla. 88 species or subspecies were also confirmed, nine of which are new and formally described herein: V. beringiana, V. chiricahuensis, V. chytryi, V. genesioides, V. kodamai, V. kurilensis, V. lilljeborgi vinlandica, V. pimuensis and V. pisewensis. Thirteen taxa were synonymized: V. arthuri basidens, V. arthuri hubrichti, V. arthuri paradoxa (= V. arthuri); V. allyniana(= V. modesta); V. andrusiana (= V. columbiana); V. conecuhensis (= V. alabamensis); V. dedecora tamagonari (= V. dedecora); V. elatior, V. idahoensis (= V. ventricosa); V. eogea (= V. ovata); V. modesta insculpta (= V. modesta concinnula), V. modesta microphasma, V. modesta sculptilis (= V. modesta castanea). Qualitative observations of conchological features, ecological preferences and geographic coverage were conducted for each subgenus and genetically supported species or subspecies-level taxon. These demonstrated that: (1) a suite of diagnostic shell features usually exists to demarcate each species-level taxon; (2) shell features were incapable of defining genetically validated subgenera; (3) all subgenera had transcontinental ranges; (4) ⅓ of all species possess continental or trans-continental ranges, with very few having range extents < 1,000 km; (5) all subgenera and fully ⅔ of global Vertigo species and subspecies are found in North America, more than 2.5 times the number found in central and eastern Asia, the second most diverse region. This is similar to several other molluscan groups, such as the polygyrid land snails and unionid bivalves for which North America is the global biodiversity hotspot.
","language":"English","publisher":"Institute of Malacology","doi":"10.4002/040.062.0104","usgsCitation":"Nekola, J.C., Chiba, S., Coles, B.F., Drost, C.A., von Proschwitz, T., and Horsak, M., 2018, A phylogenetic overview of the genus Vertigo O. F. Müller, 1773 (Gastropoda: Pulmonata: Pupillidae: Vertigininae): Malacologia, v. 62, no. 1, p. 21-161, https://doi.org/10.4002/040.062.0104.","productDescription":"141 p.","startPage":"21","endPage":"161","ipdsId":"IP-081461","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":356193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc483e4b0f5d57878ea98","contributors":{"authors":[{"text":"Nekola, Jeffrey C.","contributorId":26214,"corporation":false,"usgs":false,"family":"Nekola","given":"Jeffrey","email":"","middleInitial":"C.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":741710,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chiba, Satoshi","contributorId":206770,"corporation":false,"usgs":false,"family":"Chiba","given":"Satoshi","email":"","affiliations":[],"preferred":false,"id":741711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coles, Brian F.","contributorId":206771,"corporation":false,"usgs":false,"family":"Coles","given":"Brian","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":741712,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drost, Charles A. 0000-0002-4792-7095 charles_drost@usgs.gov","orcid":"https://orcid.org/0000-0002-4792-7095","contributorId":3151,"corporation":false,"usgs":true,"family":"Drost","given":"Charles","email":"charles_drost@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":741713,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"von Proschwitz, Ted","contributorId":206772,"corporation":false,"usgs":false,"family":"von Proschwitz","given":"Ted","email":"","affiliations":[],"preferred":false,"id":741714,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Horsak, Michal","contributorId":206773,"corporation":false,"usgs":false,"family":"Horsak","given":"Michal","email":"","affiliations":[],"preferred":false,"id":741715,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70194087,"text":"ofr20171149 - 2018 - ModelArchiver—A program for facilitating the creation of groundwater model archives","interactions":[],"lastModifiedDate":"2018-03-02T10:13:23","indexId":"ofr20171149","displayToPublicDate":"2018-03-01T12:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1149","title":"ModelArchiver—A program for facilitating the creation of groundwater model archives","docAbstract":"ModelArchiver is a program designed to facilitate the creation of groundwater model archives that meet the requirements of the U.S. Geological Survey (USGS) policy (Office of Groundwater Technical Memorandum 2016.02, https://water.usgs.gov/admin/memo/GW/gw2016.02.pdf, https://water.usgs.gov/ogw/policy/gw-model/). ModelArchiver version 1.0 leads the user step-by-step through the process of creating a USGS groundwater model archive. The user specifies the contents of each of the subdirectories within the archive and provides descriptions of the archive contents. Descriptions of some files can be specified automatically using file extensions. Descriptions also can be specified individually. Those descriptions are added to a readme.txt file provided by the user. ModelArchiver moves the content of the archive to the archive folder and compresses some folders into .zip files.
