Researchers and practitioners alike often need to understand and characterize how water and solutes move through a stream in terms of the relative importance of in-stream and near-stream storage and transport processes. In-channel and subsurface storage processes are highly variable in space and time and difficult to measure. Storage estimates are commonly obtained using transient-storage models (TSMs) of the experimentally obtained solute-tracer test data. The TSM equations represent key transport and storage processes with a suite of numerical parameters. Parameter values are estimated via inverse modeling, in which parameter values are iteratively changed until model simulations closely match observed solute-tracer data. Several investigators have shown that TSM parameter estimates can be highly uncertain. When this is the case, parameter values cannot be used reliably to interpret stream-reach functioning. However, authors of most TSM studies do not evaluate or report parameter certainty. Here, we present a software tool linked to the One-dimensional Transport with Inflow and Storage (OTIS) model that enables researchers to conduct uncertainty analyses via Monte-Carlo parameter sampling and to visualize uncertainty and sensitivity results. We demonstrate application of our tool to 2 case studies and compare our results to output obtained from more traditional implementation of the OTIS model. We conclude by suggesting best practices for transient-storage modeling and recommend that future applications of TSMs include assessments of parameter certainty to support comparisons and more reliable interpretations of transport processes.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/690444","usgsCitation":"Ward, A.S., Kelleher, C.A., Mason, S.J., Wagener, T., McIntyre, N., McGlynn, B.L., Runkel, R.L., and Payn, R.A., 2017, A software tool to assess uncertainty in transient-storage model parameters using Monte Carlo simulations: Freshwater Science, v. 36, no. 1, p. 195-217, https://doi.org/10.1086/690444.","productDescription":"23 p.","startPage":"195","endPage":"217","ipdsId":"IP-074821","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":461661,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research-information.bris.ac.uk/en/publications/2ec1a71e-046a-4faa-ad85-2f323af51119","text":"External Repository"},{"id":340632,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"590454a1e4b022cee40dc222","contributors":{"authors":[{"text":"Ward, Adam S.","contributorId":11508,"corporation":false,"usgs":true,"family":"Ward","given":"Adam","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":693393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelleher, Christa A.","contributorId":46417,"corporation":false,"usgs":true,"family":"Kelleher","given":"Christa","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":693394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mason, Seth J. K.","contributorId":191535,"corporation":false,"usgs":false,"family":"Mason","given":"Seth","email":"","middleInitial":"J. K.","affiliations":[],"preferred":false,"id":693395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagener, Thorsten","contributorId":176323,"corporation":false,"usgs":false,"family":"Wagener","given":"Thorsten","email":"","affiliations":[],"preferred":false,"id":693396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McIntyre, Neil","contributorId":191602,"corporation":false,"usgs":false,"family":"McIntyre","given":"Neil","email":"","affiliations":[],"preferred":false,"id":693397,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McGlynn, Brian L.","contributorId":83012,"corporation":false,"usgs":true,"family":"McGlynn","given":"Brian","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":693398,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":693392,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Payn, Robert A.","contributorId":36461,"corporation":false,"usgs":true,"family":"Payn","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":693399,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70187205,"text":"70187205 - 2017 - Scale-specific habitat relationships influence patch occupancy: defining neighborhoods to optimize the effectiveness of landscape-scale grassland bird conservation","interactions":[],"lastModifiedDate":"2017-04-26T12:45:06","indexId":"70187205","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Scale-specific habitat relationships influence patch occupancy: defining neighborhoods to optimize the effectiveness of landscape-scale grassland bird conservation","docAbstract":"Context
Beyond the recognized importance of protecting large areas of contiguous habitat, conservation efforts for many species are complicated by the fact that patch suitability may also be affected by characteristics of the landscape within which the patch is located. Currently, little is known about the spatial scales at which species respond to different aspects of the landscape surrounding an occupied patch.
Objectives
Using grassland bird point count data, we describe an approach to evaluating scale-specific effects of landscape composition on patch occupancy.
Methods
We used data from 793 point count surveys conducted in idle and grazed grasslands across Wisconsin, USA from 2012 to 2014 to evaluate scale-dependencies in the response of grassland birds to landscape composition. Patch occupancy models were used to evaluate the relationship between occupancy and landscape composition at scales from 100 to 3000 m.
Results
Bobolink (Dolichonyx oryzivorus) exhibited a pattern indicating selection for grassland habitats in the surrounding landscape at all spatial scales while selecting against other habitats. Eastern Meadowlark (Sturnella magna) displayed evidence of scale sensitivity for all habitat types. Grasshopper Sparrow (Ammodramus savannarum) showed a strong positive response to pasture and idle grass at all scales and negatively to cropland at large scales. Unlike other species, patch occupancy by Henslow’s Sparrow (A. henslowii) was primarily influenced by patch area.
Conclusions
Our results suggest that both working grasslands (pasture) and idle conservation grasslands can play an important role in grassland bird conservation but also highlight the importance of considering species-specific patch and landscape characteristics for effective conservation.
Passive integrated transponder (PIT) tags are commonly used to investigate relationships between individual fish and their environment. The recent availability of smaller tags has provided the opportunity to tag smaller fish. In this study, we implanted 8-mm PIT tags into small Brook Trout Salvelinus fontinalis (35–50 mm FL; 0.35–1.266 g) and compared tag retention, growth rates, and survival of PIT-tagged fish with those of fish subjected to handling only or to handling plus fin clipping. We also examined how initial size at tagging affected absolute and specific growth rates of PIT-tagged individuals over time. We found that survival was 100%, tag retention was 96.7%, and fish size did not vary across treatments at 29 and 64 d posttagging. Additionally, there was no evidence that growth rate (FL or mass) was influenced by the initial size of the fish that were PIT tagged. Our results indicate that retention rates of 8-mm PIT tags surgically implanted into small Brook Trout are high and that there is no discernible effect on growth or survival in the laboratory. The ability to implant smaller PIT tags into smaller fish earlier in the season would allow researchers conducting PIT tag studies to expand demographic models to estimate survival of age-0 fish through the summer of their first year.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2017.1307291","usgsCitation":"O’Donnell, M.J., and Letcher, B.H., 2017, Implanting 8-mm passive integrated transponder tags into small Brook Trout: Effects on growth and survival in the laboratory: North American Journal of Fisheries Management, v. 37, no. 3, p. 605-611, https://doi.org/10.1080/02755947.2017.1307291.","productDescription":"7 p.","startPage":"605","endPage":"611","ipdsId":"IP-076069","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":461675,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02755947.2017.1307291","text":"Publisher Index Page"},{"id":341241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-27","publicationStatus":"PW","scienceBaseUri":"5916c9b4e4b044b359e48696","contributors":{"authors":[{"text":"O’Donnell, Matthew J. 0000-0002-9089-2377 modonnell@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":2003,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Matthew","email":"modonnell@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":694998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Letcher, Benjamin H. 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":149617,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","email":"bletcher@usgs.gov","middleInitial":"H.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":694997,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70187327,"text":"70187327 - 2017 - A decade of insights into grassland ecosystem responses to global environmental change","interactions":[],"lastModifiedDate":"2017-04-28T15:36:26","indexId":"70187327","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5263,"text":"Nature Ecology & Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A decade of insights into grassland ecosystem responses to global environmental change","docAbstract":"Earth’s biodiversity and carbon uptake by plants, or primary productivity, are intricately interlinked, underlie many essential ecosystem processes, and depend on the interplay among environmental factors, many of which are being changed by human activities. While ecological theory generalizes across taxa and environments, most empirical tests of factors controlling diversity and productivity have been observational, single-site experiments, or meta-analyses, limiting our understanding of variation among site-level responses and tests of general mechanisms. A synthesis of results from ten years of a globally distributed, coordinated experiment, the Nutrient Network (NutNet), demonstrates that species diversity promotes ecosystem productivity and stability, and that nutrient supply and herbivory control diversity via changes in composition, including invasions of non-native species and extinction of native species. Distributed experimental networks are a powerful tool for tests and integration of multiple theories and for generating multivariate predictions about the effects of global changes on future ecosystems.","language":"English","publisher":"Nature","doi":"10.1038/s41559-017-0118","usgsCitation":"Borer, E.T., Grace, J.B., Harpole, W., MacDougall, A.S., and Seabloom, E.W., 2017, A decade of insights into grassland ecosystem responses to global environmental change: Nature Ecology & Evolution, v. 1, Article 0118, https://doi.org/10.1038/s41559-017-0118.","productDescription":"Article 0118","ipdsId":"IP-080267","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":340631,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-20","publicationStatus":"PW","scienceBaseUri":"590454a1e4b022cee40dc224","contributors":{"authors":[{"text":"Borer, Elizabeth T.","contributorId":45049,"corporation":false,"usgs":false,"family":"Borer","given":"Elizabeth","email":"","middleInitial":"T.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":693343,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":693342,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harpole, W. Stanley","contributorId":138708,"corporation":false,"usgs":false,"family":"Harpole","given":"W. Stanley","affiliations":[{"id":12468,"text":"Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA","active":true,"usgs":false}],"preferred":false,"id":693344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"MacDougall, Andrew S.","contributorId":39509,"corporation":false,"usgs":true,"family":"MacDougall","given":"Andrew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":693345,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Seabloom, Eric W.","contributorId":60762,"corporation":false,"usgs":false,"family":"Seabloom","given":"Eric","email":"","middleInitial":"W.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":693346,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188346,"text":"70188346 - 2017 - Microbial methane from in situ biodegradation of coal and shale: A review and reevaluation of hydrogen and carbon isotope signatures","interactions":[],"lastModifiedDate":"2017-06-06T16:08:28","indexId":"70188346","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"Microbial methane from in situ biodegradation of coal and shale: A review and reevaluation of hydrogen and carbon isotope signatures","docAbstract":"Stable carbon and hydrogen isotope signatures of methane, water, and inorganic carbon are widely utilized in natural gas systems for distinguishing microbial and thermogenic methane and for delineating methanogenic pathways (acetoclastic, hydrogenotrophic, and/or methylotrophic methanogenesis). Recent studies of coal and shale gas systems have characterized in situ microbial communities and provided stable isotope data (δD-CH4, δD-H2O, δ13C-CH4, and δ13C-CO2) from a wider range of environments than available previously. Here we review the principal biogenic methane-yielding pathways in coal beds and shales and the isotope effects imparted on methane, document the uncertainties and inconsistencies in established isotopic fingerprinting techniques, and identify the knowledge gaps in understanding the subsurface processes that govern H and C isotope signatures of biogenic methane. We also compare established isotopic interpretations with recent microbial community characterization techniques, which reveal additional inconsistencies in the interpretation of microbial metabolic pathways in coal beds and shales. Collectively, the re-assessed data show that widely-utilized isotopic fingerprinting techniques neglect important complications in coal beds and shales.
