Summer stream temperatures limit the distribution of Brook Trout Salvelinus fontinalis and are affected by riparian vegetation. We used riparian and instream habitat surveys along with stream temperature loggers placed throughout streams to determine the potential for riparian vegetation shading to increase the length of stream that is thermally suitable for Brook Trout. Twelve streams located throughout central Wisconsin were evaluated in the summers of 2007 and 2008. Across all streams, nonparametric ANCOVA modeling was used to identify spatial temperature patterns within a year for individual stream segments. Riparian tree-vegetated segments had a significantly lower mean change in stream temperature per kilometer of stream compared with grass-vegetated segments during the periods of maximum daily and weekly average temperatures, when we accounted for upstream temperature. Riparian grass-vegetated segments increased on average 1.19°C/km (SE, 0.44) during the maximum daily average temperature period and 0.93°C/km (SE, 0.39) during the maximum weekly average temperature period, whereas tree-vegetated segments decreased 0.48°C/km (SE, 0.39) and 0.30°C/km (SE, 0.25) during those respective time periods. Maximum weekly average temperatures were also modeled with different shading levels using a heat budget temperature model, U.S. Fish and Wildlife Service's Stream Segment Temperature Model. Across 11 study streams (one stream model could not be calibrated), modeled stream temperatures in equilibrium with their environmental conditions ranging from 23.2°C to 28.3°C at 0% shading could be reduced to 18.8–23.5°C with 75% shading. Modeled increases in shade up to 75% from the current average of 34% increased the length of surveyed stream thermally suitable to Brook Trout by 4.9 km on Sucker Creek. We conclude that riparian forests are important for maintaining thermal conditions suitable for Brook Trout in central Wisconsin streams and can be managed to increase the amount of stream habitat thermally suitable for Brook Trout.
","language":"English","publisher":"American Fisheries Society","doi":"10.1080/02755947.2013.785989","usgsCitation":"Cross, B.K., Bozek, M.A., and Mitro, M.G., 2013, Influences of riparian vegetation on trout stream temperatures in central Wisconsin: North American Journal of Fisheries Management, v. 33, no. 4, p. 682-692, https://doi.org/10.1080/02755947.2013.785989.","productDescription":"11 p.","startPage":"682","endPage":"692","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-035142","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":319346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.8564453125,\n 43.35713822211053\n ],\n [\n -92.8564453125,\n 45.98169518512228\n ],\n [\n -88.08837890625,\n 45.98169518512228\n ],\n [\n -88.08837890625,\n 43.35713822211053\n ],\n [\n -92.8564453125,\n 43.35713822211053\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"33","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2013-06-20","publicationStatus":"PW","scienceBaseUri":"56f50fcae4b0f59b85e1eb6b","contributors":{"authors":[{"text":"Cross, Benjamin K.","contributorId":167783,"corporation":false,"usgs":false,"family":"Cross","given":"Benjamin","email":"","middleInitial":"K.","affiliations":[{"id":24832,"text":"Washngton State University, Pullman, WA","active":true,"usgs":false}],"preferred":false,"id":623505,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bozek, Michael A.","contributorId":51030,"corporation":false,"usgs":true,"family":"Bozek","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":623504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitro, Matthew G.","contributorId":167784,"corporation":false,"usgs":false,"family":"Mitro","given":"Matthew","email":"","middleInitial":"G.","affiliations":[{"id":24833,"text":"Wisconsin DNR, Madison, WI","active":true,"usgs":false}],"preferred":false,"id":623506,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70173430,"text":"70173430 - 2013 - Advantages of geographically weighted regression for modeling benthic substrate in two Greater Yellowstone Ecosystem streams","interactions":[],"lastModifiedDate":"2016-06-20T15:35:56","indexId":"70173430","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1550,"text":"Environmental Modeling & Assessment","onlineIssn":" 1573-296","printIssn":"1420-2026","active":true,"publicationSubtype":{"id":10}},"title":"Advantages of geographically weighted regression for modeling benthic substrate in two Greater Yellowstone Ecosystem streams","docAbstract":"Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m2 cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r 2. For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r 2 values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.
","language":"English","publisher":"Springer","doi":"10.1007/s10666-012-9334-2","usgsCitation":"Sheehan, K.R., Strager, M.P., and Welsh, S., 2013, Advantages of geographically weighted regression for modeling benthic substrate in two Greater Yellowstone Ecosystem streams: Environmental Modeling & Assessment, v. 18, no. 2, p. 209-219, https://doi.org/10.1007/s10666-012-9334-2.","productDescription":"11 p.","startPage":"209","endPage":"219","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-033772","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":324038,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","volume":"18","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2012-09-05","publicationStatus":"PW","scienceBaseUri":"576913aee4b07657d19fef8a","contributors":{"authors":[{"text":"Sheehan, Kenneth R.","contributorId":146541,"corporation":false,"usgs":false,"family":"Sheehan","given":"Kenneth","email":"","middleInitial":"R.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":637122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strager, Michael P.","contributorId":169817,"corporation":false,"usgs":false,"family":"Strager","given":"Michael","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":637123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":152088,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart A.","email":"swelsh@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":637121,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178655,"text":"70178655 - 2013 - Development and application of a soil organic matter-based soil quality index in mineralized terrane of the Western US","interactions":[],"lastModifiedDate":"2017-11-21T15:03:23","indexId":"70178655","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1534,"text":"Environmental Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Development and application of a soil organic matter-based soil quality index in mineralized terrane of the Western US","docAbstract":"Soil quality indices provide a means of distilling large amounts of data into a single metric that evaluates the soil’s ability to carry out key ecosystem functions. Primarily developed in agroecosytems, then forested ecosystems, an index using the relation between soil organic matter and other key soil properties in more semi-arid systems of the Western US impacted by different geologic mineralization was developed. Three different sites in two different mineralization types, acid sulfate and Cu/Mo porphyry in California and Nevada, were studied. Soil samples were collected from undisturbed soils in both mineralized and nearby unmineralized terrane as well as waste rock and tailings. Eight different microbial parameters (carbon substrate utilization, microbial biomass-C, mineralized-C, mineralized-N and enzyme activities of acid phosphatase, alkaline phosphatase, arylsulfatase, and fluorescein diacetate) along with a number of physicochemical parameters were measured. Multiple linear regression models between these parameters and both total organic carbon and total nitrogen were developed, using the ratio of predicted to measured values as the soil quality index. In most instances, pooling unmineralized and mineralized soil data within a given study site resulted in lower model correlations. Enzyme activity was a consistent explanatory variable in the models across the study sites. Though similar indicators were significant in models across different mineralization types, pooling data across sites inhibited model differentiation of undisturbed and disturbed sites. This procedure could be used to monitor recovery of disturbed systems in mineralized terrane and help link scientific and management disciplines.
","language":"English","publisher":"Springer","doi":"10.1007/s12665-012-1876-8","usgsCitation":"Blecker, S., Stillings, L., Amacher, M., Ippolito, J., and DeCrappeo, N., 2013, Development and application of a soil organic matter-based soil quality index in mineralized terrane of the Western US: Environmental Earth Sciences, v. 68, no. 7, p. 1887-1901, https://doi.org/10.1007/s12665-012-1876-8.","productDescription":"15 p.","startPage":"1887","endPage":"1901","ipdsId":"IP-026505","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":473894,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://eprints.nwisrl.ars.usda.gov/id/eprint/1489/1/1453.pdf","text":"External Repository"},{"id":331460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"68","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2012-08-12","publicationStatus":"PW","scienceBaseUri":"58468aebe4b04fc80e5236cd","contributors":{"authors":[{"text":"Blecker, S.W.","contributorId":99671,"corporation":false,"usgs":true,"family":"Blecker","given":"S.W.","email":"","affiliations":[],"preferred":false,"id":654730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stillings, Lisa L. 0000-0002-9011-8891 stilling@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-8891","contributorId":3143,"corporation":false,"usgs":true,"family":"Stillings","given":"Lisa L.","email":"stilling@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":654726,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amacher, M.C.","contributorId":74043,"corporation":false,"usgs":true,"family":"Amacher","given":"M.C.","affiliations":[],"preferred":false,"id":654728,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ippolito, J.A.","contributorId":54890,"corporation":false,"usgs":true,"family":"Ippolito","given":"J.A.","email":"","affiliations":[],"preferred":false,"id":654727,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"DeCrappeo, N.M.","contributorId":86269,"corporation":false,"usgs":true,"family":"DeCrappeo","given":"N.M.","affiliations":[],"preferred":false,"id":654729,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70154859,"text":"70154859 - 2013 - Potential for bias in using hybrids between common carp (Cyprinus carpio) and goldfish (Carassius auratus) in endocrine studies: a first report of hybrids in Lake Mead, Nevada, U.S.A","interactions":[],"lastModifiedDate":"2015-08-18T09:37:47","indexId":"70154859","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":737,"text":"American Midland Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Potential for bias in using hybrids between common carp (Cyprinus carpio) and goldfish (Carassius auratus) in endocrine studies: a first report of hybrids in Lake Mead, Nevada, U.S.A","docAbstract":"During a 2008 study to assess endocrine and reproductive health of common carp (Cyprinus carpio) in Lake Mead, Nevada (U.S.A.) we identified two fish, one male and one female, as hybrids with goldfish (Carassius auratus) based on morphology, lateral line scale count, and lack of anterior barbels. Gross examination of the female hybrid ovaries indicated presence of vitellogenic ovarian follicles; whereas histological evaluation of the male hybrid testes showed lobule-like structures with open lumens but without germ cells, suggesting it was sterile. Because common carp/goldfish hybrids are more susceptible to gonadal tumors and may have different endocrine profiles than common carp, researchers using common carp as a model for endocrine/reproductive studies should be aware of the possible presence of hybrids.