As part of the archive, the modeler must create a metadata file describing the archive. The program has a built-in metadata editor and provides links to websites that can aid in creation of the metadata. The built-in metadata editor is also available as a stand-alone program named FgdcMetaEditor version 1.0, which also is described in this report. ModelArchiver updates the metadata file provided by the user with descriptions of the files in the archive. An optional archive list file generated automatically by ModelMuse can streamline the creation of archives by identifying input files, output files, model programs, and ancillary files for inclusion in the archive.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171149","usgsCitation":"Winston, R.B., 2018, ModelArchiver—A program for facilitating the creation of groundwater model archives: U.S. Geological Survey Open-File Report 2017–1149, 15 p., https://doi.org/10.3133/ofr20171149.","productDescription":"Report: vi, 15 p.; Application Site","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088876","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":437993,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73X85M1","text":"USGS data release","linkHelpText":"Software release: ModelArchiver and FgdcMetaEditor"},{"id":350293,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1149/coverthb.jpg"},{"id":350294,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1149/ofr20171149.pdf","text":"Report","size":"1.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1149"},{"id":350295,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://doi.org/10.5066/F73X85M1","text":"ModelArchiver and FgdcMetaEditor","linkFileType":{"id":5,"text":"html"}}],"contact":"Director, U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
In a study conducted by the U.S. Geological Survey, in cooperation with the Pennsylvania Department of Transportation, freshwater mussels were salvaged and relocated from the anticipated zone of impact for the Pond Eddy Bridge construction project in New York and Pennsylvania. Five 25-meter (m) by 25-m cells along the Pennsylvania bank of the Delaware River were sampled in three generally straight-line passes by four surveyors wearing snorkel gear for a total of 180 survey minutes per cell. All mussels encountered were collected and identified to species. A subset of individuals was marked with shellfish tags, weighed, and measured prior to relocation upstream from the zone of impact. A total of 3,434 mussels, including 3,393 Elliptio complanata (eastern elliptio mussels), 39 Anodonta implicata (alewife floaters), 1 Strophitus undulatus (creeper), and 1 Pyganodon cataracta (eastern floater), were salvaged and relocated. All non-eastern elliptio species were georeferenced using a high-resolution global positioning system unit; a subset of tagged eastern elliptio was placed in transects between georeferenced points. These mussels will be monitored to assess the effects of translocation on mortality and body condition at 1 month, 1 year, and 2 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181009","collaboration":"Prepared in cooperation with the Pennsylvania Department of Transportation","usgsCitation":"Galbraith, H.S., Blakeslee, C.J., and Cole, J.C., 2018, Freshwater mussel salvage and relocation at the Pond Eddy Bridge, Delaware River, New York and Pennsylvania: U.S. Geological Survey Open-File Report 2018–1009, 5 p., https://doi.org/10.3133/ofr20181009.","productDescription":"iii, 5 p.","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-078737","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":352158,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1009/ofr20181009.pdf","text":"Report","size":"1.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1009"},{"id":352157,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1009/coverthb2.jpg"}],"country":"United States","state":"New York, Pennsylvania","otherGeospatial":"Delaware River, Pond Eddy Bridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.8264217376709,\n 41.43381464755217\n ],\n [\n -74.81114387512207,\n 41.43381464755217\n ],\n [\n -74.81114387512207,\n 41.44513909047355\n ],\n [\n -74.8264217376709,\n 41.44513909047355\n ],\n [\n -74.8264217376709,\n 41.43381464755217\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Predation by nonnative fishes is one factor that has been implicated in the decline of juvenile salmonids in the Pacific Northwest. Impoundment of much of the Snake and Columbia Rivers has altered food webs and created habitat favorable for species such as smallmouth bass Micropterus dolomieu. Smallmouth bass are common throughout the Columbia River basin and have become the most abundant predator in lower Snake River reservoirs (Zimmerman and Parker 1995). This is a concern for Snake River fall Chinook salmon Oncorhynchus tshawytscha (hereafter, subyearlings) that may be particularly vulnerable due to their relatively small size and because their main-stem rearing habitats often overlap or are in close proximity to habitats used by smallmouth bass (Curet 1993; Tabor et al. 1993).
Concern over juvenile salmon predation spawned a number of large-scale studies to quantify its effect in the late 1980s, 1990s, and early 2000s (Poe et al. 1991; Rieman et al. 1991; Vigg et al. 1991; Fritts and Pearsons 2004; Naughton et al. 2004). Smallmouth bass predation represented 9% of total salmon consumption by predatory fishes in John Day Reservoir, Columbia River, from 1983 through 1986 (Rieman et al. 1991). In transitional habitat between the Hanford Reach of the Columbia River and McNary Reservoir, juvenile salmon (presumably subyearlings) were found in 65% of smallmouth bass (>200 mm) stomachs and comprised 59% of the diet by weight (Tabor et al. 1993). Within Lower Granite Reservoir on the Snake River, Naughton et al. (2004) showed that monthly consumption (based on weight) ranged from 5% in the upper reaches of the reservoir to 11% in the forebay. However, studies in the Snake River were conducted soon after Endangered Species Act (ESA) listing of Snake River fall Chinook salmon (NMFS 1992). During this time, fall Chinook salmon abundance was at an historic low, which may explain why consumption rates were relatively low compared to those from studies conducted in the Columbia and Yakima Rivers where abundance was higher (e.g., Tabor et al. 1993; Fritts and Pearsons 2004).
We speculate that predation on subyearlings by smallmouth bass in the Snake River may have increased in recent years for several reasons. Since their ESA listing, recovery measures implemented for Snake River fall Chinook salmon have resulted in a large increase in the juvenile population (Connor et al. 2013). Considering that subyearlings probably now make up a larger portion of the forage fish population, it is plausible they should make up a larger portion of smallmouth bass diets. Second, migrating subyearlings delay downstream movement in the transition zones of the Clearwater River and Snake River for varying lengths of time (Tiffan et al. 2010), which increases their exposure and vulnerability to predators. Spatial overlap in locations of smallmouth bass and subyearlings that died during migration provides support for this (Tiffan et al. 2010). Finally, the later outmigration of subyearlings from the Clearwater River results in their presence in Lower Granite Reservoir during the warmest summer months when predation rates of smallmouth bass should be highest.