Isotopic fingerprinting techniques that combine δ13C-CH4 with δD-CH4 and/or δ13C-CO2have significant limitations: (1) The consistent ~ 160‰ offset between δD-H2O and δD-CH4 could imply that hydrogenotrophic methanogenesis is the dominant metabolic pathway in microbial gas systems. However, hydrogen isotopes can equilibrate between methane precursors and coexisting water, yielding a similar apparent H isotope signal as hydrogenotrophic methanogenesis, regardless of the actual methane formation pathway. (2) Non-methanogenic processes such as sulfate reduction, Fe oxide reduction, inputs of thermogenic methane, anaerobic methane oxidation, and/or formation water interaction can cause the apparent carbon isotope fractionation between δ13C-CH4 and δ13C-CO2(α13CCO2-CH4) to differ from the true methanogenic fractionation, complicating interpretation of methanogenic pathways. (3) Where little-fractionating non-methanogenic bacterial processes compete with highly-fractionating methanogenesis, the mass balance between CH4 and CO2 is affected. This has implications for δ13C values and provides an alternative interpretation for net C isotope signatures than solely the pathways used by active methanogens. (4) While most of the reviewed values of δD-H2O - δD-CH4 and α13CCO2-CH4 are apparently consistent with hydrogenotrophic methanogenesis as the dominant pathway in coal beds and shales, recent microbial community characterization techniques suggest a possible role for acetoclastic or methylotrophic methanogenesis in some basins.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2017.01.027","usgsCitation":"Vinson, D.S., Blair, N.E., Martini, A.M., Larter, S., Orem, W.H., and McIntosh, J.C., 2017, Microbial methane from in situ biodegradation of coal and shale: A review and reevaluation of hydrogen and carbon isotope signatures: Chemical Geology, v. 453, p. 128-145, https://doi.org/10.1016/j.chemgeo.2017.01.027.","productDescription":"18 p.","startPage":"128","endPage":"145","ipdsId":"IP-073590","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":469977,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2017.01.027","text":"Publisher Index Page"},{"id":342186,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"453","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5937bf2de4b0f6c2d0d9c756","contributors":{"authors":[{"text":"Vinson, David S.","contributorId":172390,"corporation":false,"usgs":false,"family":"Vinson","given":"David","email":"","middleInitial":"S.","affiliations":[{"id":25392,"text":"Department of Geography and Earth Science, University of North Carolina at Charlotte, North Carolina, USA","active":true,"usgs":false}],"preferred":false,"id":697341,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blair, Neal E.","contributorId":192674,"corporation":false,"usgs":false,"family":"Blair","given":"Neal","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":697342,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martini, Anna M.","contributorId":192675,"corporation":false,"usgs":false,"family":"Martini","given":"Anna","email":"","middleInitial":"M.","affiliations":[{"id":35249,"text":"Department of Geology, Amherst College","active":true,"usgs":false}],"preferred":false,"id":697343,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larter, Steve","contributorId":192676,"corporation":false,"usgs":false,"family":"Larter","given":"Steve","email":"","affiliations":[],"preferred":false,"id":697344,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":697340,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McIntosh, Jennifer C.","contributorId":139870,"corporation":false,"usgs":false,"family":"McIntosh","given":"Jennifer","email":"","middleInitial":"C.","affiliations":[{"id":13301,"text":"Department of Hydrology and Water Resources, University of Arizona, Tucson, Arizona","active":true,"usgs":false}],"preferred":false,"id":697345,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70186553,"text":"70186553 - 2017 - Using diets of Canis breeding pairs to assess resource partitioning between sympatric red wolves and coyotes","interactions":[],"lastModifiedDate":"2017-04-05T16:20:30","indexId":"70186553","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Using diets of Canis breeding pairs to assess resource partitioning between sympatric red wolves and coyotes","docAbstract":"Foraging behaviors of red wolves (Canis rufus) and coyotes (Canis latrans) are complex and their ability to form congeneric breeding pairs and hybridize further complicates our understanding of factors influencing their diets. Through scat analysis, we assessed prey selection of red wolf, coyote, and congeneric breeding pairs formed by red wolves and coyotes, and found that all 3 had similar diets. However, red wolf and congeneric pairs consumed more white-tailed deer (Odocoileus virginianus) than coyote pairs. Coyotes forming breeding pairs with red wolves had 12% more white-tailed deer in their diet than conspecifics paired with coyotes. Contrary to many studies on coyotes in the southeastern United States, we found coyotes in eastern North Carolina to be primarily carnivorous with increased consumption of deer during winter. Although prey selection was generally similar among the 3 groups, differences in diet among different breeding pairs were strongly associated with body mass. Larger breeding pairs consumed more white-tailed deer, and fewer rabbits (Sylvilagus spp.) and other small mammals. Partitioning of food resources by sympatric red wolves and coyotes is likely via differences in the proportions of similar prey consumed, rather than differences in types of prey exploited. Consequently, our results suggest coexistence of red wolves and coyotes in the southeastern United States may not be possible because there are limited opportunities for niche partitioning to reduce competitive interactions.