","language":"English","publisher":"University of Notre Dame","doi":"10.1674/0003-0031-169.2.426","usgsCitation":"Goodbred, S.L., Patino, R., Orsak, E., Sharma, P., and Ruessler, S., 2013, Potential for bias in using hybrids between common carp (Cyprinus carpio) and goldfish (Carassius auratus) in endocrine studies: a first report of hybrids in Lake Mead, Nevada, U.S.A: American Midland Naturalist, v. 169, no. 2, p. 426-431, https://doi.org/10.1674/0003-0031-169.2.426.","productDescription":"6 p.","startPage":"426","endPage":"431","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-025582","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":306842,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Lake Mead","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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sgoodbred@usgs.gov","contributorId":497,"corporation":false,"usgs":true,"family":"Goodbred","given":"Steven","email":"sgoodbred@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":568379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":564286,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Orsak, Erik","contributorId":92763,"corporation":false,"usgs":true,"family":"Orsak","given":"Erik","affiliations":[],"preferred":false,"id":568380,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sharma, Prakash","contributorId":107435,"corporation":false,"usgs":true,"family":"Sharma","given":"Prakash","email":"","affiliations":[],"preferred":false,"id":568381,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ruessler, Shane druessler@usgs.gov","contributorId":4660,"corporation":false,"usgs":true,"family":"Ruessler","given":"Shane","email":"druessler@usgs.gov","affiliations":[],"preferred":true,"id":568382,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70136013,"text":"70136013 - 2013 - Population ecology of polar bears in Davis Strait, Canada and Greenland","interactions":[],"lastModifiedDate":"2014-12-22T11:24:26","indexId":"70136013","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Population ecology of polar bears in Davis Strait, Canada and Greenland","docAbstract":"Until recently, the sea ice habitat of polar bears was understood to be variable, but environmental variability was considered to be cyclic or random, rather than progressive. Harvested populations were believed to be at levels where density effects were considered not significant. However, because we now understand that polar bear demography can also be influenced by progressive change in the environment, and some populations have increased to greater densities than historically lower numbers, a broader suite of factors should be considered in demographic studies and management. We analyzed 35 years of capture and harvest data from the polar bear (Ursus maritimus) subpopulation in Davis Strait, including data from a new study (2005–2007), to quantify its current demography. We estimated the population size in 2007 to be 2,158 ± 180 (SE), a likely increase from the 1970s. We detected variation in survival, reproductive rates, and age-structure of polar bears from geographic sub-regions. Survival and reproduction of bears in southern Davis Strait was greater than in the north and tied to a concurrent dramatic increase in breeding harp seals (Pagophilus groenlandicus) in Labrador. The most supported survival models contained geographic and temporal variables. Harp seal abundance was significantly related to polar bear survival. Our estimates of declining harvest recovery rate, and increasing total survival, suggest that the rate of harvest declined over time. Low recruitment rates, average adult survival rates, and high population density, in an environment of high prey density, but deteriorating and variable ice conditions, currently characterize the Davis Strait polar bears. Low reproductive rates may reflect negative effects of greater densities or worsening ice conditions.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.489","usgsCitation":"Peacock, E.L., Taylor, M.K., Laake, J.L., and Stirling, I., 2013, Population ecology of polar bears in Davis Strait, Canada and Greenland: Journal of Wildlife Management, v. 77, no. 3, p. 463-476, https://doi.org/10.1002/jwmg.489.","productDescription":"14 p.","startPage":"463","endPage":"476","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-026628","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":296841,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Greenland","otherGeospatial":"Davis Strait","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.63671875,\n 48.22467264956519\n ],\n [\n -92.63671875,\n 73.1758971742261\n ],\n [\n -43.9453125,\n 73.1758971742261\n ],\n [\n -43.9453125,\n 48.22467264956519\n ],\n [\n -92.63671875,\n 48.22467264956519\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"77","issue":"3","noUsgsAuthors":false,"publicationDate":"2013-02-19","publicationStatus":"PW","scienceBaseUri":"54dd2c27e4b08de9379b366f","contributors":{"authors":[{"text":"Peacock, Elizabeth L. 0000-0001-7279-0329 lpeacock@usgs.gov","orcid":"https://orcid.org/0000-0001-7279-0329","contributorId":3361,"corporation":false,"usgs":true,"family":"Peacock","given":"Elizabeth","email":"lpeacock@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":false,"id":537057,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Mitchell K.","contributorId":131049,"corporation":false,"usgs":false,"family":"Taylor","given":"Mitchell","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":537062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Laake, Jeffrey L.","contributorId":83851,"corporation":false,"usgs":false,"family":"Laake","given":"Jeffrey","email":"","middleInitial":"L.","affiliations":[{"id":6578,"text":"National Marine Fisheries Service, Seattle, WA 98112, USA","active":true,"usgs":false}],"preferred":false,"id":537063,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stirling, Ian","contributorId":72079,"corporation":false,"usgs":false,"family":"Stirling","given":"Ian","email":"","affiliations":[{"id":6962,"text":"Science and Technology Branch, Environment Canada","active":true,"usgs":false}],"preferred":false,"id":537064,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193248,"text":"70193248 - 2013 - Comment on “Apatite 4He/3He and (U-Th)/He Evidence for an Ancient Grand Canyon”","interactions":[],"lastModifiedDate":"2017-11-06T14:25:59","indexId":"70193248","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Comment on “Apatite 4He/3He and (U-Th)/He Evidence for an Ancient Grand Canyon”","title":"Comment on “Apatite 4He/3He and (U-Th)/He Evidence for an Ancient Grand Canyon”","docAbstract":"Flowers and Farley (Reports, 21 December 2012, p. 1616; published online 29 November 2012) propose that the Grand Canyon is 70 million years old. Starkly contrasting models for the age of the Grand Canyon—70 versus 6 million years—can be reconciled by a shallow paleocanyon that was carved in the eastern Grand Canyon 25 to 15 million years ago (Ma), negating the proposed 70 Ma and 55 Ma paleocanyons. Cooling models and geologic data are most consistent with a 5 to 6 Ma age for western Grand Canyon and Marble Canyon.