In 2016 and 2017, we focused our smallmouth bass predation efforts in Lower Granite Reservoir downstream of the transition zones and the confluence area where we worked during 2012–2015. This report primarily covers results from 2017 but some results from 2016 are also included for comparison. Similar to past years, our first objective was to quantify smallmouth bass consumption rates of subyearlings, determine bass abundance, and describe bass diets. In addition, Tiffan et al. (2016a) posited that predation risk to subyearlings may be higher in shoreline habitats that are more suitable for smallmouth bass and lower in shoreline habitats that are more suitable for subyearlings. To test this hypothesis, our second objective was to examine the relationship between smallmouth bass predation and subyearling habitat suitability. Our final objective was to combine estimates of consumption with smallmouth bass abundance to derive estimates of total Chinook salmon losses to smallmouth bass for 2016 and 2017.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Snake River fall Chinook salmon life history investigations","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Erhardt, J.M., Tiffan, K.F., Hemingway, R.J., Bickford, B.K., and Rhodes, T., 2018, Smallmouth bass predation on subyearling fall Chinook salmon in Lower Granite Reservoir, 2016–2017, 28 p.","productDescription":"28 p.","startPage":"1","endPage":"28","ipdsId":"IP-097293","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":361382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":361363,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/DocumentViewer/P159940/75127-1.pdf"}],"country":"United States","state":"Idaho, Washington","otherGeospatial":"Lower Granite Reservoir, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.4383544921875,\n 46.33839522276402\n ],\n [\n -116.94122314453125,\n 46.33839522276402\n ],\n [\n -116.94122314453125,\n 46.68901548485151\n ],\n [\n -117.4383544921875,\n 46.68901548485151\n ],\n [\n -117.4383544921875,\n 46.33839522276402\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Erhardt, John M. 0000-0002-5170-285X jerhardt@usgs.gov","orcid":"https://orcid.org/0000-0002-5170-285X","contributorId":5380,"corporation":false,"usgs":true,"family":"Erhardt","given":"John","email":"jerhardt@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":757602,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":757604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hemingway, Rulon J. 0000-0001-8143-0325 rhemingway@usgs.gov","orcid":"https://orcid.org/0000-0001-8143-0325","contributorId":194697,"corporation":false,"usgs":true,"family":"Hemingway","given":"Rulon","email":"rhemingway@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":757603,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bickford, Brad K. 0000-0003-3756-6588 bbickford@usgs.gov","orcid":"https://orcid.org/0000-0003-3756-6588","contributorId":140889,"corporation":false,"usgs":true,"family":"Bickford","given":"Brad","email":"bbickford@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":757605,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rhodes, Tobyn N. 0000-0002-4023-4827","orcid":"https://orcid.org/0000-0002-4023-4827","contributorId":210057,"corporation":false,"usgs":true,"family":"Rhodes","given":"Tobyn N.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":757606,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70199063,"text":"70199063 - 2018 - Challenges of forecasting flooding on coral reef–lined coasts","interactions":[],"lastModifiedDate":"2018-08-30T11:01:18","indexId":"70199063","displayToPublicDate":"2018-03-01T11:01:09","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3879,"text":"Eos, Earth and Space Science News","active":true,"publicationSubtype":{"id":10}},"title":"Challenges of forecasting flooding on coral reef–lined coasts","docAbstract":"Understanding wave-driven coastal flooding is a challenging scientific problem; the need for forecasts is becoming more urgent because of sea level rise, climate change, and ever-growing coastal populations. The tools developed for sandy shorelines are generally not applicable to coral reef–lined coasts with their complex bathymetry, hydrodynamically rough reef platforms, steep and poorly sorted beaches, and low coastal elevations. Advances in understanding and predicting flooding on coral reef–lined coasts thus require concerted efforts from a number of disciplines, including climatology, oceanography, geology, and ecology.
","language":"English","publisher":"EOS","doi":"10.1029/2018EO098517","usgsCitation":"Storlazzi, C., 2018, Challenges of forecasting flooding on coral reef–lined coasts: Eos, Earth and Space Science News, v. 99, Online Article, https://doi.org/10.1029/2018EO098517.","productDescription":"Online Article","ipdsId":"IP-095824","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468944,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018eo098517","text":"Publisher Index Page"},{"id":356952,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a2eae4b0702d0e843010","contributors":{"authors":[{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":2333,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt D.","email":"cstorlazzi@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":743890,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70192456,"text":"ofr20171138 - 2018 - Chronic wasting disease—Status, science, and management support by the U.S. Geological Survey","interactions":[],"lastModifiedDate":"2019-03-26T15:04:41","indexId":"ofr20171138","displayToPublicDate":"2018-03-01T11:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1138","title":"Chronic wasting disease—Status, science, and management support by the U.S. Geological Survey","docAbstract":"The U.S. Geological Survey (USGS) investigates chronic wasting disease (CWD) at multiple science centers and cooperative research units across the Nation and supports the management of CWD through science-based strategies. CWD research conducted by USGS scientists has three strategies: (1) to understand the biology, ecology, and causes and distribution of CWD; (2) to assess and predict the spread and persistence of CWD in wildlife and the environment; and (3) to develop tools for early detection, diagnosis, surveillance, and control of CWD.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171138","usgsCitation":"Carlson, C.M., Hopkins, M.C., Nguyen, N.T., Richards, B.J., Walsh, D.P., and Walter, W.D., 2018, Chronic wasting disease—Status, science, and management support by the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2017–1138, 8 p., https://doi.org/10.3133/ofr20171138.","productDescription":"8 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-086268","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":352085,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1138/coverthb.jpg"},{"id":352086,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1138/ofr20171138.pdf","text":"Report","size":"3.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1138"}],"contact":"Director, National Wildlife Health Center
U.S. Geological Survey
6006 Schroeder Road
Madison, WI 53711
The 2017 Discovering the Deep expedition provided the first glimpse of the deep-sea geology and ecology of the deepwater regions of Swains Island, the Howland and Baker Islands Unit of PRIMNM, Phoenix Islands Protected Areas (PIPA), and the Tokelau Region (Figure 1). Prior to this expedition, virtually no visual reconnaissance had been conducted in any of these areas below scuba diving depths.
