Road networks and the disturbance associated with vehicle traffic alter animal behavior, movements, and habitat selection. The response of moose (Alces americanus) to roads has been documented in relatively rural areas, but less is known about moose response to roads in more highly roaded landscapes. We examined road‐crossing frequencies and habitat use of global positioning system (GPS)‐collared moose in Massachusetts, USA, where moose home ranges have road densities approximately twice that of previous studies. We compared seasonal road‐crossing frequencies of moose with a null movement model. We estimated moose travel speeds during road‐crossing events and compared them with speeds during other home range movements. To estimate the extent of the road effect zone and determine how roads influenced moose habitat use, we fit a third‐order resource selection function. With the exception of the lowest use road class (<10 vehicles/day), we found moose crossed roads less than expected based on the null movement model and frequency decreased with increasing road size and traffic. Moose crossed roads faster than they traveled during other times. This effect increased with increasing road use intensity. Overall, roads were a major factor determining what portions of Massachusetts moose used and how they moved among habitat patches. Our results suggest that moose in Massachusetts can adapt to a high‐density road network, but the road effect is still strongly negative and, in some cases, is more pronounced than in study areas with lower road densities. Future road construction and the expansion of road networks may have a large effect on moose and other wildlife.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21459","usgsCitation":"Wattles, D.W., Zeller, K.A., and DeStefano, S., 2018, Response of moose to a high‐density road network: Journal of Wildlife Management, v. 82, no. 5, p. 929-939, https://doi.org/10.1002/jwmg.21459.","productDescription":"11 p.","startPage":"929","endPage":"939","ipdsId":"IP-076431","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":353884,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-06","publicationStatus":"PW","scienceBaseUri":"5afee6cce4b0da30c1bfbe0e","contributors":{"authors":[{"text":"Wattles, David W.","contributorId":204573,"corporation":false,"usgs":false,"family":"Wattles","given":"David","email":"","middleInitial":"W.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":734379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zeller, Katherine A.","contributorId":204574,"corporation":false,"usgs":false,"family":"Zeller","given":"Katherine","email":"","middleInitial":"A.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":734380,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeStefano, Stephen 0000-0003-2472-8373 destef@usgs.gov","orcid":"https://orcid.org/0000-0003-2472-8373","contributorId":166706,"corporation":false,"usgs":true,"family":"DeStefano","given":"Stephen","email":"destef@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":734378,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197451,"text":"70197451 - 2018 - Book review: Handbook of cyanobacterial monitoring and cyanotoxin analysis","interactions":[],"lastModifiedDate":"2018-06-05T10:37:08","indexId":"70197451","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5706,"text":"Limnology and Oceanography Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Book review: Handbook of cyanobacterial monitoring and cyanotoxin analysis","docAbstract":"Review of Meriluoto, Jussi, Lisa Spoof, and GeoffreyA. Codd [eds.]. 2017. Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis. John Wiley & Sons, Ltd.: Chichester, West Sussex, UK, ISBN 978‐1‐119‐06868‐6 (978‐1‐119‐06876‐1 eBook), DOI 10.1002/9781119068761.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lob.10227","usgsCitation":"Graham, J., and Loftin, K.A., 2018, Book review: Handbook of cyanobacterial monitoring and cyanotoxin analysis: Limnology and Oceanography Bulletin, v. 27, no. 2, p. 61-62, https://doi.org/10.1002/lob.10227.","productDescription":"2 p.","startPage":"61","endPage":"62","ipdsId":"IP-092798","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":468798,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lob.10227","text":"Publisher Index Page"},{"id":354718,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-17","publicationStatus":"PW","scienceBaseUri":"5b46e58ee4b060350a15d1d8","contributors":{"authors":[{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":150737,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer L.","email":"jlgraham@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":737200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Keith A. 0000-0001-5291-876X kloftin@usgs.gov","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":868,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","email":"kloftin@usgs.gov","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":737201,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70197428,"text":"70197428 - 2018 - Forecasting an invasive species’ distribution with global distribution data, local data, and physiological information","interactions":[],"lastModifiedDate":"2018-06-04T10:36:56","indexId":"70197428","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting an invasive species’ distribution with global distribution data, local data, and physiological information","docAbstract":"Understanding invasive species distributions and potential invasions often requires broad‐scale information on the environmental tolerances of the species. Further, resource managers are often faced with knowing these broad‐scale relationships as well as nuanced environmental factors related to their landscape that influence where an invasive species occurs and potentially could occur. Using invasive buffelgrass (Cenchrus ciliaris), we developed global models and local models for Saguaro National Park, Arizona, USA, based on location records and literature on physiological tolerances to environmental factors to investigate whether environmental relationships of a species at a global scale are also important at local scales. In addition to correlative models with five commonly used algorithms, we also developed a model using a priori user‐defined relationships between occurrence and environmental characteristics based on a literature review. All correlative models at both scales performed well based on statistical evaluations. The user‐defined curves closely matched those produced by the correlative models, indicating that the correlative models may be capturing mechanisms driving the distribution of buffelgrass. Given climate projections for the region, both global and local models indicate that conditions at Saguaro National Park may become more suitable for buffelgrass. Combining global and local data with correlative models and physiological information provided a holistic approach to forecasting invasive species distributions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2279","usgsCitation":"Jarnevich, C.S., Young, N.E., Talbert, M., and Talbert, C., 2018, Forecasting an invasive species’ distribution with global distribution data, local data, and physiological information: Ecosphere, v. 9, no. 5, p. 1-12, https://doi.org/10.1002/ecs2.2279.","productDescription":"e02279; 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-097154","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468799,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2279","text":"Publisher Index Page"},{"id":437929,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y99UFF","text":"USGS data release","linkHelpText":"Data for forecasting buffelgrass distribution with global distribution data, local data, and physiological information"},{"id":354686,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Saguaro National Park","volume":"9","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-29","publicationStatus":"PW","scienceBaseUri":"5b155d84e4b092d9651e1b61","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":737118,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, Nicholas E.","contributorId":58572,"corporation":false,"usgs":true,"family":"Young","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":737119,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Talbert, Marian 0000-0003-0588-0265 mtalbert@usgs.gov","orcid":"https://orcid.org/0000-0003-0588-0265","contributorId":196740,"corporation":false,"usgs":true,"family":"Talbert","given":"Marian","email":"mtalbert@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":737120,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Talbert, Colin 0000-0002-9505-1876 talbertc@usgs.gov","orcid":"https://orcid.org/0000-0002-9505-1876","contributorId":181913,"corporation":false,"usgs":true,"family":"Talbert","given":"Colin","email":"talbertc@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":737121,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196560,"text":"ofr20181070 - 2018 - Population dynamics of the northern tamarisk beetle (Diorhabda carinulata) in the Colorado River Basin","interactions":[],"lastModifiedDate":"2018-05-02T10:36:39","indexId":"ofr20181070","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1070","title":"Population dynamics of the northern tamarisk beetle (Diorhabda carinulata) in the Colorado River Basin","docAbstract":"SBSC Staff,
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