","language":"English","publisher":"American Society of Mammalogists","doi":"10.1093/jmammal/gyw233","usgsCitation":"Hinton, J.W., Ashley, A.K., Dellinger, J.A., Gittleman, J.L., van Manen, F.T., and Chamberlain, M.J., 2017, Using diets of Canis breeding pairs to assess resource partitioning between sympatric red wolves and coyotes: Journal of Mammalogy, v. 98, no. 2, p. 475-488, https://doi.org/10.1093/jmammal/gyw233.","productDescription":"14 p.","startPage":"475","endPage":"488","ipdsId":"IP-079451","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":339274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"98","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-09","publicationStatus":"PW","scienceBaseUri":"58e60271e4b09da6799ac67d","contributors":{"authors":[{"text":"Hinton, Joseph W.","contributorId":179346,"corporation":false,"usgs":false,"family":"Hinton","given":"Joseph","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":688723,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ashley, Annaliese K.","contributorId":190531,"corporation":false,"usgs":false,"family":"Ashley","given":"Annaliese","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":688724,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dellinger, Justin A.","contributorId":190532,"corporation":false,"usgs":false,"family":"Dellinger","given":"Justin","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":688725,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gittleman, John L.","contributorId":190533,"corporation":false,"usgs":false,"family":"Gittleman","given":"John","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":688726,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":688722,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chamberlain, Michael J.","contributorId":179350,"corporation":false,"usgs":false,"family":"Chamberlain","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":688727,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70187202,"text":"70187202 - 2017 - Mitigating future avian malaria threats to Hawaiian forest birds from climate change","interactions":[],"lastModifiedDate":"2017-04-26T13:12:50","indexId":"70187202","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Mitigating future avian malaria threats to Hawaiian forest birds from climate change","docAbstract":"Avian malaria, transmitted by Culex quinquefasciatus mosquitoes in the Hawaiian Islands, has been a primary contributor to population range limitations, declines, and extinctions for many endemic Hawaiian honeycreepers. Avian malaria is strongly influenced by climate; therefore, predicted future changes are expected to expand transmission into higher elevations and intensify and lengthen existing transmission periods at lower elevations, leading to further population declines and potential extinction of highly susceptible honeycreepers in mid- and high-elevation forests. Based on future climate changes and resulting malaria risk, we evaluated the viability of alternative conservation strategies to preserve endemic Hawaiian birds at mid and high elevations through the 21st century. We linked an epidemiological model with three alternative climatic projections from the Coupled Model Intercomparison Project to predict future malaria risk and bird population dynamics for the coming century. Based on climate change predictions, proposed strategies included mosquito population suppression using modified males, release of genetically modified refractory mosquitoes, competition from other introduced mosquitoes that are not competent vectors, evolved malaria-tolerance in native honeycreepers, feral pig control to reduce mosquito larval habitats, and predator control to improve bird demographics. Transmission rates of malaria are predicted to be higher than currently observed and are likely to have larger impacts in high-elevation forests where current low rates of transmission create a refuge for highly-susceptible birds. As a result, several current and proposed conservation strategies will be insufficient to maintain existing forest bird populations. We concluded that mitigating malaria transmission at high elevations should be a primary conservation goal. Conservation strategies that maintain highly susceptible species like Iiwi (Drepanis coccinea) will likely benefit other threatened and endangered Hawai’i species, especially in high-elevation forests. Our results showed that mosquito control strategies offer potential long-term benefits to high elevation Hawaiian honeycreepers. However, combined strategies will likely be needed to preserve endemic birds at mid elevations. Given the delay required to research, develop, evaluate, and improve several of these currently untested conservation strategies we suggest that planning should begin expeditiously.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0168880","usgsCitation":"Liao, W., Atkinson, C.T., LaPointe, D., and Samuel, M.D., 2017, Mitigating future avian malaria threats to Hawaiian forest birds from climate change: PLoS ONE, v. 12, no. 1, p. 1-25, https://doi.org/10.1371/journal.pone.0168880.","productDescription":"e0168880; 25 p.","startPage":"1","endPage":"25","ipdsId":"IP-075370","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":469962,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0168880","text":"Publisher Index Page"},{"id":340462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-06","publicationStatus":"PW","scienceBaseUri":"5901b1b9e4b0c2e071a99b90","contributors":{"authors":[{"text":"Liao, Wei","contributorId":147740,"corporation":false,"usgs":false,"family":"Liao","given":"Wei","email":"","affiliations":[{"id":13018,"text":"Department of Forest and Wildlife Ecology, University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":693050,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atkinson, Carter T. 0000-0002-4232-5335 catkinson@usgs.gov","orcid":"https://orcid.org/0000-0002-4232-5335","contributorId":1124,"corporation":false,"usgs":true,"family":"Atkinson","given":"Carter","email":"catkinson@usgs.gov","middleInitial":"T.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":693051,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaPointe, Dennis dlapointe@usgs.gov","contributorId":2926,"corporation":false,"usgs":true,"family":"LaPointe","given":"Dennis","email":"dlapointe@usgs.gov","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":false,"id":693052,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Samuel, Michael D. msamuel@usgs.gov","contributorId":1419,"corporation":false,"usgs":true,"family":"Samuel","given":"Michael","email":"msamuel@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":693012,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188157,"text":"70188157 - 2017 - The role of uplift and erosion in the persistence of saline groundwater in the shallow subsurface","interactions":[],"lastModifiedDate":"2018-03-29T11:22:31","indexId":"70188157","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The role of uplift and erosion in the persistence of saline groundwater in the shallow subsurface","docAbstract":"In many regions of the world, the shallow (<300 m) subsurface is replenished with meteoric recharge within a few centuries or millennia, but in some regions saline groundwater persists despite abundant rainfall. Analyses of the flushing rate of shallow groundwater usually consider the permeability and recharge rate and a static landscape. The influence of landscape evolution can become important over millions of years, however. Here we present numerical simulations of fluid flow and transport in the top 1 km of a sedimentary foreland basin dominated by aquitards, where the rate of uplift and erosion (20 m Ma−1) balances that of meteoric flushing. Paleozoic age saline groundwater and brine persist at shallow depths that might otherwise have contained potable water. Similar hydrogeologic conditions, and uplift and erosion rates, likely exist in many other regions of the world, where a moving landscape has probably never been considered as an important contributor to groundwater quality.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017GL072980","usgsCitation":"Yager, R.M., McCoy, K.J., Voss, C.I., Sanford, W.E., and Winston, R.B., 2017, The role of uplift and erosion in the persistence of saline groundwater in the shallow subsurface: Geophysical Research Letters, v. 44, no. 8, p. 3672-3681, https://doi.org/10.1002/2017GL072980.","productDescription":"10 p.","startPage":"3672","endPage":"3681","ipdsId":"IP-074589","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":469971,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017gl072980","text":"Publisher Index Page"},{"id":352930,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"8","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-30","publicationStatus":"PW","scienceBaseUri":"5afee8abe4b0da30c1bfc48d","contributors":{"authors":[{"text":"Yager, Richard M. 0000-0001-7725-1148 ryager@usgs.gov","orcid":"https://orcid.org/0000-0001-7725-1148","contributorId":950,"corporation":false,"usgs":true,"family":"Yager","given":"Richard","email":"ryager@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":696942,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCoy, Kurt J. 0000-0002-9756-8238 kjmccoy@usgs.gov","orcid":"https://orcid.org/0000-0002-9756-8238","contributorId":1391,"corporation":false,"usgs":true,"family":"McCoy","given":"Kurt","email":"kjmccoy@usgs.gov","middleInitial":"J.