ROV dives during this expedition focused on deep-sea corals, sponges, and fish assemblages, with particular interest in locating high-density and high-diversity biological communities. Indeed, nine high-density biological communities were documented out of the 19 dive sites explored. Many of these observations were new records for these regions, and several likely yielded species new to science. Acoustic mapping operations covered more than 47,000 km2 of seafloor. The collected imagery and specimens will improve our understanding of the distribution of deep-sea corals and sponges, the ages of the seafloor features, and overall geological context of these different environments.
","language":"English","publisher":"The Oceanography Society","doi":"10.5670/oceanog.2018.supplement.01","usgsCitation":"Demopoulos, A.W., Auscavitch, S., Sowers, D., Pawlenko, N., and Kennedy, B.R., 2018, Discovering the deep: Exploring remote Pacific marine protected areas: Oceanography, v. 31, no. 1 Supplement, p. 76-77, https://doi.org/10.5670/oceanog.2018.supplement.01.","productDescription":"2 p.","startPage":"76","endPage":"77","ipdsId":"IP-091987","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468945,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5670/oceanog.2018.supplement.01","text":"Publisher Index Page"},{"id":361873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"1 Supplement","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Demopoulos, Amanda W.J. 0000-0003-2096-4694 ademopoulos@usgs.gov","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":145681,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","email":"ademopoulos@usgs.gov","middleInitial":"W.J.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":758992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Auscavitch, Steven","contributorId":168299,"corporation":false,"usgs":false,"family":"Auscavitch","given":"Steven","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":758994,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sowers, Derek","contributorId":214036,"corporation":false,"usgs":false,"family":"Sowers","given":"Derek","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":758995,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pawlenko, Nikolai","contributorId":214037,"corporation":false,"usgs":false,"family":"Pawlenko","given":"Nikolai","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":758996,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kennedy, Brian R. C.","contributorId":149603,"corporation":false,"usgs":false,"family":"Kennedy","given":"Brian","email":"","middleInitial":"R. C.","affiliations":[{"id":12641,"text":"NOAA NMFS","active":true,"usgs":false}],"preferred":false,"id":758993,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70198339,"text":"70198339 - 2018 - Individual species–area relationships in temperate coniferous forests","interactions":[],"lastModifiedDate":"2018-07-31T08:55:23","indexId":"70198339","displayToPublicDate":"2018-03-01T08:55:16","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2490,"text":"Journal of Vegetation Science","active":true,"publicationSubtype":{"id":10}},"title":"Individual species–area relationships in temperate coniferous forests","docAbstract":"Questions
What drives individual species–area relationships in temperate coniferous forests?
Location
Two 25.6‐ha forest plots on the Pacific Slope of North America, one in California, and one in Washington State.
Methods
We mapped all trees ≥1 cm in diameter and examined tree species diversity of their local neighbourhoods by calculating the individual species–area relationship for each species and for each of three diameter classes (saplings, mature trees and large‐diameter trees).
Results
In the California plot, small trees in four of the five major species occurred in neighbourhoods with higher levels of diversity than would be expected at random. In the Washington plot, small trees for four of five abundant species had neighbourhoods with lower than expected diversity at distances ≤5 m for small trees. However, at distances >5 m, all five species showed higher than expected diversity in their neighbourhoods. Larger trees at both plots tended to occur in neighbourhoods with lower than expected diversity, and no large‐diameter focal species had neighbourhoods with higher than expected diversity.
Conclusion
Diversity and co‐existence in temperate conifer‐dominated forests do not appear to be the result of random processes. Competitive interactions appear to dominate for the largest trees of most species, resulting in neighbourhoods with lower diversity. For smaller trees, we suggest that a positive response to environmental heterogeneity is the likely driver of neighbourhoods with higher than expected diversity, although we cannot rule out the possibility that facilitation or conspecific negative density dependence (CNDD) also play a role.
The U.S. Geological Survey tested the utility of imaging spectroscopy (also referred to as hyperspectral remote sensing) as an aid to regional mineral exploration efforts in remote parts of Alaska. Airborne imaging spectrometer data were collected in 2014 over unmined porphyry Cu deposits in the eastern Alaska Range using the HyMap™ sensor. Maps of the distributions of predominant minerals, made by matching reflectance signatures in the remotely sensed data to reference spectra in the shortwave infrared region, do not uniquely discriminate individual rock units. However, they do highlight hydrothermal alteration associated with porphyry deposits and prospects hosted mostly within the Nabesna pluton. In and around porphyry Cu deposits at Orange Hill and Bond Creek, unique spectral signatures are related to variations in chlorite and white mica abundance and their chemical composition. This is best revealed in the longer-wavelength 2,200-nm Al-OH absorption feature positions in pixels spectrally dominated by white mica proximal to porphyry deposits. Similar spectral signatures of chlorite and white mica wavelength positions were also recognized away from the porphyry deposits; follow-up sampling identified these satellite areas to also contain Cu-Mo-Au mineralized rock. Our study confirms that airborne imaging spectroscopy has application for regional mineral exploration in exposed mountainous terrain in Alaska.
Switchgrass (Panicum virgatum) intercropping is a novel forest management practice for biomass production intended to generate cellulosic feedstocks within intensively managed loblolly pine‐dominated landscapes. These pine plantations are important for early‐successional bird species, as short rotation times continually maintain early‐successional habitat. We tested the efficacy of using community models compared to individual surrogate species models in understanding influences on nest survival. We analysed nest data to test for differences in habitat use for 14 bird species in plots managed for switchgrass intercropping and controls within loblolly pine (Pinus taeda) plantations in Mississippi, USA.