","language":"English","publisher":"Science","doi":"10.1126/science.1233982","usgsCitation":"Karlstrom, K.E., Lee, J.P., Kelley, S.A., Crow, R.S., Young, R.A., Lucchitta, I., Beard, L.S., Dorsey, R., Ricketts, J., Dickinson, W.R., and Crossey, L., 2013, Comment on “Apatite 4He/3He and (U-Th)/He Evidence for an Ancient Grand Canyon”: Science, v. 340, no. 6129, p. 143-143, https://doi.org/10.1126/science.1233982.","productDescription":"Article 143; 3 p.","startPage":"143","endPage":"143","ipdsId":"IP-044130","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":348295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"340","issue":"6129","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a07ef2ce4b09af898c8cd81","contributors":{"authors":[{"text":"Karlstrom, Karl E.","contributorId":75597,"corporation":false,"usgs":true,"family":"Karlstrom","given":"Karl","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":720734,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, John P. jplee@usgs.gov","contributorId":3291,"corporation":false,"usgs":true,"family":"Lee","given":"John","email":"jplee@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":718362,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, Shari A.","contributorId":25606,"corporation":false,"usgs":true,"family":"Kelley","given":"Shari","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":720735,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crow, Ryan S. 0000-0002-2403-6361 rcrow@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-6361","contributorId":5792,"corporation":false,"usgs":true,"family":"Crow","given":"Ryan","email":"rcrow@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":720736,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, Richard A.","contributorId":38975,"corporation":false,"usgs":true,"family":"Young","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":720737,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lucchitta, Ivo","contributorId":94291,"corporation":false,"usgs":true,"family":"Lucchitta","given":"Ivo","email":"","affiliations":[],"preferred":false,"id":720738,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Beard, L. Sue 0000-0001-9552-1893 sbeard@usgs.gov","orcid":"https://orcid.org/0000-0001-9552-1893","contributorId":152,"corporation":false,"usgs":true,"family":"Beard","given":"L.","email":"sbeard@usgs.gov","middleInitial":"Sue","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":720739,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dorsey, Rebecca","contributorId":140302,"corporation":false,"usgs":false,"family":"Dorsey","given":"Rebecca","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":720740,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ricketts, Jason","contributorId":60362,"corporation":false,"usgs":true,"family":"Ricketts","given":"Jason","email":"","affiliations":[],"preferred":false,"id":720741,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dickinson, William R.","contributorId":75064,"corporation":false,"usgs":true,"family":"Dickinson","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":720742,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Crossey, Laura","contributorId":24485,"corporation":false,"usgs":true,"family":"Crossey","given":"Laura","affiliations":[],"preferred":false,"id":720743,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70193600,"text":"70193600 - 2013 - Contrasting patterns of vesiculation in low, intermediate, and high Hawaiian fountains: A case study of the 1969 Mauna Ulu eruption","interactions":[],"lastModifiedDate":"2017-11-03T18:31:45","indexId":"70193600","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Contrasting patterns of vesiculation in low, intermediate, and high Hawaiian fountains: A case study of the 1969 Mauna Ulu eruption","docAbstract":"Hawaiian-style eruptions, or Hawaiian fountains, typically occur at basaltic volcanoes and are sustained, weakly explosive jets of gas and dominantly coarse, juvenile ejecta (dense spatter to delicate reticulite). Almost the entire range of styles and mass eruption rates within Hawaiian fountaining occurred during twelve fountaining episodes recorded at Mauna Ulu, Kīlauea between May and December 1969. Such diversity in intensity and style is controlled during magma ascent by many processes that can be constrained by the size and shape of vesicles in the 1969 pyroclasts. This paper describes pyroclast vesicularity from high, intermediate, and low fountaining episodes with eruption rates from 0.05 to 1.3 × 106 m3 h− 1. As each eruptive episode progressed, magma ascent slowed in and around the vent system, offering extended time for bubbles to grow and coalesce. Late ejected pyroclasts are thus characterized by populations of fewer and larger vesicles with relaxed shapes. This progression continued in the intervals between episodes after termination of fountain activity. The time scale for this process of shallow growth, coalescence and relaxation of bubbles is typically tens of hours. Rims and cores of pumiceous pyroclasts from moderate to high fountaining episodes record a second post-fragmentation form of vesicle maturation. Partially thermally insulated pyroclasts can have internal bubble populations evolve more dynamically with continued growth and coalescence, on a time scale of only minutes, during transport in the fountains. Reticulite, which formed in a short-lived fountain 540 m in height, underwent late, short-lived bubble nucleation followed by rapid growth of a uniform bubble population in a thermally insulated fountain, and quenched at the onset of permeability before significant coalescence. These contrasting patterns of shallow degassing and outgassing were the dominant controls in determining both the form and duration of fountaining episodes at Mauna Ulu, and probably for many other Hawaiian-style eruptions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2013.01.016","usgsCitation":"Parcheta, C.E., Houghton, B.F., and Swanson, D., 2013, Contrasting patterns of vesiculation in low, intermediate, and high Hawaiian fountains: A case study of the 1969 Mauna Ulu eruption: Journal of Volcanology and Geothermal Research, v. 255, p. 79-89, https://doi.org/10.1016/j.jvolgeores.2013.01.016.","productDescription":"11 p.","startPage":"79","endPage":"89","ipdsId":"IP-044007","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":348096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Ulu","volume":"255","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fc2eade4b0531197b27fdc","contributors":{"authors":[{"text":"Parcheta, Carolyn E. cparcheta@usgs.gov","contributorId":5316,"corporation":false,"usgs":true,"family":"Parcheta","given":"Carolyn","email":"cparcheta@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":true,"id":719776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houghton, Bruce F. 0000-0002-7532-9770","orcid":"https://orcid.org/0000-0002-7532-9770","contributorId":140077,"corporation":false,"usgs":false,"family":"Houghton","given":"Bruce","email":"","middleInitial":"F.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false},{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":719777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swanson, Donald A. 0000-0002-1680-3591 donswan@usgs.gov","orcid":"https://orcid.org/0000-0002-1680-3591","contributorId":3137,"corporation":false,"usgs":true,"family":"Swanson","given":"Donald A.","email":"donswan@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":false,"id":719778,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193603,"text":"70193603 - 2013 - Pre-eruption deformation caused by dike intrusion beneath Kizimen volcano, Kamchatka, Russia, observed by InSAR","interactions":[],"lastModifiedDate":"2017-11-02T16:02:41","indexId":"70193603","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Pre-eruption deformation caused by dike intrusion beneath Kizimen volcano, Kamchatka, Russia, observed by InSAR","docAbstract":"Interferometric synthetic aperture radar (InSAR) images reveal a pre-eruption deformation signal at Kizimen volcano, Kamchatka, Russia, where an ongoing eruption began in mid-November, 2010. The previous eruption of this basaltic andesite-to-dacite stratovolcano occurred in 1927–1928. InSAR images from both ascending and descending orbital passes of Envisat and ALOS PALSAR satellites show as much as 6 cm of line-of-sight shortening from September 2008 to September 2010 in a broad area centered at Kizimen. About 20 cm of opening of a nearly vertical dike provides an adequate fit to the surface deformation pattern. The model dike is approximately 14 km long, 10 km high, centered 13 km beneath Kizimen, and strikes NE–SW. Time-series analysis of multi-temporal interferograms indicates that (1) intrusion started sometime between late 2008 and July 2009, (2) continued at a nearly constant rate, and (3) resulted in a volume expansion of 3.2 × 107 m3 by September 2010, i.e., about two months before the onset of the 2010 eruption. Earthquakes located above the tip of the dike accompanied the intrusion. Eventually, magma pressure in the dike exceeded the confining strength of the host rock, triggering the 2010 eruption. Our results provide insight into the intrusion process that preceded an explosive eruption at a Pacific Rim stratovolcano following nearly a century of quiescence, and therefore have implications for monitoring and hazards assessment at similar volcanoes elsewhere.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2013.02.011","usgsCitation":"Ji, L., Lu, Z., Dzurisin, D., and Senyukov, S., 2013, Pre-eruption deformation caused by dike intrusion beneath Kizimen volcano, Kamchatka, Russia, observed by InSAR: Journal of Volcanology and Geothermal Research, v. 256, p. 87-95, https://doi.org/10.1016/j.jvolgeores.2013.02.011.","productDescription":"9 p.","startPage":"87","endPage":"95","ipdsId":"IP-044765","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":348133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia","otherGeospatial":"Kamchatka, Kizimen Volcano","volume":"256","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59fc2eade4b0531197b27fd9","contributors":{"authors":[{"text":"Ji, Lingyun","contributorId":199609,"corporation":false,"usgs":false,"family":"Ji","given":"Lingyun","email":"","affiliations":[],"preferred":false,"id":719573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Zhong 0000-0001-9181-1818 lu@usgs.gov","orcid":"https://orcid.org/0000-0001-9181-1818","contributorId":901,"corporation":false,"usgs":true,"family":"Lu","given":"Zhong","email":"lu@usgs.gov","affiliations":[],"preferred":true,"id":719572,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dzurisin, Daniel 0000-0002-0138-5067 dzurisin@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-5067","contributorId":538,"corporation":false,"usgs":true,"family":"Dzurisin","given":"Daniel","email":"dzurisin@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":719571,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Senyukov, Sergey","contributorId":199610,"corporation":false,"usgs":false,"family":"Senyukov","given":"Sergey","email":"","affiliations":[],"preferred":false,"id":719574,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70043312,"text":"70043312 - 2013 - The feasibility of producing adequate feedstock for year–round cellulosic ethanol production in an intensive agricultural fuelshed","interactions":[],"lastModifiedDate":"2018-01-26T17:12:36","indexId":"70043312","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":993,"text":"BioEnergy Research","active":true,"publicationSubtype":{"id":10}},"title":"The feasibility of producing adequate feedstock for year–round cellulosic ethanol production in an intensive agricultural fuelshed","docAbstract":"To date, cellulosic ethanol production has not been commercialized in the United States. However, government mandates aimed at increasing second-generation biofuel production could spur exploratory development in the cellulosic ethanol industry. We conducted an in-depth analysis of the fuelshed surrounding a starch-based ethanol plant near York, Nebraska that has the potential for cellulosic ethanol production. To assess the feasibility of supplying adequate biomass for year-round cellulosic ethanol production from residual maize (Zea mays) stover and bioenergy switchgrass (Panicum virgatum) within a 40-km road network service area of the existing ethanol plant, we identified ∼14,000 ha of marginally productive cropland within the service area suitable for conversion from annual rowcrops to switchgrass and ∼132,000 ha of maize-enrolled cropland from which maize stover could be collected. Annual maize stover and switchgrass biomass supplies within the 40-km service area could range between 429,000 and 752,000 metric tons (mT). Approximately 140–250 million liters (l) of cellulosic ethanol could be produced, rivaling the current 208 million l annual starch-based ethanol production capacity of the plant. We conclude that sufficient quantities of biomass could be produced from maize stover and switchgrass near the plant to support year-round cellulosic ethanol production at current feedstock yields, sustainable removal rates and bioconversion efficiencies. Modifying existing starch-based ethanol plants in intensive agricultural fuelsheds could increase ethanol output, return marginally productive cropland to perennial vegetation, and remove maize stover from productive cropland to meet feedstock demand.","language":"English","publisher":"Springer","doi":"10.1007/s12155-013-9311-x","usgsCitation":"Uden, D.R., Mitchell, R.B., Allen, C.R., Guan, Q., and McCoy, T.D., 2013, The feasibility of producing adequate feedstock for year–round cellulosic ethanol production in an intensive agricultural fuelshed: BioEnergy Research, v. 6, no. 3, p. 930-938, https://doi.org/10.1007/s12155-013-9311-x.","productDescription":"9 p.","startPage":"930","endPage":"938","ipdsId":"IP-043680","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":271038,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":271037,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s12155-013-9311-x"}],"country":"United States","volume":"6","issue":"3","noUsgsAuthors":false,"publicationDate":"2013-03-22","publicationStatus":"PW","scienceBaseUri":"516fc468e4b05024ef3cd420","contributors":{"authors":[{"text":"Uden, Daniel R.","contributorId":74258,"corporation":false,"usgs":true,"family":"Uden","given":"Daniel","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":473362,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mitchell, Rob B.","contributorId":100715,"corporation":false,"usgs":true,"family":"Mitchell","given":"Rob","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":473365,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":473361,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guan, Qingfeng","contributorId":85067,"corporation":false,"usgs":true,"family":"Guan","given":"Qingfeng","email":"","affiliations":[],"preferred":false,"id":473363,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCoy, Tim D.","contributorId":86669,"corporation":false,"usgs":true,"family":"McCoy","given":"Tim","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":473364,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70045252,"text":"70045252 - 2013 - Variability of displacement at a point: Implications for earthquake‐size distribution and rupture hazard on faults","interactions":[],"lastModifiedDate":"2021-05-21T17:13:11.854001","indexId":"70045252","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Variability of displacement at a point: Implications for earthquake‐size distribution and rupture hazard on faults","docAbstract":"To investigate the nature of earthquake‐magnitude distributions on faults, we compare the interevent variability of surface displacement at a point on a fault from a composite global data set of paleoseismic observations with the variability expected from two prevailing magnitude–frequency distributions: the truncated‐exponential model and the characteristic‐earthquake model. We use forward modeling to predict the coefficient of variation (CV) for the alternative earthquake distributions, incorporating factors that would effect observations of displacement at a site. The characteristic‐earthquake model (with a characteristic‐magnitude range of ±0.25) produces CV values consistent with the data (CV∼0.5) only if the variability for a given earthquake magnitude is small. This condition implies that rupture patterns on a fault are stable, in keeping with the concept behind the model. This constraint also bears upon fault‐rupture hazard analysis, which, for lack of point‐specific information, has used global scaling relations to infer variability in average displacement for a given‐size earthquake. Exponential distributions of earthquakes (from M 5 to the maximum magnitude) give rise to CV values that are significantly larger than the empirical constraint. A version of the model truncated at M 7, however, yields values consistent with a larger CV (∼0.6) determined for small‐displacement sites. Although this result allows for a difference in the magnitude distribution of smaller surface‐rupturing earthquakes, it may reflect, in part, less stability in the displacement profile of smaller ruptures and/or the tails of larger ruptures.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120120159","usgsCitation":"Hecker, S., Abrahamson, N., and Wooddell, K., 2013, Variability of displacement at a point: Implications for earthquake‐size distribution and rupture hazard on faults: Bulletin of the Seismological Society of America, v. 103, no. 2A, p. 651-674, https://doi.org/10.1785/0120120159.","productDescription":"24 p.","startPage":"651","endPage":"674","ipdsId":"IP-037964","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":272873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"2A","noUsgsAuthors":false,"publicationDate":"2013-03-21","publicationStatus":"PW","scienceBaseUri":"51a5d1f1e4b0605bc571f02d","contributors":{"authors":[{"text":"Hecker, Suzanne 0000-0002-5054-372X shecker@usgs.gov","orcid":"https://orcid.org/0000-0002-5054-372X","contributorId":3553,"corporation":false,"usgs":true,"family":"Hecker","given":"Suzanne","email":"shecker@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":477140,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abrahamson, N. A.","contributorId":27152,"corporation":false,"usgs":false,"family":"Abrahamson","given":"N. A.","affiliations":[],"preferred":false,"id":477141,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wooddell, Kathryn","contributorId":47674,"corporation":false,"usgs":false,"family":"Wooddell","given":"Kathryn","email":"","affiliations":[{"id":13174,"text":"Pacific Gas & Electric","active":true,"usgs":false}],"preferred":false,"id":477142,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70044643,"text":"70044643 - 2013 - The effects of increased stream temperatures on juvenile steelhead growth in the Yakima River Basin based on projected climate change scenarios","interactions":[],"lastModifiedDate":"2016-04-26T10:00:54","indexId":"70044643","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1246,"text":"Climate Change","onlineIssn":"1573-1480","printIssn":"0165-0009","active":true,"publicationSubtype":{"id":10}},"title":"The effects of increased stream temperatures on juvenile steelhead growth in the Yakima River Basin based on projected climate change scenarios","docAbstract":"Stakeholders within the Yakima River Basin expressed concern over impacts of climate change on mid-Columbia River steelhead (Oncorhynchus mykiss), listed under the Endangered Species Act. We used a bioenergetics model to assess the impacts of changing stream temperatures—resulting from different climate change scenarios—on growth of juvenile steelhead in the Yakima River Basin. We used diet and fish size data from fieldwork in a bioenergetics model and integrated baseline and projected stream temperatures from down-scaled air temperature climate modeling into our analysis. The stream temperature models predicted that daily mean temperatures of salmonid-rearing streams in the basin could increase by 1–2°C and our bioenergetics simulations indicated that such increases could enhance the growth of steelhead in the spring, but reduce it during the summer. However, differences in growth rates of fish living under different climate change scenarios were minor, ranging from about 1–5%. Because our analysis focused mostly on the growth responses of steelhead to changes in stream temperatures, further work is needed to fully understand the potential impacts of climate change. Studies should include evaluating changing stream flows on fish activity and energy budgets, responses of aquatic insects to climate change, and integration of bioenergetics, population dynamics, and habitat responses to climate change.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-012-0627-x","usgsCitation":"Hardiman, J.M., and Mesa, M.G., 2013, The effects of increased stream temperatures on juvenile steelhead growth in the Yakima River Basin based on projected climate change scenarios: Climate Change, v. 124, no. 1, p. 413-426, https://doi.org/10.1007/s10584-012-0627-x.","productDescription":"14 p.","startPage":"413","endPage":"426","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-037042","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":273237,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -120.65,46.19 ], [ -120.65,47.01 ], [ -119.77,47.01 ], [ -119.77,46.19 ], [ -120.65,46.19 ] ] ] } } ] }","volume":"124","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-01-17","publicationStatus":"PW","scienceBaseUri":"51af0c70e4b08a3322c2c351","contributors":{"authors":[{"text":"Hardiman, Jill M. 0000-0002-3661-9695 jhardiman@usgs.gov","orcid":"https://orcid.org/0000-0002-3661-9695","contributorId":2672,"corporation":false,"usgs":true,"family":"Hardiman","given":"Jill","email":"jhardiman@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":476122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mesa, Matthew G. mmesa@usgs.gov","contributorId":3423,"corporation":false,"usgs":true,"family":"Mesa","given":"Matthew","email":"mmesa@usgs.gov","middleInitial":"G.