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":696943,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Voss, Clifford I. 0000-0001-5923-2752 cvoss@usgs.gov","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":1559,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford","email":"cvoss@usgs.gov","middleInitial":"I.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":696944,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanford, Ward E. 0000-0002-6624-0280 wsanford@usgs.gov","orcid":"https://orcid.org/0000-0002-6624-0280","contributorId":2268,"corporation":false,"usgs":true,"family":"Sanford","given":"Ward","email":"wsanford@usgs.gov","middleInitial":"E.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":696945,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Winston, Richard B. 0000-0002-6287-8834 rbwinst@usgs.gov","orcid":"https://orcid.org/0000-0002-6287-8834","contributorId":3567,"corporation":false,"usgs":true,"family":"Winston","given":"Richard","email":"rbwinst@usgs.gov","middleInitial":"B.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":696946,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70182811,"text":"70182811 - 2017 - Inner-shelf ocean dynamics and seafloor morphologic changes during Hurricane Sandy","interactions":[],"lastModifiedDate":"2018-07-23T12:49:07","indexId":"70182811","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1333,"text":"Continental Shelf Research","active":true,"publicationSubtype":{"id":10}},"title":"Inner-shelf ocean dynamics and seafloor morphologic changes during Hurricane Sandy","docAbstract":"Hurricane Sandy was one of the most destructive hurricanes in US history, making landfall on the New Jersey coast on Oct 30, 2012. Storm impacts included several barrier island breaches, massive coastal erosion, and flooding. While changes to the subaerial landscape are relatively easily observed, storm-induced changes to the adjacent shoreface and inner continental shelf are more difficult to evaluate. These regions provide a framework for the coastal zone, are important for navigation, aggregate resources, marine ecosystems, and coastal evolution. Here we provide unprecedented perspective regarding regional inner continental shelf sediment dynamics based on both observations and numerical modeling over time scales associated with these types of large storm events. Oceanographic conditions and seafloor morphologic changes are evaluated using both a coupled atmospheric-ocean-wave-sediment numerical modeling system and observation analysis from a series of geologic surveys and oceanographic instrument deployments focused on a region offshore of Fire Island, NY. The geologic investigations conducted in 2011 and 2014 revealed lateral movement of sedimentary structures of distances up to 450 m and in water depths up to 30 m, and vertical changes in sediment thickness greater than 1 m in some locations. The modeling investigations utilize a system with grid refinement designed to simulate oceanographic conditions with progressively increasing resolutions for the entire US East Coast (5-km grid), the New York Bight (700-m grid), and offshore of Fire Island, NY (100-m grid), allowing larger scale dynamics to drive smaller scale coastal changes. Model results in the New York Bight identify maximum storm surge of up to 3 m, surface currents on the order of 2 ms-1 along the New Jersey coast, waves up to 8 m in height, and bottom stresses exceeding 10 Pa. Flow down the Hudson Shelf Valley is shown to result in convergent sediment transport and deposition along its axis. Modeled sediment redistribution along Fire Island showed erosion across the crests of inner shelf sand ridges and sedimentation in adjacent troughs, consistent with the geologic observations.","language":"English","publisher":"Elsevier ","doi":"10.1016/j.csr.2017.02.003","usgsCitation":"Warner, J., Schwab, W.C., List, J.H., Safak, I., Liste, M., and Baldwin, W.E., 2017, Inner-shelf ocean dynamics and seafloor morphologic changes during Hurricane Sandy: Continental Shelf Research, v. 138, p. 1-18, https://doi.org/10.1016/j.csr.2017.02.003.","productDescription":"18 p.","startPage":"1","endPage":"18","ipdsId":"IP-072875","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":469976,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.csr.2017.02.003","text":"Publisher Index Page"},{"id":336758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"138","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58e60271e4b09da6799ac67f","chorus":{"doi":"10.1016/j.csr.2017.02.003","url":"http://dx.doi.org/10.1016/j.csr.2017.02.003","publisher":"Elsevier BV","authors":"Warner John C., Schwab William C., List Jeffrey H., Safak Ilgar, Liste Maria, Baldwin Wayne","journalName":"Continental Shelf Research","publicationDate":"4/2017"},"contributors":{"authors":[{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":2681,"corporation":false,"usgs":true,"family":"Warner","given":"John C.","email":"jcwarner@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":673847,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwab, William C. 0000-0001-9274-5154 bschwab@usgs.gov","orcid":"https://orcid.org/0000-0001-9274-5154","contributorId":417,"corporation":false,"usgs":true,"family":"Schwab","given":"William","email":"bschwab@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":673848,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"List, Jeffrey H. 0000-0001-8594-2491 jlist@usgs.gov","orcid":"https://orcid.org/0000-0001-8594-2491","contributorId":174581,"corporation":false,"usgs":true,"family":"List","given":"Jeffrey","email":"jlist@usgs.gov","middleInitial":"H.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":673849,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Safak, Ilgar 0000-0001-7675-0770 isafak@usgs.gov","orcid":"https://orcid.org/0000-0001-7675-0770","contributorId":5522,"corporation":false,"usgs":true,"family":"Safak","given":"Ilgar","email":"isafak@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":673850,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liste, Maria","contributorId":190581,"corporation":false,"usgs":false,"family":"Liste","given":"Maria","email":"","affiliations":[],"preferred":false,"id":673851,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baldwin, Wayne E. 0000-0001-5886-0917 wbaldwin@usgs.gov","orcid":"https://orcid.org/0000-0001-5886-0917","contributorId":1321,"corporation":false,"usgs":true,"family":"Baldwin","given":"Wayne","email":"wbaldwin@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":673852,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70185241,"text":"70185241 - 2017 - 2010-2015 Juvenile fish ecology in the Nisqually River Delta and Nisqually Reach Aquatic Reserve","interactions":[],"lastModifiedDate":"2017-04-07T10:58:21","indexId":"70185241","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":5367,"text":"Salmon Recovery Program Technical Report","active":true,"publicationSubtype":{"id":9}},"seriesNumber":"2016-1","title":"2010-2015 Juvenile fish ecology in the Nisqually River Delta and Nisqually Reach Aquatic Reserve","docAbstract":"The return of tidal inundation to over 750 acres of the U. S. Fish and Wildlife Service Billy Frank Jr. Nisqually National Wildlife Refuge (NNWR) in fall of 2009 was the crowning moment in the effort to protect and restore the Nisqually Delta. The Nisqually NWR project complemented three earlier restoration projects completed by the Nisqually Indian Tribe (Tribe) on tribal property to restore over 900 acres of the estuary, representing the largest estuary restoration project in the Pacific Northwest and one of the most significant advances to date towards the recovery of Puget Sound (USFWS 2005).
In 2011 the Washington Department of Natural Resources (WADNR established the over 14000 acre Nisqually Reach Aquatic Reserve (Reserve), complementing the protection and restoration successes in the Nisqually Delta. The Reserve includes all state-owned aquatic lands around Anderson, Ketron and Eagle islands and part of McNeil Island (Figure 1, WDNR 2011). The Reserve also includes a diverse assemblage of nearshore and offshore habitats important to resident and migratory fish including federal endangered species act listed fish like Chinook salmon (Oncorynchus tshawytscha) and steelhead (O. mykiss). Studies in the Nisqually Estuary (Ellings and Hodgson 2007, David et al. 2014, Ellings et al. 2016) and South Puget Sound (Duffy 2003) have summarized fish use of the area. However, the fish ecology of the reserve had not been systematically surveyed.
The Tribe, U.S. Geological Survey (USGS), NNWR, Nisqually River Foundation (NRF), and others are currently conducting a multi-year, interdisciplinary, hypothesis-based research and monitoring study investigating the impact of delta restoration on estuarine processes, habitat structures, and functions. Our interdisciplinary monitoring framework enables us to link key estuarine processes with habitat development and biological response at multiple scales across the restored footprint, reference marshes, and throughout the Nisqually Reach. Key research components include hydrology and sediment regime, channel and marsh topography and development, vegetation colonization, and invertebrate, bird, and fish abundance, habitat use, and foraging ecology. After the Reserve was established, the WDNR and the research partnership led by the Tribe expanded the existing delta fish ecology assessment to include sampling stations throughout the Reserve. The results of the Reserve fish ecology assessment provide a unique regional analysis of fish ecology from the Nisqually River to McNeil Island.