We adapted hierarchical models using hyper‐parameters to incorporate information from both common and rare species to understand community‐level nest survival. This approach incorporates rare species that are often discarded due to low sample sizes, but can inform community‐level demographic parameter estimates. We illustrate use of this approach in generating both species‐level and community‐wide estimates of daily survival rates for songbird nests. We were able to include rare species with low sample size (minimum n = 5) to inform a hyper‐prior, allowing us to estimate effects of covariates on daily survival at the community level, then compare this with a single‐species approach using surrogate species. Using single‐species models, we were unable to generate estimates below a sample size of 21 nests per species.
Community model species‐level survival and parameter estimates were similar to those generated by five single‐species models, with improved precision in community model parameters.
Covariates of nest placement indicated that switchgrass at the nest site (<4 m) reduced daily nest survival, although intercropping at the forest stand level increased daily nest survival.
Synthesis and applications. Community models represent a viable method for estimating community nest survival rates and effects of covariates while incorporating limited data for rarely detected species. Intercropping switchgrass in loblolly pine plantations slightly increased daily nest survival at the research plot scale (0.1 km2), although at a local scale (50 m2) switchgrass negatively influenced nest survival. A likely explanation is intercropping shifted community composition, favouring species with greater disturbance tolerance.
The relative roles of science and human values can be difficult to distinguish when informal processes are used to make complex and contentious decisions in wildlife management. Structured Decision Making (SDM) offers a formal process for making such decisions, where scientific results and concepts can be disentangled from the values of differing stakeholders. We used SDM to formally integrate science and human values for a citizen working group of ungulate hunting advocates, lion hunting advocates, and outfitters convened to address the contentious allocation of harvest quotas for mountain lions (Puma concolor) in west‐central Montana, USA, during 2014. A science team consisting of mountain lion biologists and population ecologists convened to support the working group. The science team used integrated population models that incorporated 4 estimates of mountain lion density to estimate population trajectories for 5 alternative harvest quotas developed by the working group. Results of the modeling predicted that effects of each harvest quota were consistent across the 4 density estimates; harvest quotas affected predicted population trajectories for 5 years after implementation but differences were not strong. Based on these results, the focus of the working group changed to differences in values among stakeholders that were the true impediment to allocating harvest quotas. By distinguishing roles of science and human values in this process, the working group was able to collaboratively recommend a compromise solution. This solution differed little from the status quo that had been the focus of debate, but the SDM process produced understanding and buy‐in among stakeholders involved, reducing disagreements, misunderstanding, and unproductive arguments founded on informal application of scientific data and concepts. Whereas investments involved in conducting SDM may be unnecessary for many decisions in wildlife management, the investment may be beneficial for complex, contentious, and multiobjective decisions that integrate science and human values.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.861","usgsCitation":"Mitchell, M.S., Cooley, H., Gude, J., Kolbe, J., Nowak, J.J., Proffitt, K.M., Sells, S.N., and Thompson, M., 2018, Distinguishing values from science in decision making: Setting harvest quotas for mountain lions in Montana: Wildlife Society Bulletin, v. 42, no. 1, p. 13-21, https://doi.org/10.1002/wsb.861.","productDescription":"9 p.","startPage":"13","endPage":"21","ipdsId":"IP-089989","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":499990,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/821cf25d1bd94a54a95a168864e646fc","text":"External Repository"},{"id":354717,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.740966796875,\n 45.433153642271385\n ],\n [\n -112.093505859375,\n 45.433153642271385\n ],\n [\n -112.093505859375,\n 47.45037978769006\n ],\n [\n -115.740966796875,\n 47.45037978769006\n ],\n [\n -115.740966796875,\n 45.433153642271385\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-14","publicationStatus":"PW","scienceBaseUri":"5b46e5ade4b060350a15d202","contributors":{"authors":[{"text":"Mitchell, Michael S. 0000-0002-0773-6905 mmitchel@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-6905","contributorId":3716,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"mmitchel@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":737199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cooley, Hilary","contributorId":205414,"corporation":false,"usgs":false,"family":"Cooley","given":"Hilary","affiliations":[],"preferred":false,"id":737227,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gude, Justin A.","contributorId":95780,"corporation":false,"usgs":true,"family":"Gude","given":"Justin A.","affiliations":[],"preferred":false,"id":737228,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kolbe, Jay","contributorId":205415,"corporation":false,"usgs":false,"family":"Kolbe","given":"Jay","email":"","affiliations":[],"preferred":false,"id":737229,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nowak, J. Joshua","contributorId":171707,"corporation":false,"usgs":false,"family":"Nowak","given":"J.","email":"","middleInitial":"Joshua","affiliations":[],"preferred":false,"id":737230,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Proffitt, Kelly M.","contributorId":106783,"corporation":false,"usgs":true,"family":"Proffitt","given":"Kelly","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":737231,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sells, Sarah N.","contributorId":171706,"corporation":false,"usgs":false,"family":"Sells","given":"Sarah","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":737232,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thompson, Mike","contributorId":205416,"corporation":false,"usgs":false,"family":"Thompson","given":"Mike","email":"","affiliations":[],"preferred":false,"id":737233,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70195432,"text":"70195432 - 2018 - Porosity of the Marcellus Shale: A contrast matching small-angle neutron scattering study","interactions":[],"lastModifiedDate":"2018-03-30T12:08:44","indexId":"70195432","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Porosity of the Marcellus Shale: A contrast matching small-angle neutron scattering study","docAbstract":"Neutron scattering techniques were used to determine the effect of mineral matter on the accessibility of water and toluene to pores in the Devonian Marcellus Shale. Three Marcellus Shale samples, representing quartz-rich, clay-rich, and carbonate-rich facies, were examined using contrast matching small-angle neutron scattering (CM-SANS) at ambient pressure and temperature. Contrast matching compositions of H2O, D2O and toluene, deuterated toluene were used to probe open and closed pores of these three shale samples. Results show that although the mean pore radius was approximately the same for all three samples, the fractal dimension of the quartz-rich sample was higher than for the clay-rich and carbonate-rich samples, indicating different pore