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":476123,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045174,"text":"ofr20131031 - 2013 - Effects of equipment performance on data quality from the National Atmospheric Deposition Program/National Trends Network and the Mercury Deposition Network","interactions":[],"lastModifiedDate":"2013-04-01T12:49:10","indexId":"ofr20131031","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","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":"2013-1031","title":"Effects of equipment performance on data quality from the National Atmospheric Deposition Program/National Trends Network and the Mercury Deposition Network","docAbstract":"The U.S. Geological Survey Branch of Quality Systems operates the Precipitation Chemistry Quality Assurance project (PCQA) to provide independent, external quality-assurance for the National Atmospheric Deposition Program (NADP). NADP is composed of five monitoring networks that measure the chemical composition of precipitation and ambient air. PCQA and the NADP Program Office completed five short-term studies to investigate the effects of equipment performance with respect to the National Trends Network (NTN) and Mercury Deposition Network (MDN) data quality: sample evaporation from NTN collectors; sample volume and mercury loss from MDN collectors; mercury adsorption to MDN collector glassware, grid-type precipitation sensors for precipitation collectors, and the effects of an NTN collector wind shield on sample catch efficiency. Sample-volume evaporation from an NTN Aerochem Metrics (ACM) collector ranged between 1.1–33 percent with a median of 4.7 percent. The results suggest that weekly NTN sample evaporation is small relative to sample volume. MDN sample evaporation occurs predominantly in western and southern regions of the United States (U.S.) and more frequently with modified ACM collectors than with N-CON Systems Inc. collectors due to differences in airflow through the collectors. Variations in mercury concentrations, measured to be as high as 47.5 percent per week with a median of 5 percent, are associated with MDN sample-volume loss. Small amounts of mercury are also lost from MDN samples by adsorption to collector glassware irrespective of collector type. MDN 11-grid sensors were found to open collectors sooner, keep them open longer, and cause fewer lid cycles than NTN 7-grid sensors. Wind shielding an NTN ACM collector resulted in collection of larger quantities of precipitation while also preserving sample integrity.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131031","usgsCitation":"Wetherbee, G.A., and Rhodes, M.F., 2013, Effects of equipment performance on data quality from the National Atmospheric Deposition Program/National Trends Network and the Mercury Deposition Network: U.S. Geological Survey Open-File Report 2013-1031, ix, 53 p., https://doi.org/10.3133/ofr20131031.","productDescription":"ix, 53 p.","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":143,"text":"Branch of Quality Systems","active":true,"usgs":true}],"links":[{"id":270417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131031.gif"},{"id":270415,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1031/"},{"id":270416,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1031/OF13-1031.pdf"}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 144.616667,13.233333 ], [ 144.616667,71.833333 ], [ -64.566667,71.833333 ], [ -64.566667,13.233333 ], [ 144.616667,13.233333 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515a9e5ee4b0105540728a1e","contributors":{"authors":[{"text":"Wetherbee, Gregory A. 0000-0002-6720-2294 wetherbe@usgs.gov","orcid":"https://orcid.org/0000-0002-6720-2294","contributorId":1044,"corporation":false,"usgs":true,"family":"Wetherbee","given":"Gregory","email":"wetherbe@usgs.gov","middleInitial":"A.","affiliations":[{"id":143,"text":"Branch of Quality Systems","active":true,"usgs":true}],"preferred":true,"id":476988,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rhodes, Mark F.","contributorId":17360,"corporation":false,"usgs":true,"family":"Rhodes","given":"Mark","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":476989,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70044434,"text":"70044434 - 2013 - White-nose syndrome is likely to extirpate the endangered Indiana bat over large parts of its range","interactions":[],"lastModifiedDate":"2018-01-04T15:23:01","indexId":"70044434","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"White-nose syndrome is likely to extirpate the endangered Indiana bat over large parts of its range","docAbstract":"White-nose syndrome, a novel fungal pathogen spreading quickly through cave-hibernating bat species in east and central North America, is responsible for killing millions of bats. We developed a stochastic, stage-based population model to forecast the population dynamics of the endangered Indiana bat (Myotis sodalis) subject to white-nose syndrome. Our population model explicitly incorporated environmentally imposed annual variability in survival and reproductive rates and demographic stochasticity in predictions of extinction. With observed rates of disease spread, >90% of wintering populations were predicted to experience white-nose syndrome within 20 years, causing the proportion of populations at the quasi-extinction threshold of less than 250 females to increase by 33.9% over 50 years. At the species’ lowest median population level, ca. year 2022, we predicted 13.7% of the initial population to remain, totaling 28,958 females (95% CI = 13,330; 92,335). By 2022, only 12 of the initial 52 wintering populations were expected to possess wintering populations of >250 females. If the species can acquire immunity to the disease, we predict 3.7% of wintering populations to be above 250 females after 50 years (year 2057) after a 69% decline in abundance (from 210,741 to 64,768 [95% CI = 49,386; 85,360] females). At the nadir of projections, we predicted regional quasi-extirpation of wintering populations in 2 of 4 Recovery Units while in a third region, where the species is currently most abundant, >95% of the wintering populations were predicted to be below 250 females. Our modeling suggests white-nose syndrome is capable of bringing about severe numerical reduction in population size and local and regional extirpation of the Indiana bat.","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.biocon.2013.01.010","usgsCitation":"Thogmartin, W.E., Sanders-Reed, C., Szymanski, J.A., McKann, P., Pruitt, L., King, R.A., Runge, M.C., and Russell, R.E., 2013, White-nose syndrome is likely to extirpate the endangered Indiana bat over large parts of its range: Biological Conservation, v. 160, p. 162-172, https://doi.org/10.1016/j.biocon.2013.01.010.","productDescription":"11 p.","startPage":"162","endPage":"172","ipdsId":"IP-036949","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":270474,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.biocon.2013.01.010"},{"id":270554,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"North America","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -167.5,5.6 ], [ -167.5,72.2 ], [ -21.6,72.2 ], [ -21.6,5.6 ], [ -167.5,5.6 ] ] ] } } ] }","volume":"160","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515d4f70e4b0803bd2eec551","contributors":{"authors":[{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":475584,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanders-Reed, Carol A.","contributorId":86441,"corporation":false,"usgs":true,"family":"Sanders-Reed","given":"Carol A.","affiliations":[],"preferred":false,"id":475591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Szymanski, Jennifer A.","contributorId":51593,"corporation":false,"usgs":true,"family":"Szymanski","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":475590,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKann, Patrick C.","contributorId":14940,"corporation":false,"usgs":true,"family":"McKann","given":"Patrick C.","affiliations":[],"preferred":false,"id":475587,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pruitt, Lori","contributorId":17468,"corporation":false,"usgs":true,"family":"Pruitt","given":"Lori","email":"","affiliations":[],"preferred":false,"id":475588,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"King, R. Andrew","contributorId":40839,"corporation":false,"usgs":true,"family":"King","given":"R.","email":"","middleInitial":"Andrew","affiliations":[],"preferred":false,"id":475589,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":475585,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Russell, Robin E. 0000-0001-8726-7303 rerussell@usgs.gov","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":3998,"corporation":false,"usgs":true,"family":"Russell","given":"Robin","email":"rerussell@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":475586,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70189267,"text":"70189267 - 2013 - A refined index of model performance: a rejoinder","interactions":[],"lastModifiedDate":"2017-07-07T10:23:43","indexId":"70189267","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2032,"text":"International Journal of Climatology","active":true,"publicationSubtype":{"id":10}},"title":"A refined index of model performance: a rejoinder","docAbstract":"Willmott et al. [Willmott CJ, Robeson SM, Matsuura K. 2012. A refined index of model performance. International Journal of Climatology, forthcoming. DOI:10.1002/joc.2419.] recently suggest a refined index of model performance (dr) that they purport to be superior to other methods. Their refined index ranges from − 1.0 to 1.0 to resemble a correlation coefficient, but it is merely a linear rescaling of our modified coefficient of efficiency (E1) over the positive portion of the domain of dr. We disagree with Willmott et al. (2012) that dr provides a better interpretation; rather, E1 is more easily interpreted such that a value of E1 = 1.0 indicates a perfect model (no errors) while E1 = 0.0 indicates a model that is no better than the baseline comparison (usually the observed mean). Negative values of E1 (and, for that matter, dr < 0.5) indicate a substantially flawed model as they simply describe a ‘level of inefficacy’ for a model that is worse than the comparison baseline. Moreover, while dr is piecewise continuous, it is not continuous through the second and higher derivatives. We explain why the coefficient of efficiency (E or E2) and its modified form (E1) are superior and preferable to many other statistics, including dr, because of intuitive interpretability and because these indices have a fundamental meaning at zero.
We also expand on the discussion begun by Garrick et al. [Garrick M, Cunnane C, Nash JE. 1978. A criterion of efficiency for rainfall-runoff models. Journal of Hydrology 36: 375-381.] and continued by Legates and McCabe [Legates DR, McCabe GJ. 1999. Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research 35(1): 233-241.] and Schaefli and Gupta [Schaefli B, Gupta HV. 2007. Do Nash values have value? Hydrological Processes 21: 2075-2080. DOI: 10.1002/hyp.6825.]. This important discussion focuses on the appropriate baseline comparison to use, and why the observed mean often may be an inadequate choice for model evaluation and development.