","language":"English","publisher":"Nisqually Indian Tribe, Department of Natural Resources","usgsCitation":"Hodgson, S., Ellings, C.S., Rubin, S.P., Hayes, M.C., Duval, W., and Grossman, E., 2017, 2010-2015 Juvenile fish ecology in the Nisqually River Delta and Nisqually Reach Aquatic Reserve: Salmon Recovery Program Technical Report 2016-1, 40 p.","productDescription":"40 p.","ipdsId":"IP-082522","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":339410,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":337772,"type":{"id":15,"text":"Index Page"},"url":"https://hws.ekosystem.us/project/220/15393"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58e8a541e4b09da6799d63a1","contributors":{"authors":[{"text":"Hodgson, Sayre","contributorId":172121,"corporation":false,"usgs":false,"family":"Hodgson","given":"Sayre","email":"","affiliations":[{"id":26985,"text":"Nisqually Indian Tribe, Olympia, WA","active":true,"usgs":false}],"preferred":false,"id":684842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellings, Christopher S.","contributorId":149343,"corporation":false,"usgs":false,"family":"Ellings","given":"Christopher","email":"","middleInitial":"S.","affiliations":[{"id":17711,"text":"Dep't Natural Resources, Nisqually Indian Tribe, Olympia, WA","active":true,"usgs":false}],"preferred":false,"id":684843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rubin, Steve P. 0000-0003-3054-7173 srubin@usgs.gov","orcid":"https://orcid.org/0000-0003-3054-7173","contributorId":3018,"corporation":false,"usgs":true,"family":"Rubin","given":"Steve","email":"srubin@usgs.gov","middleInitial":"P.","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":684841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayes, Michael C. 0000-0002-9060-0565 mhayes@usgs.gov","orcid":"https://orcid.org/0000-0002-9060-0565","contributorId":3017,"corporation":false,"usgs":true,"family":"Hayes","given":"Michael","email":"mhayes@usgs.gov","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":684844,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duval, Walker","contributorId":189437,"corporation":false,"usgs":false,"family":"Duval","given":"Walker","affiliations":[],"preferred":false,"id":684845,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grossman, Eric E. 0000-0003-0269-6307 egrossman@usgs.gov","orcid":"https://orcid.org/0000-0003-0269-6307","contributorId":2334,"corporation":false,"usgs":true,"family":"Grossman","given":"Eric E.","email":"egrossman@usgs.gov","affiliations":[],"preferred":false,"id":684846,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70192086,"text":"70192086 - 2017 - Circumpolar analysis of the Adélie Penguin reveals the importance of environmental variability in phenological mismatch","interactions":[],"lastModifiedDate":"2017-10-19T15:19:36","indexId":"70192086","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Circumpolar analysis of the Adélie Penguin reveals the importance of environmental variability in phenological mismatch","docAbstract":"Evidence of climate-change-driven shifts in plant and animal phenology have raised concerns that certain trophic interactions may be increasingly mismatched in time, resulting in declines in reproductive success. Given the constraints imposed by extreme seasonality at high latitudes and the rapid shifts in phenology seen in the Arctic, we would also expect Antarctic species to be highly vulnerable to climate-change-driven phenological mismatches with their environment. However, few studies have assessed the impacts of phenological change in Antarctica. Using the largest database of phytoplankton phenology, sea-ice phenology, and Adélie Penguin breeding phenology and breeding success assembled to date, we find that, while a temporal match between Penguin breeding phenology and optimal environmental conditions sets an upper limit on breeding success, only a weak relationship to the mean exists. Despite previous work suggesting that divergent trends in Adélie Penguin breeding phenology are apparent across the Antarctic continent, we find no such trends. Furthermore, we find no trend in the magnitude of phenological mismatch, suggesting that mismatch is driven by interannual variability in environmental conditions rather than climate-change-driven trends, as observed in other systems. We propose several criteria necessary for a species to experience a strong climate-change-driven phenological mismatch, of which several may be violated by this system.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.1749","usgsCitation":"Youngflesh, C., Jenouvrier, S., Li, Y., Ji, R., Ainley, D.G., Ballard, G., Barbraud, C., Delord, K., Dugger, K., Emmerson, L.M., Fraser, W.R., Hinke, J.T., Lyver, P.O., Olmastroni, S., Southwell, C.J., Trivelpiece, S.G., Trivelpiece, W.Z., and Lynch, H.J., 2017, Circumpolar analysis of the Adélie Penguin reveals the importance of environmental variability in phenological mismatch: Ecology, v. 98, no. 4, p. 940-951, https://doi.org/10.1002/ecy.1749.","productDescription":"12 p.","startPage":"940","endPage":"951","ipdsId":"IP-076568","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469974,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1912/8890","text":"External Repository"},{"id":346997,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica","volume":"98","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-20","publicationStatus":"PW","scienceBaseUri":"59e9b995e4b05fe04cd65c97","contributors":{"authors":[{"text":"Youngflesh, Casey","contributorId":197697,"corporation":false,"usgs":false,"family":"Youngflesh","given":"Casey","email":"","affiliations":[],"preferred":false,"id":714111,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenouvrier, Stephanie","contributorId":197698,"corporation":false,"usgs":false,"family":"Jenouvrier","given":"Stephanie","email":"","affiliations":[],"preferred":false,"id":714112,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Yun","contributorId":197732,"corporation":false,"usgs":false,"family":"Li","given":"Yun","affiliations":[],"preferred":false,"id":714113,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ji, Rubao","contributorId":197699,"corporation":false,"usgs":false,"family":"Ji","given":"Rubao","affiliations":[],"preferred":false,"id":714114,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ainley, David G.","contributorId":32039,"corporation":false,"usgs":false,"family":"Ainley","given":"David","email":"","middleInitial":"G.","affiliations":[{"id":34154,"text":"Point Reyes Bird Observatory, Stinson Beach, CA","active":true,"usgs":false}],"preferred":false,"id":714115,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ballard, Grant","contributorId":197700,"corporation":false,"usgs":false,"family":"Ballard","given":"Grant","email":"","affiliations":[],"preferred":false,"id":714116,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Barbraud, 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alteration","interactions":[],"lastModifiedDate":"2025-12-23T14:37:28.701523","indexId":"70192945","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Classification of California streams using combined deductive and inductive approaches: Setting the foundation for analysis of hydrologic alteration","docAbstract":"Regional classification of streams is an early step in the Ecological Limits of Hydrologic Alteration framework. Many stream classifications are based on an inductive approach using hydrologic data from minimally disturbed basins, but this approach may underrepresent streams from heavily disturbed basins or sparsely gaged arid regions. An alternative is a deductive approach, using watershed climate, land use, and geomorphology to classify streams, but this approach may miss important hydrological characteristics of streams. We classified all stream reaches in California using both approaches. First, we used Bayesian and hierarchical clustering to classify reaches according to watershed characteristics. Streams were clustered into seven classes according to elevation, sedimentary rock, and winter precipitation. Permutation-based analysis of variance and random forest analyses were used to determine which hydrologic variables best separate streams into their respective classes. Stream typology (i.e., the class that a stream reach is assigned to) is shaped mainly by patterns of high and mean flow behavior within the stream's landscape context. Additionally, random forest was used to determine which hydrologic variables best separate minimally disturbed reference streams from non-reference streams in each of the seven classes. In contrast to stream typology, deviation from reference conditions is more difficult to detect and is largely defined by changes in low-flow variables, average daily flow, and duration of flow. Our combined deductive/inductive approach allows us to estimate flow under minimally disturbed conditions based on the deductive analysis and compare to measured flow based on the inductive analysis in order to estimate hydrologic change.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.1802","usgsCitation":"Pyne, M.I., Carlisle, D.M., Konrad, C.P., and Stein, E.D., 2017, Classification of California streams using combined deductive and inductive approaches: Setting the foundation for analysis of hydrologic alteration: Ecohydrology, v. 10, no. 3, e1802, 14 p; Data Release, https://doi.org/10.1002/eco.1802.","productDescription":"e1802, 14 p; Data Release","ipdsId":"IP-073147","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":348846,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70R9MJ7","text":"USGS data release","description":"USGS data release","linkHelpText":"Select watershed attributes for California stream segments (NHDPlus V.1)"},{"id":348662,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Water Science Center","active":false,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":717395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Konrad, Christopher P. 0000-0002-7354-547X cpkonrad@usgs.gov","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":1716,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","email":"cpkonrad@usgs.gov","middleInitial":"P.