size distributions among the samples. The number density of pores was highest in the clay-rich sample and lowest in the quartz-rich sample. Contrast matching with water and toluene mixtures shows that the accessibility of pores to water and toluene also varied among the samples. In general, water accessed approximately 70–80% of the larger pores (>80 nm radius) in all three samples. At smaller pore sizes (~5–80 nm radius), the fraction of accessible pores decreases. The lowest accessibility to both fluids is at pore throat size of ~25 nm radii with the quartz-rich sample exhibiting lower accessibility than the clay- and carbonate-rich samples. The mechanism for this behaviour is unclear, but because the mineralogy of the three samples varies, it is likely that the inaccessible pores in this size range are associated with organics and not a specific mineral within the samples. At even smaller pore sizes (~<2.5 nm radius), in all samples, the fraction of accessible pores to water increases again to approximately 70–80%. Accessibility to toluene generally follows that of water; however, in the smallest pores (~<2.5 nm radius), accessibility to toluene decreases, especially in the clay-rich sample which contains about 30% more closed pores than the quartz- and carbonate-rich samples. Results from this study show that mineralogy of producing intervals within a shale reservoir can affect accessibility of pores to water and toluene and these mineralogic differences may affect hydrocarbon storage and production and hydraulic fracturing characteristics
For more than 20 yrs, damage patterns and instrumental recordings have highlighted the influence of the local 3D geologic structure on earthquake ground motions (e.g.,
We recommend that future U.S. national seismic hazard assessment make use of all available regional information, including that in urban models. In this column, we describe the roles of and differences between the urban and national models, and discuss the obstacles to and benefits of integrating the urban models with the national model. Future progress on this issue will require further coordination and implementation efforts between the USGS and external partners.
In December–February (DJF) of 2015/16, a strong El Niño (Niño‑3.4 SST >29°C) contributed to a severe drought over southern Africa (SA; Funk et al. 2016). A 9-million ton cereal deficit resulted in 26 mil‑ lion people in need of humanitarian assistance (SADC 2016). While SA rainfall has a well-documented nega‑ tive teleconnection with Niño‑3.4 SSTs (Hoell et al. 2015, 2017; Jury et al. 1994; Lindesay 1988; Misra 2003; Nicholson and Entekhabi 1987; Nicholson and Kim 1997; Reason et al. 2000; Rocha and Simmonds 1997), the link between climate change and El Niño remains unclear (Christensen et al. 2013) due to the large natural variability of ENSO SSTs (Wittenberg 2009), uncertainties surrounding measurements and trends (Solomon and Newman 2012), intermodel differences in ENSO representation and feedbacks (Guilyardi et al. 2012; Kim et al. 2014), and difficulties associated with quantifying ENSO strength (Cai et al. 2015).
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-17-0112.1","usgsCitation":"Funk, C., Davenport, F., Harrison, L., Magadzire, T., Galu, G., Artan, G.A., Shukla, S., Korecha, D., Indeje, M., Pomposi, C., Macharia, D., Husak, G., and Dieudonne Nsadisa, F., 2018, Anthropogenic enhancement of moderate-to-strong El Niño events likely contributed to drought and poor harvests in southern Africa during 2016: Bulletin of the American Meteorological Society, v. 99, no. 1, p. S91-S94, https://doi.org/10.1175/BAMS-D-17-0112.1.","productDescription":"4 p.","startPage":"S91","endPage":"S94","ipdsId":"IP-086759","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":461003,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/bams-d-17-0112.1","text":"Publisher Index Page"},{"id":352865,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-26","publicationStatus":"PW","scienceBaseUri":"5afee713e4b0da30c1bfc0dc","contributors":{"authors":[{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":708831,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davenport, Frank","contributorId":145816,"corporation":false,"usgs":false,"family":"Davenport","given":"Frank","email":"","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":708832,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrison, Laura","contributorId":192382,"corporation":false,"usgs":false,"family":"Harrison","given":"Laura","email":"","affiliations":[],"preferred":false,"id":708833,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Magadzire, Tamuka","contributorId":145822,"corporation":false,"usgs":false,"family":"Magadzire","given":"Tamuka","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":731922,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Galu, Gideon","contributorId":97360,"corporation":false,"usgs":true,"family":"Galu","given":"Gideon","affiliations":[],"preferred":false,"id":731923,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Artan, Guleid A. 0000-0001-8409-6182 gartan@usgs.gov","orcid":"https://orcid.org/0000-0001-8409-6182","contributorId":2938,"corporation":false,"usgs":true,"family":"Artan","given":"Guleid","email":"gartan@usgs.gov","middleInitial":"A.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":731924,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":708834,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Korecha, Diriba","contributorId":195983,"corporation":false,"usgs":false,"family":"Korecha","given":"Diriba","email":"","affiliations":[],"preferred":false,"id":708835,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Indeje, Matayo","contributorId":203615,"corporation":false,"usgs":false,"family":"Indeje","given":"Matayo","email":"","affiliations":[],"preferred":false,"id":731925,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pomposi, Catherine","contributorId":195984,"corporation":false,"usgs":false,"family":"Pomposi","given":"Catherine","email":"","affiliations":[],"preferred":false,"id":708836,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Macharia, Denis","contributorId":195985,"corporation":false,"usgs":false,"family":"Macharia","given":"Denis","email":"","affiliations":[],"preferred":false,"id":708837,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Husak, Gregory","contributorId":145811,"corporation":false,"usgs":false,"family":"Husak","given":"Gregory","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":708838,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Dieudonne Nsadisa, Faka","contributorId":203616,"corporation":false,"usgs":false,"family":"Dieudonne Nsadisa","given":"Faka","email":"","affiliations":[],"preferred":false,"id":731926,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70196133,"text":"70196133 - 2018 - National Gas Hydrate Program Expedition 01 offshore India; gas hydrate systems as revealed by hydrocarbon gas geochemistry","interactions":[],"lastModifiedDate":"2018-05-04T15:12:28","indexId":"70196133","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","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":"National Gas Hydrate Program Expedition 01 offshore India; gas hydrate systems as revealed by hydrocarbon gas geochemistry","docAbstract":"The National Gas Hydrate Program Expedition 01 (NGHP-01) targeted gas hydrate accumulations offshore of the Indian Peninsula and along the Andaman convergent margin. The primary objectives of coring were to understand the geologic and geochemical controls on the accumulation of methane hydrate and their linkages to underlying petroleum systems. Four areas were investigated: 1) the Kerala-Konkan Basin in the eastern Arabian Sea, 2) the Mahanadi and 3) Krishna-Godavari Basins in the western Bay of Bengal, and 4) the Andaman forearc Basin in the Andaman Sea.