","language":"English","publisher":"Royal Meteorological Society","doi":"10.1002/joc.3487","usgsCitation":"Legates, D.R., and McCabe, G., 2013, A refined index of model performance: a rejoinder: International Journal of Climatology, v. 33, no. 4, p. 1053-1056, https://doi.org/10.1002/joc.3487.","productDescription":"4 p.","startPage":"1053","endPage":"1056","ipdsId":"IP-034043","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":343465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2012-04-18","publicationStatus":"PW","scienceBaseUri":"59609db9e4b0d1f9f0594c46","contributors":{"authors":[{"text":"Legates, David R.","contributorId":194273,"corporation":false,"usgs":false,"family":"Legates","given":"David","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":703820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":167116,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory J.","email":"gmccabe@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":703819,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045064,"text":"tm4C3 - 2013 - Stochastic empirical loading and dilution model (SELDM) version 1.0.0","interactions":[],"lastModifiedDate":"2014-06-10T15:49:42","indexId":"tm4C3","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"4-C3","title":"Stochastic empirical loading and dilution model (SELDM) version 1.0.0","docAbstract":"The Stochastic Empirical Loading and Dilution Model (SELDM) is designed to transform complex scientific data into meaningful information about the risk of adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks. The U.S. Geological Survey developed SELDM in cooperation with the Federal Highway Administration to help develop planning-level estimates of event mean concentrations, flows, and loads in stormwater from a site of interest and from an upstream basin. Planning-level estimates are defined as the results of analyses used to evaluate alternative management measures; planning-level estimates are recognized to include substantial uncertainties (commonly orders of magnitude). SELDM uses information about a highway site, the associated receiving-water basin, precipitation events, stormflow, water quality, and the performance of mitigation measures to produce a stochastic population of runoff-quality variables. SELDM provides input statistics for precipitation, prestorm flow, runoff coefficients, and concentrations of selected water-quality constituents from National datasets. Input statistics may be selected on the basis of the latitude, longitude, and physical characteristics of the site of interest and the upstream basin. The user also may derive and input statistics for each variable that are specific to a given site of interest or a given area. SELDM is a stochastic model because it uses Monte Carlo methods to produce the random combinations of input variable values needed to generate the stochastic population of values for each component variable. SELDM calculates the dilution of runoff in the receiving waters and the resulting downstream event mean concentrations and annual average lake concentrations. Results are ranked, and plotting positions are calculated, to indicate the level of risk of adverse effects caused by runoff concentrations, flows, and loads on receiving waters by storm and by year. Unlike deterministic hydrologic models, SELDM is not calibrated by changing values of input variables to match a historical record of values. Instead, input values for SELDM are based on site characteristics and representative statistics for each hydrologic variable. Thus, SELDM is an empirical model based on data and statistics rather than theoretical physiochemical equations. SELDM is a lumped parameter model because the highway site, the upstream basin, and the lake basin each are represented as a single homogeneous unit. Each of these source areas is represented by average basin properties, and results from SELDM are calculated as point estimates for the site of interest. Use of the lumped parameter approach facilitates rapid specification of model parameters to develop planning-level estimates with available data. The approach allows for parsimony in the required inputs to and outputs from the model and flexibility in the use of the model. For example, SELDM can be used to model runoff from various land covers or land uses by using the highway-site definition as long as representative water quality and impervious-fraction data are available.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Water Quality in Book 4 Hydrologic Analysis and Interpretation","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4C3","collaboration":"Prepared in cooperation with the Department of Transportation Federal Highway Administration, Office of Project Development and Environmental Review. This report is Chapter 3 of Section C: Water Quality in Book 4 Hydrologic Analysis and Interpretation","usgsCitation":"Granato, G., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S. Geological Survey Techniques and Methods 4-C3, Manual: xii, 112 p.; 5 Appendices; Digital Media Directory, https://doi.org/10.3133/tm4C3.","productDescription":"Manual: xii, 112 p.; 5 Appendices; Digital Media Directory","numberOfPages":"124","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":270337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm4C3.jpg"},{"id":270332,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/04/c03/tm4-C3_final_508_files/tm4-C3_apdx1_v030813.pdf"},{"id":270330,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/04/c03/"},{"id":270333,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/04/c03/tm4-C3_final_508_files/tm4-C3_apdx2_v030813.pdf"},{"id":270331,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/c03/tm4-C3_final_508_files/tm4-C3_main_v031913.pdf"},{"id":270334,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/04/c03/tm4-C3_final_508_files/tm4-C3_apdx3_pages_v030813.pdf"},{"id":270335,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/04/c03/tm4-C3_final_508_files/tm4-C3_apdx4_v030813.pdf"},{"id":270336,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/tm/04/c03/virtual_CD/index.html"}],"country":"United States","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5156a9ece4b06ea905cdc006","contributors":{"authors":[{"text":"Granato, Gregory E. 0000-0002-2561-9913 ggranato@usgs.gov","orcid":"https://orcid.org/0000-0002-2561-9913","contributorId":1692,"corporation":false,"usgs":true,"family":"Granato","given":"Gregory E.","email":"ggranato@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":false,"id":476716,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045073,"text":"sir20135049 - 2013 - Shallow groundwater in the Matanuska-Susitna Valley, Alaska—Conceptualization and simulation of flow","interactions":[],"lastModifiedDate":"2018-07-18T13:50:39","indexId":"sir20135049","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5049","title":"Shallow groundwater in the Matanuska-Susitna Valley, Alaska—Conceptualization and simulation of flow","docAbstract":"The Matanuska-Susitna Valley is in the Upper Cook Inlet Basin and is currently undergoing rapid population growth outside of municipal water and sewer service areas. In response to concerns about the effects of increasing water use on future groundwater availability, a study was initiated between the Alaska Department of Natural Resources and the U.S. Geological Survey. The goals of the study were (1) to compile existing data and collect new data to support hydrogeologic conceptualization of the study area, and (2) to develop a groundwater flow model to simulate flow dynamics important at the regional scale. The purpose of the groundwater flow model is to provide a scientific framework for analysis of regional-scale groundwater availability. To address the first study goal, subsurface lithologic data were compiled into a database and were used to construct a regional hydrogeologic framework model describing the extent and thickness of hydrogeologic units in the Matanuska-Susitna Valley. The hydrogeologic framework model synthesizes existing maps of surficial geology and conceptual geochronologies developed in the study area with the distribution of lithologies encountered in hundreds of boreholes. The geologic modeling package Geological Surveying and Investigation in Three Dimensions (GSI3D) was used to construct the hydrogeologic framework model. In addition to characterizing the hydrogeologic framework, major groundwater-budget components were quantified using several different techniques. A land-surface model known as the Deep Percolation Model was used to estimate in-place groundwater recharge across the study area. This model incorporates data on topography, soils, vegetation, and climate. Model-simulated surface runoff was consistent with observed streamflow at U.S. Geological Survey streamgages. Groundwater withdrawals were estimated on the basis of records from major water suppliers during 2004-2010. Fluxes between groundwater and surface water were estimated during field investigations on several small streams. Regional groundwater flow patterns were characterized by synthesizing previous water-table maps with a synoptic water-level measurement conducted during 2009. Time-series water-level data were collected at groundwater and lake monitoring stations over the study period (2009–present). Comparison of historical groundwater-level records with time-series groundwater-level data collected during this study showed similar patterns in groundwater-level fluctuation in response to precipitation. Groundwater-age data collected during previous studies show that water moves quickly through the groundwater system, suggesting that the system responds quickly to changes in climate forcing. Similarly, the groundwater system quickly returns to long-term average conditions following variability due to seasonal or interannual changes in precipitation. These analyses indicate that the groundwater system is in a state of dynamic equilibrium, characterized by water-level fluctuation about a constant average state, with no long-term trends in aquifer-system storage. To address the second study goal, a steady-state groundwater flow model was developed to simulate regional groundwater flow patterns. The groundwater flow model was bounded by physically meaningful hydrologic features, and appropriate internal model boundaries were specified on the basis of conceptualization of the groundwater system resulting in a three-layer model. Calibration data included 173 water‑level measurements and 18 measurements of streamflow gains and losses along small streams. Comparison of simulated and observed heads and flows showed that the model accurately simulates important regional characteristics of the groundwater flow system. This model is therefore appropriate for studying regional-scale groundwater availability. Mismatch between model-simulated and observed hydrologic quantities is likely because of the coarse grid size of the model and seasonal transient effects. Next steps towards model refinement include the development of a transient groundwater flow model that is suitable for analysis of seasonal variability in hydraulic heads and flows. In addition, several important groundwater budget components remain poorly quantified—including groundwater outflow to the Matanuska River, Little Susitna River, and Knik Arm.