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":717397,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stein, Eric D.","contributorId":198848,"corporation":false,"usgs":false,"family":"Stein","given":"Eric","email":"","middleInitial":"D.","affiliations":[{"id":12704,"text":"Southern California Coastal Water Research Project","active":true,"usgs":false}],"preferred":false,"id":717398,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191950,"text":"70191950 - 2017 - Earning their stripes: The potential of tiger trout and other salmonids as biological controls of forage fishes in a western reservoir","interactions":[],"lastModifiedDate":"2017-10-19T11:32:22","indexId":"70191950","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Earning their stripes: The potential of tiger trout and other salmonids as biological controls of forage fishes in a western reservoir","docAbstract":"Maintaining a balance between predator and prey populations can be an ongoing challenge for fisheries managers, especially in managing artificial ecosystems such as reservoirs. In a high-elevation Utah reservoir, the unintentional introduction of the Utah Chub Gila atraria and its subsequent population expansion prompted managers to experimentally shift from exclusively stocking Rainbow Trout Oncorhynchus mykiss to also stocking tiger trout (female Brown Trout Salmo trutta × male Brook Trout Salvelinus fontinalis) and Bonneville Cutthroat Trout O. clarkii utah (hereafter, Cutthroat Trout) as potential biological control agents. We measured a combination of diet, growth, temperature, and abundance and used bioenergetic simulations to quantify predator demand versus prey supply. Utah Chub were the predominant prey type for tiger trout, contributing up to 80% of the diet depending on the season. Utah Chub represented up to 70% of the total diet consumed by Cutthroat Trout. Although Utah Chub dominated the fish biomass in the reservoir, we still estimated abundances of 238,000 tiger trout, 214,000 Cutthroat Trout, and 55,000 Rainbow Trout. Consequently, when expanded to the population level of each predator, tiger trout and Cutthroat Trout consumed large quantities of Utah Chub on an annual basis: tiger trout consumed 508,000 kg (2,660 g/predator) of the standing prey population, and Cutthroat Trout consumed an estimated 322,000 kg (1,820 g/predator). The estimated combined consumption by Cutthroat Trout and tiger trout exceeded the estimate of Utah Chub annual production. As such, our results suggest that the high rates of piscivory exhibited by Cutthroat Trout and tiger trout in artificial lentic ecosystems are likely sufficient to effectively reduce the overall abundance of forage fishes and to prevent forage fishes from dominating fish assemblages. Collectively, this research provides the first documented findings on tiger trout ecology and performance, which will aid managers in designing and implementing the best stocking strategy to optimize sport fish performance, control undesirable forage fish, and enhance and maintain angler satisfaction.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2016.1264509","usgsCitation":"Winters, L.K., Budy, P., and Thiede, G.P., 2017, Earning their stripes: The potential of tiger trout and other salmonids as biological controls of forage fishes in a western reservoir: North American Journal of Fisheries Management, v. 37, no. 2, p. 380-394, https://doi.org/10.1080/02755947.2016.1264509.","productDescription":"15 p.","startPage":"380","endPage":"394","ipdsId":"IP-074773","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":346956,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Scofield Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.1952018737793,\n 39.74045836300404\n ],\n [\n -111.11160278320312,\n 39.74045836300404\n ],\n [\n -111.11160278320312,\n 39.819743927696756\n ],\n [\n -111.1952018737793,\n 39.819743927696756\n ],\n [\n -111.1952018737793,\n 39.74045836300404\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-08","publicationStatus":"PW","scienceBaseUri":"59e9b995e4b05fe04cd65c9f","contributors":{"authors":[{"text":"Winters, Lisa K.","contributorId":171640,"corporation":false,"usgs":false,"family":"Winters","given":"Lisa","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":713773,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budy, Phaedra E. 0000-0002-9918-1678 pbudy@usgs.gov","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":140028,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra","email":"pbudy@usgs.gov","middleInitial":"E.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":713772,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thiede, Gary P.","contributorId":9154,"corporation":false,"usgs":true,"family":"Thiede","given":"Gary","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":713774,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204148,"text":"70204148 - 2017 - 2016 status of the Lake Ontario Lower Trophic levels","interactions":[],"lastModifiedDate":"2019-09-24T08:47:40","indexId":"70204148","displayToPublicDate":"2017-03-31T13:15:24","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"16","displayTitle":"2016 Status of the Lake Ontario Lower Trophic Levels","title":"2016 status of the Lake Ontario Lower Trophic levels","docAbstract":"Significant Findings for Year 2016:\n1) Offshore spring total phosphorus (TP) in 2016 was 6.2 μg/L, higher than 2014 and 2015 (4.0 and 4.2 μg/L); there was no significant decline 2001 - 2016. Offshore soluble reactive phosphorus (SRP) was very low in 2016; Apr/May – Oct mean values were <1 μg/L. SRP has been stable in nearshore and offshore habitats since 1998 (range, 0.4 – 3.3 μg/L). Apr/May – Oct mean TP concentrations were low at both nearshore and offshore locations (range 5.2 – 9.9 μg/L). TP and SRP concentrations were significantly higher in nearshore compared to offshore habitats (7.6 μg/L vs 6.0 μg/L, TP; 1.4 μg/L vs 0.8 μg/L, SRP).\n2) Chlorophyll-a and Secchi depth values are indicative of oligotrophic conditions in nearshore and offshore habitats. Offshore summer chlorophyll-a declined significantly 2000 - 2016. Nearshore chlorophyll-a increased 1995 - 2004 but then declined 2005 - 2016. Epilimnetic chlorophyll-a averaged between 1.4 and 2.5 μg/L across sites, and offshore and nearshore Apr/May – Oct concentrations were the same (1.9 μg/L). Summer Secchi depth increased significantly in the offshore 2000 - 2016 and showed no trend in the nearshore, 1995 – 2016. Apr/May – Oct Secchi depth ranged from 5.0 m to 13.0 m at individual sites and was significantly higher in the offshore (10.0 m) than nearshore (6.5 m).\n3) In 2016, Apr/May – Oct epilimnetic zooplankton density and biomass were not different between the offshore and the nearshore, but calanoid copepod and Limnocalanus biomass were higher in the offshore (4.7 mg/m3 vs 2.6 mg/m3 and 0.7 mg/m3 vs 0.1 mg/m3), and bosminid biomass was higher in the nearshore (1.1 mg/m3 vs 0.3 mg/m3). Zooplankton size was significantly higher in the offshore than the nearshore (0.66 mm vs 0.49 mm).\n4) Peak (July) epilimnetic biomass of Cercopagis was 1.0 mg/m3 in the nearshore and 1.4 mg/m3 in the offshore. Peak (October) epilimnetic biomass of Bythotrephes was 1.8 mg/m3 in the nearshore and 0.6 mg/m3 in the offshore. Bythotrephes biomass has increased significantly in the nearshore, 1995 – 2016. Bythotrephes was more abundant in 2016 than in the previous two years and the zooplankton community responded accordingly with a decrease in bosminds and cyclopoids.\n5) Summer nearshore zooplankton density and biomass declined significantly 1995 – 2004 and then remained stable 2005 – 2016. The decline was due to reductions in bosminids and cyclopoids.\n6) Summer epilimnetic offshore zooplankton density and biomass increased significantly 2005 – 2016. In 2016, offshore summer epilimnetic zooplankton biomass was 22 mg/m3--less than half that observed in 2015--but still slightly higher than the mean from 2005 – 2015 (21 mg/m3).\n7) Most offshore zooplankton biomass was found in the metalimnion in July and September, and in the hypolimnion in October. Limnocalanus dominated the metalimnion in July while other calanoids and daphnids comprised most of the biomass in September. Limnocalanus and other calanoids dominated the October hypolimnion. Whole water column samples taken show a stable zooplankton biomass but changing community composition since 2010. Cyclopoids increased 2013 – 2015 and declined in 2016, while the calanoid pattern was the opposite. Daphnids declined 2014 – 2015 but rebounded in 2016.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Annual Report Bureau of Fisheries Lake Ontario Unit and St. Lawrence River Unit to Great Lakes Fishery Commission’s Lake Ontario Committee.","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"New York State Department of Environmental Conservation","usgsCitation":"Holeck, K.T., Lars G. 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Since 1990, cyanobacterial blooms have been present occasionally in Cheney Reservoir, resulting in increased treatment costs and decreased recreational use. Cyanobacteria, the cyanotoxin microcystin, and the taste-and-odor compounds geosmin and 2-methylisoborneol have been measured in Cheney Reservoir by the U.S. Geological Survey, in cooperation with the city of Wichita, for about 16 years. The purpose of this report is to describe the occurrence of cyanobacteria, microcystin, and taste-and-odor compounds in Cheney Reservoir during May 2001 through June 2016 and to update previously published logistic regression models that used continuous water-quality data to estimate the probability of microcystin and geosmin occurrence above relevant thresholds.
Cyanobacteria, microcystin, and geosmin were detected in about 84, 52, and 31 percent of samples collected in Cheney Reservoir during May 2001 through June 2016, respectively. 2-methylisoborneol was less common, detected in only 3 percent of samples. Microcystin and geosmin concentrations exceeded advisory values of concern more frequently than cyanobacterial abundance; therefore, cyanobacteria are not a good indicator of the presence of these taste-and-odor compounds in Cheney Reservoir. Broad seasonal patterns in cyanobacteria and microcystin were evident, though abundance and concentration varied by orders of magnitude across years. Cyanobacterial abundances generally peaked in late summer or early fall (August through October), and smaller peaks were observed in winter (January through February). In a typical year, microcystin was first detected in June or July, increased to its seasonal maxima in the summer (July through September), and then decreased. Seasonal patterns in geosmin were less consistent than cyanobacteria and microcystin, but geosmin typically had a small peak during winter (January through March) during most years and a large peak during summer (July through September) during some years. Though the relation between cyanobacterial abundance and microcystin and geosmin concentrations was positive, overall correlations were weak, likely because production is strain-specific and cyanobacterial strain composition may vary substantially over time. Microcystin often was present without taste-and-odor compounds. By comparison, where taste-and-odor compounds were present, microcystin frequently was detected. Taste-and-odor compounds, therefore, may be used as indicators that microcystin may be present; however, microcystin was present without taste-and-odor compounds, so taste or odor alone does not provide sufficient warning to ensure human-health protection.