Upward flux of methane at three of the four of the sites cored during NGHP-01 is apparent from the presence of seafloor mounds, seismic evidence for upward gas migration, shallow sub-seafloor geochemical evidence of methane oxidation, and near-seafloor gas composition that resembles gas from depth.
The Kerala-Konkan Basin well contained only CO2 with no detectable hydrocarbons suggesting there is no gas hydrate system here. Gas and gas hydrate from the Krishna-Godavari Basin is mainly microbial methane with δ13C values ranging from −58.9 to −78.9‰, with small contributions from microbial ethane (−52.1‰) and CO2. Gas from the Mahanadi Basin was mainly methane with lower concentrations of C2-C5 hydrocarbons (C1/C2 ratios typically >1000) and CO2. Carbon isotopic compositions that ranged from −70.7 to −86.6‰ for methane and −62.9 to −63.7‰ for ethane are consistent with a microbial gas source; however deeper cores contained higher molecular weight hydrocarbon gases suggesting a small contribution from a thermogenic gas source. Gas composition in the Andaman Basin was mainly methane with lower concentrations of ethane to isopentane and CO2, C1/C2 ratios were mainly >1000 although deeper samples were <1000. Carbon isotopic compositions range from −65.2 to −80.7‰ for methane, −53.1 to −55.2‰ for ethane is consistent with mainly microbial gas sources, although one value recorded of −35.4‰ for propane suggests a thermogenic source. Gas hydrate accumulations in the Krishna-Godavari and Mahanadi Basins are the result of a microbially sourced gas hydrate system. The system is enhanced by the migration of microbial gas from surrounding areas through pathways including high-porosity delta sands, shale diapirism, faulting and folding of sediment due to the local processes associated with rapid sediment deposition, sediment overpressure, and the recycling of methane from a rapidly upward moving gas hydrate stability zone. The gas hydrate system in the Andaman Basin is less well constrained due to lack of exploration and occurs in a forearc basin. Each of these hydrate-bearing systems overlies and is likely supported by the presence and possible migration of gas from deeper gas-prone petroleum systems currently generating thermogenic hydrocarbons at much greater depths.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2017.11.011","usgsCitation":"Lorenson, T., and Collett, T.S., 2018, National Gas Hydrate Program Expedition 01 offshore India; gas hydrate systems as revealed by hydrocarbon gas geochemistry: Marine and Petroleum Geology, v. 92, p. 477-492, https://doi.org/10.1016/j.marpetgeo.2017.11.011.","productDescription":"16 p.","startPage":"477","endPage":"492","ipdsId":"IP-077202","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468959,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marpetgeo.2017.11.011","text":"Publisher Index Page"},{"id":353004,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","volume":"92","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee711e4b0da30c1bfc0c0","contributors":{"authors":[{"text":"Lorenson, Thomas 0000-0001-7669-2873 tlorenson@usgs.gov","orcid":"https://orcid.org/0000-0001-7669-2873","contributorId":174599,"corporation":false,"usgs":true,"family":"Lorenson","given":"Thomas","email":"tlorenson@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":731492,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":731493,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196258,"text":"70196258 - 2018 - Movers and stayers: Novel assemblages in changing environments","interactions":[],"lastModifiedDate":"2018-03-28T13:39:51","indexId":"70196258","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3653,"text":"Trends in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Movers and stayers: Novel assemblages in changing environments","docAbstract":"How species will respond to ongoing climate and other change is of increasing concern.
Most attention is given to how species move or are moved, but many species stay.
Understanding the dynamics of new species combinations is essential for successful conservation in a changing climate.
Increased attention to species movement in response to environmental change highlights the need to consider changes in species distributions and altered biological assemblages. Such changes are well known from paleoecological studies, but have accelerated with ongoing pervasive human influence. In addition to species that move, some species will stay put, leading to an array of novel interactions. Species show a variety of responses that can allow movement or persistence. Conservation and restoration actions have traditionally focused on maintaining or returning species in particular places, but increasingly also include interventions that facilitate movement. Approaches are required that incorporate the fluidity of biotic assemblages into the goals set and interventions deployed.