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135049","collaboration":"Prepared in cooperation with the Alaska Department of Natural Resources","usgsCitation":"Kikuchi, C.P., 2013, Shallow groundwater in the Matanuska-Susitna Valley, Alaska—Conceptualization and simulation of flow: U.S. Geological Survey Scientific Investigations Report 2013-5049, Report: viii, 86 p.; 4 Appendices, https://doi.org/10.3133/sir20135049.","productDescription":"Report: viii, 86 p.; 4 Appendices","numberOfPages":"96","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":270376,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135049.jpg"},{"id":270372,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5049/sir20135049_appendixA.xlsx"},{"id":270373,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5049/sir20135049_appendixB.xlsx"},{"id":270374,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5049/sir20135049_appendixC.xlsx"},{"id":270375,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5049/sir20135049_appendixD.xlsx"},{"id":270370,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5049/"},{"id":270371,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5049/pdf/sir20135049.pdf"}],"country":"United States","state":"Alaska","otherGeospatial":"Matanuska-susitna Valley","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5156a9e9e4b06ea905cdbff6","contributors":{"authors":[{"text":"Kikuchi, Colin P.","contributorId":61311,"corporation":false,"usgs":true,"family":"Kikuchi","given":"Colin","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":476735,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045074,"text":"sir20135036 - 2013 - Simulation of salinity intrusion along the Georgia and South Carolina coasts using climate-change scenarios","interactions":[],"lastModifiedDate":"2017-01-18T13:12:10","indexId":"sir20135036","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-5036","title":"Simulation of salinity intrusion along the Georgia and South Carolina coasts using climate-change scenarios","docAbstract":"Potential changes in climate could alter interactions between environmental and societal systems and adversely affect the availability of water resources in many coastal communities. Changes in streamflow patterns in conjunction with sea-level rise may change the salinity-intrusion dynamics of coastal rivers. Several municipal water-supply intakes are located along the Georgia and South Carolina coast that are proximal to the present day saltwater-freshwater interface of tidal rivers. Increases in the extent of salinity intrusion resulting from climate change could threaten the availability of freshwater supplies in the vicinity of these intakes. To effectively manage these supplies, water-resource managers need estimates of potential changes in the frequency, duration, and magnitude of salinity intrusion near their water-supply intakes that may occur as a result of climate change. This study examines potential effects of climate change, including altered streamflow and sea-level rise, on the dynamics of saltwater intrusion near municipal water-supply intakes in two coastal areas. One area consists of the Atlantic Intracoastal Waterway (AIW) and the Waccamaw River near Myrtle Beach along the Grand Strand of the South Carolina Coast, and the second area is on or near the lower Savannah River near Savannah, Georgia. The study evaluated how future sea-level rise and a reduction in streamflows can potentially affect salinity intrusion and threaten municipal water supplies and the biodiversity of freshwater tidal marshes in these two areas. Salinity intrusion occurs as a result of the interaction between three principal forces—streamflow, mean coastal water levels, and tidal range. To analyze and simulate salinity dynamics at critical coastal gaging stations near four municipal water-supply intakes, various data-mining techniques, including artificial neural network (ANN) models, were used to evaluate hourly streamflow, salinity, and coastal water-level data collected over a period exceeding 10 years. The ANN models were trained (calibrated) to learn the specific interactions that cause salinity intrusions, and resulting models were able to accurately simulate historical salinity dynamics in both study areas. Changes in sea level and streamflow quantity and timing can be simulated by the salinity intrusion models to evaluate various climate-change scenarios. The salinity intrusion models for the study areas are deployed in a decision support system to facilitate the use of the models for management decisions by coastal water-resource managers. The report describes the use of the salinity-intrusion models decision support system to evaluate salinity-intrusion dynamics for various climate-change scenarios, including incremental increases in sea level in combination with incremental decreases in streamflow. Operation of municipal water-treatment plants is problematic when the specific-conductance values for source water are greater than 1,000 to 2,000 microsiemens per centimeter (µS/cm). High specific-conductance values contribute to taste problems that require treatment. Data from a gage downstream from a municipal water intake indicate specific conductance exceeded 1,000 µS/cm about 5.4 percent of the time over the 14-year period from August 1995 to August 2008. Simulations of specific conductance at this gaging station that incorporates sea-level rises resulted in a doubling of the exceedances to 11.0 percent for a 1-foot increase and 17.6 percent for a 2-foot increase. The frequency of intrusion of water with specific conductance values of 1,000 µS/cm was less sensitive to incremental reductions in streamflow than to incremental increases in sea level. Simulations of conditions associated with a 10-percent reduction in streamflow, in combination with a 1-foot rise in sea level, increased the percentage of time specific conductance exceeded 1,000 µS/cm at this site from 11.0 to 13.3 percent, and a 20-percent reduction in streamflow increased the percentage of time to 16.6 percent. Precipitation and temperature data from a global circulation model were used, after scale adjustments, as input to a watershed model of the Yadkin-Pee Dee River basin, which flows into the Waccamaw River and Atlantic Intracoastal Waterway study area in South Carolina. The simulated streamflow for historical conditions and projected climate change in the future was used as input for the ANN model in decision support system. Results of simulations incorporating climate-change projections for alterations in streamflow indicate an increase in the frequency of salinity-intrusion events and a shift in the seasonal occurrence of the intrusion events from the summer to the fall.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135036","collaboration":"Prepared in cooperation with the Beaufort-Jasper Water and Sewer Authority","usgsCitation":"Conrads, P., Roehl, E.A., Daamen, R.C., and Cook, J., 2013, Simulation of salinity intrusion along the Georgia and South Carolina coasts using climate-change scenarios: U.S. Geological Survey Scientific Investigations Report 2013-5036, Report: xix, 94 p.; 5 Appendices, https://doi.org/10.3133/sir20135036.","productDescription":"Report: xix, 94 p.; 5 Appendices","numberOfPages":"110","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5156a9eae4b06ea905cdbffa","contributors":{"authors":[{"text":"Conrads, Paul 0000-0003-0408-4208 pconrads@usgs.gov","orcid":"https://orcid.org/0000-0003-0408-4208","contributorId":764,"corporation":false,"usgs":true,"family":"Conrads","given":"Paul","email":"pconrads@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":476736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roehl, Edwin A. Jr.","contributorId":108083,"corporation":false,"usgs":false,"family":"Roehl","given":"Edwin","suffix":"Jr.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":476739,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daamen, Ruby C.","contributorId":105391,"corporation":false,"usgs":true,"family":"Daamen","given":"Ruby","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":476738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cook, John B.","contributorId":45594,"corporation":false,"usgs":true,"family":"Cook","given":"John B.","affiliations":[],"preferred":false,"id":476737,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045067,"text":"sir20125248 - 2013 - Status and understanding of groundwater quality in the San Francisco Bay groundwater basins, 2007—California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2013-03-29T11:19:06","indexId":"sir20125248","displayToPublicDate":"2013-03-29T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5248","title":"Status and understanding of groundwater quality in the San Francisco Bay groundwater basins, 2007—California GAMA Priority Basin Project","docAbstract":"Groundwater quality in the approximately 620-square-mile (1,600-square-kilometer) San Francisco Bay study unit was investigated as part of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is located in the Southern Coast Ranges of California, in San Francisco, San Mateo, Santa Clara, Alameda, and Contra Costa Counties. The GAMA Priority Basin Project is being conducted by the California State Water Resources Control Board in collaboration with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory. The GAMA San Francisco Bay study was designed to provide a spatially unbiased assessment of the quality of untreated groundwater within the primary aquifer system, as well as a statistically consistent basis for comparing water quality throughout the State. The assessment is based on water-quality and ancillary data collected by the USGS from 79 wells in 2007 and is supplemented with water-quality data from the California Department of Public Health (CDPH) database. The primary aquifer system is defined by the depth interval of the wells listed in the CDPH database for the San Francisco Bay study unit. The quality of groundwater in shallower or deeper water-bearing zones may differ from that in the primary aquifer system; shallower groundwater may be more vulnerable to surficial contamination. The first component of this study, the status of the current quality of the groundwater resource, was assessed by using data from samples analyzed for volatile organic compounds (VOCs), pesticides, and naturally occurring inorganic constituents, such as major ions and trace elements. Water- quality data from the CDPH database also were incorporated for this assessment. This status assessment is intended to characterize the quality of groundwater resources within the primary aquifer system of the San Francisco Bay study unit, not the treated drinking water delivered to consumers by water purveyors.