Logistic regression models that estimate the probability of microcystin occurrence at concentrations greater than or equal to 0.1 micrograms per liter and geosmin occurrence at concentrations greater than or equal to 5 nanograms per liter were developed. Models were developed using the complete dataset (January 2003 through June 2016 for microcystin [14-year dataset]; May 2001 through June 2016 for geosmin [16-year dataset]) and an abbreviated 4-year dataset (January 2013 through June 2016 for microcystin and geosmin). Performance of the newly developed models was compared with previously published models that were developed using data collected during May 2001 through December 2009. A seasonal component and chlorophyll fluorescence (a surrogate for algal biomass) were the explanatory variables for microcystin occurrence at concentrations greater than or equal to 0.1 micrograms per liter in all models. All models were relatively robust, though the previously published and 14-year models performed better over time; however, as a tool to estimate microcystin occurrence at concentrations greater than or equal to 0.1 micrograms per liter in a real-time notification system near the Cheney Dam, the 4-year model is most representative of recent (2013 through 2016) conditions. All models for geosmin occurrence at concentrations greater than or equal to 5 nanograms per liter had different explanatory variables and model forms. The previously published and 16-year models were not robust over time, likely because of changing environmental conditions and seasonal patterns in geosmin occurrence. By comparison, the abbreviated 4-year model may be a useful tool to estimate geosmin occurrence at concentrations greater than or equal to 5 nanograms per liter in a real-time notification system near the Cheney Dam. The better performance of the abbreviated 4-year geosmin model during 2013 through 2016 relative to the previously published and 16-year models demonstrates the need for continuous reevaluation of models estimating the probability of occurrence.
Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
Since 1996, the U.S. Geological Survey (USGS), in cooperation with the City of Wichita, has done studies in the Cheney Reservoir watershed to understand environmental effects on water-quality conditions. Early studies (1996–2001) determined subwatershed sources of contaminants, nutrient and sediment loading to Cheney Reservoir, changes in reservoir sediment quality over time, and watershed sources of phosphorus. Later studies (2001–present) focused on nutrient and sediment concentrations and mass transport from the watershed; the presence of cyanobacteria, cyanotoxins, and taste-and-odor compounds in the reservoir; and development of regression models for real-time computations of water-quality constituents of interest that may affect drinking-water treatment. This fact sheet summarizes key results from studies done by the USGS during 1996–2016 in the Cheney Reservoir watershed and Cheney Reservoir.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173019","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Graham, J.L., Foster, G.M., and Kramer, A.R., 2017, Twenty years of water-quality studies in the Cheney Reservoir watershed, Kansas, 1996–2016: U.S. Geological Survey Fact Sheet 2017–3019, 4 p., https://doi.org/10.3133/fs20173019.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-080288","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":338840,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3019/fs20173019.pdf","text":"Fact Sheet","size":"1.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3019"},{"id":338839,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3019/coverthb.jpg"},{"id":338841,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20175016","text":"SIR 2017–5016","description":"SIR 2017–5016","linkHelpText":"Occurrence of cyanobacteria, microcystin, and taste-and-odor compounds in Cheney Reservoir, Kansas, 2001-16"}],"country":"United States","state":"Kansas","otherGeospatial":"Cheney Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.74,\n 38.1\n ],\n [\n -99.25,\n 38.1\n ],\n [\n -99.25,\n 37.5\n ],\n [\n -97.74,\n 37.5\n ],\n [\n -97.74,\n 38.1\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
This report summarizes the results of three-dimensional (3-D) resistivity inversion simulations that were performed to account for local 3-D distortion of the electric field in the presence of 3-D regional structure, without any a priori information on the actual 3-D distribution of the known subsurface geology. The methodology used a 3-D geologic model to create a 3-D resistivity forward (“known”) model that depicted the subsurface resistivity structure expected for the input geologic configuration. The calculated magnetotelluric response of the modeled resistivity structure was assumed to represent observed magnetotelluric data and was subsequently used as input into a 3-D resistivity inverse model that used an iterative 3-D algorithm to estimate 3-D distortions without any a priori geologic information. A publicly available inversion code, WSINV3DMT, was used for all of the simulated inversions, initially using the default parameters, and subsequently using adjusted inversion parameters. A semiautomatic approach of accounting for the static shift using various selections of the highest frequencies and initial models was also tested. The resulting 3-D resistivity inversion simulation was compared to the “known” model and the results evaluated. The inversion approach that produced the lowest misfit to the various local 3-D distortions was an inversion that employed an initial model volume resistivity that was nearest to the maximum resistivities in the near-surface layer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171007","usgsCitation":"Rodriguez, B.D., 2017, Semiautomatic approaches to account for 3-D distortion of the electric field from local, near-surface structures in 3-D resistivity inversions of 3-D regional magnetotelluric data: U.S. Geological Survey Open-File Report 2017–1007, 25 p., https://doi.org/10.3133/ofr20171007.","productDescription":"iii, 25 p.","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-068212","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":338862,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1007/coverthb.jpg"},{"id":338863,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1007/ofr20171007.pdf","text":"Report","size":"20.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1007"}],"contact":"Director, Crustal Geophysics and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS 964
Denver, CO 80225
The Prairie Pothole Region (PPR) of north-central United States and south-central Canada supports greater than half of all breeding mallards (Anas platyrhynchos) annually counted in North America and is the focus of widespread conservation and research efforts. Allocation of conservation resources for this socioeconomically important population would benefit from an understanding of the nature of spatiotemporal variation in distribution of breeding mallards throughout the 850,000 km2 landscape. We used mallard counts from the Waterfowl Breeding Population and Habitat Survey to test for spatial heterogeneity and identify high- and low-abundance regions of breeding mallards over a 50-year time series. We found strong annual spatial heterogeneity in all years: 90% of mallards counted annually were on an average of only 15% of surveyed segments. Using a local indicator of spatial autocorrelation, we found a relatively static distribution of low-count clusters in northern Montana, USA, and southern Alberta, Canada, and a dynamic distribution of high-count clusters throughout the study period. Distribution of high-count clusters shifted southeast from northwestern portions of the PPR in Alberta and western Saskatchewan, Canada, to North and South Dakota, USA, during the latter half of the study period. This spatial redistribution of core mallard breeding populations was likely driven by interactions between environmental variation that created favorable hydrological conditions for wetlands in the eastern PPR and dynamic land-use patterns related to upland cropping practices and government land-retirement programs. Our results highlight an opportunity for prioritizing relatively small regions within the PPR for allocation of wetland and grassland conservation for mallard populations. However, the extensive spatial heterogeneity in core distributions over our study period suggests such spatial prioritization will have to overcome challenges presented by dynamic land-use and climate patterns in the region, and thus merits additional monitoring and empirical research to anticipate future population distribution. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.
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Andrew","contributorId":97405,"corporation":false,"usgs":true,"family":"Dolloff","given":"C.","email":"","middleInitial":"Andrew","affiliations":[],"preferred":false,"id":694022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hicke, Jeffrey A.","contributorId":87832,"corporation":false,"usgs":true,"family":"Hicke","given":"Jeffrey","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":694023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marcot, Bruce G.","contributorId":152612,"corporation":false,"usgs":false,"family":"Marcot","given":"Bruce","email":"","middleInitial":"G.","affiliations":[{"id":18944,"text":"Pacific Northwest Research Station, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":694024,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCarl, Bruce","contributorId":51645,"corporation":false,"usgs":true,"family":"McCarl","given":"Bruce","affiliations":[],"preferred":false,"id":694025,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McMahon, Gerard 0000-0001-7675-777X gmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7675-777X","contributorId":191488,"corporation":false,"usgs":true,"family":"McMahon","given":"Gerard","email":"gmcmahon@usgs.gov","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":693238,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morton, John M.","contributorId":17097,"corporation":false,"usgs":true,"family":"Morton","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":694026,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70187422,"text":"70187422 - 2017 - Reconnaissance sedimentology of selected tertiary exposures in the upland region bordering the Yukon Flats basin, east-central Alaska","interactions":[],"lastModifiedDate":"2017-05-08T13:48:52","indexId":"70187422","displayToPublicDate":"2017-03-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5388,"text":"Preliminary Interpretive Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"2016-6","title":"Reconnaissance sedimentology of selected tertiary exposures in the upland region bordering the Yukon Flats basin, east-central Alaska","docAbstract":"This report summarizes reconnaissance sedimentologic and stratigraphic observations made during six days of helicopter-supported fieldwork in 2002 on Tertiary sedimentary rocks exposed in the upland region around the flanks of the Yukon Flats basin in east-central Alaska (fig. 1). This project was a cooperative effort between the Alaska Division of Geological & Geophysical Surveys (DGGS) and the U.S. Geological Survey (USGS) to investigate the geology of the basin in preparation for an assessment of the undiscovered, technically recoverable hydrocarbon resources (Stanley and others, 2004). Field observations and interpretations summarized in this report are reconnaissance level. At most, no more than a few hours were spent on the ground at any location. Measured sections included in this report are sketch sec- tions and thicknesses shown are approximate. Relatively detailed observations were made by the authors at only three locations, including The Mudbank (Hodzana River), Rampart (east bank of the Yukon River), and Bryant Creek (along the Tintina fault near the Canada border). These three locations are described first in relative detail, then followed by general descriptions of other locations.