This report describes options for monitoring the status and population trends of the golden eagle (Aquila chrysaetos) within the Desert Renewable Energy Conservation Plan (DRECP) area of Southern California in maintaining stable or increasing population in the planning area. The report profiles the ecology of golden eagles in the region and provides a range of potential sampling options to address monitoring needs and objectives. This approach also focused on links between changes in human land-use, golden eagle nesting and foraging habitat conditions, and population dynamics. The report outlines how monitoring data from demographic, prey, and habitat studies were used to develop a predictive demographic model for golden eagles in the DRECP area. Results from the model simulations suggest increases in renewable energy development could have negative consequences for population trajectories. Results also suggest site-specific conservation actions could reduce the magnitude of negative impacts to the local population of eagles.
A monitoring framework is proposed including: (1) annual assessments of site-occupancy and reproduction by territorial pairs of golden eagles (including rates at which sites become colonized or vacated over time); (2) estimates of survival, movements, and intensity of use of landscapes by breeding and non-breeding golden eagles; (3) periodic (conducted every two to four years) assessments of nesting and foraging habitats, prey populations, and associations with land-use and management activities; and (4) updating the predictive demographic model with new information obtained on eagles and associated population stressors.
The results of this research were published in the Journal of Rapture Research, Wiens, David,Inman, Rich D., Esque, Todd C., Longshore, Kathleen M. and Nussear, Kenneth (2017). Spatial Demographic Models to Inform Conservation Planning of Golden Eagles in Renewable Energy Landscapes. 51(3):234-257.
","language":"English","publisher":"California Energy Commission","usgsCitation":"Wiens, D., Kolar, P., and Katzner, T., 2018, Golden Eagle Monitoring Plan for the Desert Renewable Energy Conservation Plan, 99 p.","productDescription":"99 p.","ipdsId":"IP-086130","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":353243,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":353224,"type":{"id":11,"text":"Document"},"url":"https://www.energy.ca.gov/2018publications/CEC-500-2018-008/CEC-500-2018-008.pdf"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee70fe4b0da30c1bfc0aa","contributors":{"authors":[{"text":"Wiens, David 0000-0002-2020-038X jwiens@usgs.gov","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":167538,"corporation":false,"usgs":true,"family":"Wiens","given":"David","email":"jwiens@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":732901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolar, Patrick 0000-0002-0076-7565 pkolar@usgs.gov","orcid":"https://orcid.org/0000-0002-0076-7565","contributorId":189512,"corporation":false,"usgs":true,"family":"Kolar","given":"Patrick","email":"pkolar@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":732902,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":732903,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195821,"text":"70195821 - 2018 - Fire and grazing influence site resistance to Bromus tectorum through their effects on shrub, bunchgrass and biocrust communities in the Great Basin (USA)","interactions":[],"lastModifiedDate":"2018-11-14T10:03:20","indexId":"70195821","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Fire and grazing influence site resistance to Bromus tectorum through their effects on shrub, bunchgrass and biocrust communities in the Great Basin (USA)","title":"Fire and grazing influence site resistance to Bromus tectorum through their effects on shrub, bunchgrass and biocrust communities in the Great Basin (USA)","docAbstract":"Shrubs, bunchgrasses and biological soil crusts (biocrusts) are believed to contribute to site resistance to plant invasions in the presence of cattle grazing. Although fire is a concomitant disturbance with grazing, little is known regarding their combined impacts on invasion resistance. We are the first to date to test the idea that biotic communities mediate the effects of disturbance on site resistance. We assessed cover of Bromus tectorum, shrubs, native bunchgrasses, lichens and mosses in 99 burned and unburned plots located on similar soils where fires occurred between 12 and 23 years before sampling. Structural equation modeling was used to test hypothesized relationships between environmental and disturbance characteristics, the biotic community and resistance to B. tectorum cover. Characteristics of fire and grazing did not directly relate to cover of B. tectorum. Relationships were mediated through shrub, bunchgrass and biocrust communities. Increased site resistance following fire was associated with higher bunchgrass cover and recovery of bunchgrasses and mosses with time since fire. Evidence of grazing was more pronounced on burned sites and was positively correlated with the cover of B. tectorum, indicating an interaction between fire and grazing that decreases site resistance. Lichen cover showed a weak, negative relationship with cover of B. tectorum. Fire reduced near-term site resistance to B. tectorum on actively grazed rangelands. Independent of fire, grazing impacts resulted in reduced site resistance to B. tectorum, suggesting that grazing management that enhances plant and biocrust communities will also enhance site resistance.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-018-0230-8","usgsCitation":"Condon, L.A., and Pyke, D.A., 2018, Fire and grazing influence site resistance to Bromus tectorum through their effects on shrub, bunchgrass and biocrust communities in the Great Basin (USA): Ecosystems, v. 21, no. 7, p. 1416-1431, https://doi.org/10.1007/s10021-018-0230-8.","productDescription":"16 p.","startPage":"1416","endPage":"1431","ipdsId":"IP-083278","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":352209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.03662109374999,\n 39.027718840211605\n ],\n [\n -111.796875,\n 39.027718840211605\n ],\n [\n -111.796875,\n 44.213709909702054\n ],\n [\n -120.03662109374999,\n 44.213709909702054\n ],\n [\n -120.03662109374999,\n 39.027718840211605\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-28","publicationStatus":"PW","scienceBaseUri":"5afee712e4b0da30c1bfc0ca","contributors":{"authors":[{"text":"Condon, Lea A. 0000-0002-9357-3881","orcid":"https://orcid.org/0000-0002-9357-3881","contributorId":202908,"corporation":false,"usgs":true,"family":"Condon","given":"Lea","email":"","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":730169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","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":730168,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}