\n Relative-concentrations (sample concentration divided by the benchmark concentration) were used for evaluating groundwater quality for those constituents that have Federal and (or) California benchmarks. A relative-concentration greater than (>) 1.0 indicates a concentration greater than a benchmark, and a relative-concentration less than or equal to (≤) 1.0 indicates a concentration equal to or less than a benchmark. Relative-concentrations of organic and special-interest constituents were classified as low (relative- concentration ≤ 0.1), moderate (0.1 < relative- concentration ≤ 1.0), or high (relative-concentration > 1.0). Inorganic constituent relative- concentrations were classified as low (relative-concentration ≤ 0.5), moderate (0.5 < relative-concentration ≤ 1.0), or high (relative- concentration > 1.0). A lower threshold value of relative-concentration was used to distinguish between low and moderate values of organic constituents because organic constituents are generally less prevalent and have smaller relative-concentrations than naturally occurring inorganic constituents. Aquifer-scale proportion was used as the metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion is defined as the percentage of the primary aquifer system that has relative-concentration greater than 1.0 for a particular constituent or class of constituents; proportion is based on an areal rather than a volumetric basis. Moderate and low aquifer-scale proportions were defined as the percentages of the primary aquifer system that have moderate and low relative-concentrations, respectively. Two statistical approaches—grid-based and spatially weighted—were used to evaluate aquifer-scale proportion for individual constituents and classes of constituents. Grid-based and spatially weighted estimates were comparable in the San Francisco Bay study unit (90-percent confidence intervals). Inorganic constituents with health-based benchmarks were present at high relative-concentrations in 5.1 percent of the primary aquifer system, and at moderate relative-concentrations in 25 percent. The high aquifer-scale proportion of inorganic constituents primarily reflected high aquifer-scale proportions of barium (3.0 percent) and nitrate (2.1 percent). Inorganic constituents with secondary maximum contaminant levels were present at high relative-concentrations in 14 percent of the primary aquifer system and at moderate relative-concentrations in 33 percent. The constituents present at high relative-concentrations included total dissolved solids (7.0 percent), chloride (6.1 percent), manganese (12 percent), and iron (3.0 percent). Organic constituents with health-based benchmarks were present at high relative-concentrations in 0.6 percent and at moderate relative-concentrations in 12 percent of the primary aquifer system. Of the 202 organic constituents analyzed for, 32 were detected. Three organic constituents were frequently detected (in 10 percent or more of samples): the trihalomethane chloroform, the solvent 1,1,1-trichloroethane and the refrigerant 1,1,2-trichlorotrifluoroethane. One special-interest constituent, perchlorate, was detected at moderate relative-concentrations in 42 percent of the primary aquifer system. The second component of this work, the understanding assessment, identified some of the primary natural and human factors that may affect groundwater quality by evaluating land use, physical characteristics of the wells, and geochemical conditions of the aquifer. Results from these evaluations were used to explain the occurrence and distribution of constituents in the study unit.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125248","collaboration":"Prepared in cooperation with the California State Water Resources Control Board A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","usgsCitation":"Parsons, M.C., Kulongoski, J., and Belitz, K., 2013, Status and understanding of groundwater quality in the San Francisco Bay groundwater basins, 2007—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2012-5248, x, 78 p., https://doi.org/10.3133/sir20125248.","productDescription":"x, 78 p.","numberOfPages":"90","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":270354,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20125248.jpg"},{"id":270352,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5248/"},{"id":270353,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5248/pdf/sir20125248.pdf"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.01055555555556,8.333333333333334E-4 ], [ -122.01055555555556,8.333333333333334E-4 ], [ -0.01611111111111111,8.333333333333334E-4 ], [ -0.01611111111111111,8.333333333333334E-4 ], [ -122.01055555555556,8.333333333333334E-4 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5156a9ebe4b06ea905cdc002","contributors":{"authors":[{"text":"Parsons, Mary C. mparsons@usgs.gov","contributorId":1571,"corporation":false,"usgs":true,"family":"Parsons","given":"Mary","email":"mparsons@usgs.gov","middleInitial":"C.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":94750,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin T.","affiliations":[],"preferred":false,"id":476725,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476723,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045097,"text":"70045097 - 2013 - A new map of standardized terrestrial ecosystems of Africa","interactions":[],"lastModifiedDate":"2018-03-23T14:25:00","indexId":"70045097","displayToPublicDate":"2013-03-28T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":669,"text":"African Geographical Review","active":true,"publicationSubtype":{"id":10}},"title":"A new map of standardized terrestrial ecosystems of Africa","docAbstract":"Terrestrial ecosystems and vegetation of Africa were classified and mapped as part of a larger effort and global protocol (GEOSS – the Global Earth Observation System of Systems), which includes an activity to map terrestrial ecosystems of the earth in a standardized, robust, and practical manner, and at the finest possible spatial resolution. To model the potential distribution of ecosystems, new continental datasets for several key physical environment datalayers (including coastline, landforms, surficial lithology, and bioclimates) were developed at spatial and classification resolutions finer than existing similar datalayers. A hierarchical vegetation classification was developed by African ecosystem scientists and vegetation geographers, who also provided sample locations of the newly classified vegetation units. The vegetation types and ecosystems were then mapped across the continent using a classification and regression tree (CART) inductive model, which predicted the potential distribution of vegetation types from a suite of biophysical environmental attributes including bioclimate region, biogeographic region, surficial lithology, landform, elevation and land cover. Multi-scale ecosystems were classified and mapped in an increasingly detailed hierarchical framework using vegetation-based concepts of class, subclass, formation, division, and macrogroup levels. The finest vegetation units (macrogroups) classified and mapped in this effort are defined using diagnostic plant species and diagnostic growth forms that reflect biogeographic differences in composition and sub-continental to regional differences in mesoclimate, geology, substrates, hydrology, and disturbance regimes (FGDC, 2008). The macrogroups are regarded as meso-scale (100s to 10,000s of hectares) ecosystems. A total of 126 macrogroup types were mapped, each with multiple, repeating occurrences on the landscape. The modeling effort was implemented at a base spatial resolution of 90 m. In addition to creating several rich, new continent-wide biophysical datalayers describing African vegetation and ecosystems, our intention was to explore feasible approaches to rapidly moving this type of standardized, continent-wide, ecosystem classification and mapping effort forward.","language":"English","publisher":"Association of American Geographers","publisherLocation":"Washington, D.C.","usgsCitation":"Sayre, R.G., Comer, P., Hak, J., Josse, C., Bow, J., Warner, H., Larwanou, M., Kelbessa, E., Bekele, T., Kehl, H., Amena, R., Andriamasimanana, R., Ba, T., Benson, L., Boucher, T., Brown, M., Cress, J.J., Dassering, O., Friesen, B.A., Gachathi, F., Houcine, S., Keita, M., Khamala, E., Marangu, D., Mokua, F., Morou, B., Mucina, L., Mugisha, S., Mwavu, E., Rutherford, M., Sanou, P., Syampungani, S., Tomor, B., Vall, A.O., Vande Weghe, J.P., Wangui, E., and Waruingi, L., 2013, A new map of standardized terrestrial ecosystems of Africa: African Geographical Review, 24 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Bojoi","contributorId":175020,"corporation":false,"usgs":false,"family":"Tomor","given":"Bojoi","email":"","affiliations":[],"preferred":false,"id":649881,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Vall, Abdallahi Ould Mohamed","contributorId":175021,"corporation":false,"usgs":false,"family":"Vall","given":"Abdallahi","email":"","middleInitial":"Ould Mohamed","affiliations":[],"preferred":false,"id":649882,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"Vande Weghe, Jean Pierre","contributorId":175022,"corporation":false,"usgs":false,"family":"Vande Weghe","given":"Jean","email":"","middleInitial":"Pierre","affiliations":[],"preferred":false,"id":649883,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Wangui, 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probabilities: an empirical model to predict rates of invasion via ballast water","docAbstract":"Ballast water discharges are a major source of species introductions into marine and estuarine ecosystems. To mitigate the introduction of new invaders into these ecosystems, many agencies are proposing standards that establish upper concentration limits for organisms in ballast discharge. Ideally, ballast discharge standards will be biologically defensible and adequately protective of the marine environment. We propose a new technique, the per capita invasion probability (PCIP), for managers to quantitatively evaluate the relative risk of different concentration-based ballast water discharge standards. PCIP represents the likelihood that a single discharged organism will become established as a new nonindigenous species. This value is calculated by dividing the total number of ballast water invaders per year by the total number of organisms discharged from ballast. Analysis was done at the coast-wide scale for the Atlantic, Gulf, and Pacific coasts, as well as the Great Lakes, to reduce uncertainty due to secondary invasions between estuaries on a single coast. The PCIP metric is then used to predict the rate of new ballast-associated invasions given various regulatory scenarios. Depending upon the assumptions used in the risk analysis, this approach predicts that approximately one new species will invade every 10–100 years with the International Maritime Organization (IMO) discharge standard of <10 organisms with body size >50 μm per m3 of ballast. This approach resolves many of the limitations associated with other methods of establishing ecologically sound discharge standards, and it allows policy makers to use risk-based methodologies to establish biologically defensible discharge standards.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecological Applications","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Ecological Society of America","publisherLocation":"Ithaca, NY","doi":"10.1890/11-1637.1","usgsCitation":"Reusser, D.A., Lee, H., Frazier, M., Ruiz, G., Fofonoff, P.W., Minton, M.S., and Miller, A.W., 2013, Per capita invasion probabilities: an empirical model to predict rates of invasion via ballast water: Ecological Applications, v. 23, no. 2, p. 321-330, https://doi.org/10.1890/11-1637.1.","productDescription":"10 p.","startPage":"321","endPage":"330","ipdsId":"IP-028025","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":270320,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":270319,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/11-1637.1"}],"volume":"23","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515406dfe4b030c71ee0670f","contributors":{"authors":[{"text":"Reusser, Deborah A. dreusser@usgs.gov","contributorId":2423,"corporation":false,"usgs":true,"family":"Reusser","given":"Deborah","email":"dreusser@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":476602,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Henry II","contributorId":40334,"corporation":false,"usgs":true,"family":"Lee","given":"Henry","suffix":"II","affiliations":[],"preferred":false,"id":476605,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frazier, Melanie","contributorId":59701,"corporation":false,"usgs":true,"family":"Frazier","given":"Melanie","affiliations":[],"preferred":false,"id":476606,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruiz, Gregory M.","contributorId":71073,"corporation":false,"usgs":true,"family":"Ruiz","given":"Gregory M.","affiliations":[],"preferred":false,"id":476607,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fofonoff, Paul W.","contributorId":21042,"corporation":false,"usgs":true,"family":"Fofonoff","given":"Paul","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":476603,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Minton, Mark S.","contributorId":73896,"corporation":false,"usgs":true,"family":"Minton","given":"Mark","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":476608,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, A. 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