","language":"English","publisher":"Alaska Division of Geological & Geophysical Surveys","doi":"10.14509/29700","usgsCitation":"LePain, D.L., and Stanley, R.G., 2017, Reconnaissance sedimentology of selected tertiary exposures in the upland region bordering the Yukon Flats basin, east-central Alaska: Preliminary Interpretive Report 2016-6, vi, 14 p., https://doi.org/10.14509/29700.","productDescription":"vi, 14 p.","ipdsId":"IP-079523","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":469978,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14509/29700","text":"Publisher Index Page"},{"id":340755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon Flats","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.06103515624997,\n 66.86108230224609\n ],\n [\n -153.896484375,\n 63.73390885572919\n ],\n [\n -141.08642578125,\n 65.22910188319217\n ],\n [\n -142.93212890625,\n 68.13885164925573\n ],\n [\n -155.06103515624997,\n 66.86108230224609\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"590aec49e4b0fc4e4492aba5","contributors":{"authors":[{"text":"LePain, David L.","contributorId":191714,"corporation":false,"usgs":false,"family":"LePain","given":"David","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":693953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanley, Richard G. 0000-0001-6192-8783 rstanley@usgs.gov","orcid":"https://orcid.org/0000-0001-6192-8783","contributorId":1832,"corporation":false,"usgs":true,"family":"Stanley","given":"Richard","email":"rstanley@usgs.gov","middleInitial":"G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":693952,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70186146,"text":"fs20173026 - 2017 - U.S. Geological Survey distribution of European Space Agency's Sentinel-2 data","interactions":[],"lastModifiedDate":"2017-05-31T10:38:36","indexId":"fs20173026","displayToPublicDate":"2017-03-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3026","title":"U.S. Geological Survey distribution of European Space Agency's Sentinel-2 data","docAbstract":"A partnership established between the European Space Agency (ESA) and the U.S. Geological Survey (USGS) allows for USGS storage and redistribution of images acquired by the MultiSpectral Instrument (MSI) on the European Union's Sentinel-2 satellite mission. The MSI data are acquired from a pair of satellites, Sentinel-2A and Sentinel-2B, which are part of a larger set of ESA missions focusing on different aspects of Earth observation. The primary purpose of the Sentinel-2 series is to collect multispectral imagery over the Earth’s land surfaces, large islands, and inland and coastal waters. Sentinel-2A was launched in 2015 and Sentinel-2B launched in 2017.
The collaborative effort between ESA and USGS provides for public access and redistribution of global acquisitions of Sentinel-2 data at no cost, which allows users to download the MSI imagery from USGS access systems such as Earth- Explorer, in addition to the ESA Sentinels Scientific Data Hub. The MSI sensor acquires 13 spectral bands that are highly complementary to data acquired by the USGS Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+). The product options from USGS include a Full-Resolution Browse (FRB) image product generated by USGS, along with a 100-kilometer (km) by 100-km tile-based Level-1C top-of-atmosphere (TOA) reflectance product that is very similar (but not identical) to the currently (2017) distributed ESA Level 1C product.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173026","usgsCitation":"Pieschke, R.L., 2017, U.S. Geological Survey distribution of European Space Agency's Sentinel-2 data: U.S. Geological Survey Fact Sheet 2017–3026, 2 p., https://doi.org/10.3133/fs20173026.\n","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-082585","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":338860,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3026/fs20173026.pdf","text":"Fact Sheet","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3026"},{"id":338859,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3026/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198–0001
We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS-COAST (Coastal One-line Assimilated Simulation Tool), is a transect-based, one-line model that predicts short-term and long-term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process-based models of coastline evolution due to longshore and cross-shore transport by waves and sea-level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long-term predictions. We apply CoSMoS-COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995-2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model's predictive capability when applied to the forecast period (2010-2100) driven by GCM-projected wave and sea-level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea-level rise scenarios of 0.93 to 2.0 m.
","language":"English","publisher":"Wiley","doi":"10.1002/2016JF004065","usgsCitation":"Vitousek, S., Barnard, P., Limber, P.W., Erikson, L.H., and Cole, B., 2017, A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change: Journal of Geophysical Research F: Earth Surface, v. 122, no. 4, p. 782-806, https://doi.org/10.1002/2016JF004065.","productDescription":"25 p.","startPage":"782","endPage":"806","ipdsId":"IP-079262","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":338922,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","volume":"122","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-06","publicationStatus":"PW","scienceBaseUri":"58df6abfe4b02ff32c6aea27","contributors":{"authors":[{"text":"Vitousek, Sean","contributorId":190192,"corporation":false,"usgs":false,"family":"Vitousek","given":"Sean","affiliations":[],"preferred":false,"id":687761,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":687760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Limber, Patrick W. 0000-0002-8207-3750 plimber@usgs.gov","orcid":"https://orcid.org/0000-0002-8207-3750","contributorId":5773,"corporation":false,"usgs":true,"family":"Limber","given":"Patrick","email":"plimber@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":687762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":687763,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cole, Blake","contributorId":190193,"corporation":false,"usgs":false,"family":"Cole","given":"Blake","email":"","affiliations":[],"preferred":false,"id":687764,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70190184,"text":"70190184 - 2017 - Quantifying acoustic doppler current profiler discharge uncertainty: A Monte Carlo based tool for moving-boat measurements","interactions":[],"lastModifiedDate":"2017-08-23T10:10:56","indexId":"70190184","displayToPublicDate":"2017-03-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":18,"text":"Abstract or summary"},"title":"Quantifying acoustic doppler current profiler discharge uncertainty: A Monte Carlo based tool for moving-boat measurements","docAbstract":"This paper presents a method using Monte Carlo simulations for assessing uncertainty of moving-boat acoustic Doppler current profiler (ADCP) discharge measurements using a software tool known as QUant, which was developed for this purpose. Analysis was performed on 10 data sets from four Water Survey of Canada gauging stations in order to evaluate the relative contribution of a range of error sources to the total estimated uncertainty. The factors that differed among data sets included the fraction of unmeasured discharge relative to the total discharge, flow nonuniformity, and operator decisions about instrument programming and measurement cross section. As anticipated, it was found that the estimated uncertainty is dominated by uncertainty of the discharge in the unmeasured areas, highlighting the importance of appropriate selection of the site, the instrument, and the user inputs required to estimate the unmeasured discharge. The main contributor to uncertainty was invalid data, but spatial inhomogeneity in water velocity and bottom-track velocity also contributed, as did variation in the edge velocity, uncertainty in the edge distances, edge coefficients, and the top and bottom extrapolation methods. To a lesser extent, spatial inhomogeneity in the bottom depth also contributed to the total uncertainty, as did uncertainty in the ADCP draft at shallow sites. The estimated uncertainties from QUant can be used to assess the adequacy of standard operating procedures. They also provide quantitative feedback to the ADCP operators about the quality of their measurements, indicating which parameters are contributing most to uncertainty, and perhaps even highlighting ways in which uncertainty can be reduced. Additionally, QUant can be used to account for self-dependent error sources such as heading errors, which are a function of heading. The results demonstrate the importance of a Monte Carlo method tool such as QUant for quantifying random and bias errors when evaluating the uncertainty of moving-boat ADCP measurements.