We describe a set of benchmark exercises that are designed to test if computer codes that simulate dynamic earthquake rupture are working as intended. These types of computer codes are often used to understand how earthquakes operate, and they produce simulation results that include earthquake size, amounts of fault slip, and the patterns of ground shaking and crustal deformation. The benchmark exercises examine a range of features that scientists incorporate in their dynamic earthquake rupture simulations. These include implementations of simple or complex fault geometry, off‐fault rock response to an earthquake, stress conditions, and a variety of formulations for fault friction. Many of the benchmarks were designed to investigate scientific problems at the forefronts of earthquake physics and strong ground motions research. The exercises are freely available on our website for use by the scientific community.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220170222","usgsCitation":"Harris, R.A., Barall, M., Aagaard, B.T., Ma, S., Roten, D., Olsen, K., Duan, B., Liu, D., Luo, B., Bai, K., Ampuero, J., Kaneko, Y., Gabriel, A., Duru, K., Ulrich, T., Wollherr, S., Shi, Z., Dunham, E., Bydlon, S., Zhang, Z., Chen, X., Somala, S.N., Pelties, C., Tago, J., Cruz-Atienza, V.M., Kozdon, J., Daub, E., Aslam, K., Kase, Y., Withers, K., and Dalguer, L., 2018, A suite of exercises for verifying dynamic earthquake rupture codes: Seismological Research Letters, v. 89, no. 3, p. 1146-1162, https://doi.org/10.1785/0220170222.","productDescription":"17 p.","startPage":"1146","endPage":"1162","ipdsId":"IP-090832","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":502555,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/10945/59234","text":"External 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,{"id":70196807,"text":"70196807 - 2018 - Quantifying temporal trends in fisheries abundance using Bayesian dynamic linear models: A case study of riverine Smallmouth Bass populations","interactions":[],"lastModifiedDate":"2018-05-02T10:48:08","indexId":"70196807","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","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":"Quantifying temporal trends in fisheries abundance using Bayesian dynamic linear models: A case study of riverine Smallmouth Bass populations","docAbstract":"Detecting temporal changes in fish abundance is an essential component of fisheries management. Because of the need to understand short‐term and nonlinear changes in fish abundance, traditional linear models may not provide adequate information for management decisions. This study highlights the utility of Bayesian dynamic linear models (DLMs) as a tool for quantifying temporal dynamics in fish abundance. To achieve this goal, we quantified temporal trends of Smallmouth Bass Micropterus dolomieu catch per effort (CPE) from rivers in the mid‐Atlantic states, and we calculated annual probabilities of decline from the posterior distributions of annual rates of change in CPE. We were interested in annual declines because of recent concerns about fish health in portions of the study area. In general, periods of decline were greatest within the Susquehanna River basin, Pennsylvania. The declines in CPE began in the late 1990s—prior to observations of fish health problems—and began to stabilize toward the end of the time series (2011). In contrast, many of the other rivers investigated did not have the same magnitude or duration of decline in CPE. Bayesian DLMs provide information about annual changes in abundance that can inform management and are easily communicated with managers and stakeholders.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10051","usgsCitation":"Schall, M.K., Blazer, V., Lorantas, R.M., Smith, G., Mullican, J.E., Keplinger, B.J., and Wagner, T., 2018, Quantifying temporal trends in fisheries abundance using Bayesian dynamic linear models: A case study of riverine Smallmouth Bass populations: North American Journal of Fisheries Management, v. 38, no. 2, p. 493-501, https://doi.org/10.1002/nafm.10051.","productDescription":"9 p.","startPage":"493","endPage":"501","ipdsId":"IP-085001","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":353909,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Pennsylvania, West Virginia","otherGeospatial":"Allegheny River, Delaware River, Juniata River, Potomac River. Susquehanna River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.09033203125,\n 37.90953361677018\n ],\n [\n -74.696044921875,\n 37.90953361677018\n ],\n [\n -74.696044921875,\n 42.15525946577863\n ],\n [\n -80.09033203125,\n 42.15525946577863\n ],\n [\n -80.09033203125,\n 37.90953361677018\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-10","publicationStatus":"PW","scienceBaseUri":"5afee6c5e4b0da30c1bfbe04","contributors":{"authors":[{"text":"Schall, Megan K.","contributorId":115964,"corporation":false,"usgs":false,"family":"Schall","given":"Megan","email":"","middleInitial":"K.","affiliations":[{"id":17758,"text":"Pennsylvania State Univ.","active":true,"usgs":false}],"preferred":false,"id":734549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":734532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lorantas, Robert M.","contributorId":204631,"corporation":false,"usgs":false,"family":"Lorantas","given":"Robert","email":"","middleInitial":"M.","affiliations":[{"id":36966,"text":"Pennsylvania Fish and Boat Commission","active":true,"usgs":false}],"preferred":false,"id":734550,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Geoffrey","contributorId":115958,"corporation":false,"usgs":true,"family":"Smith","given":"Geoffrey","affiliations":[],"preferred":false,"id":734551,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mullican, John E.","contributorId":203245,"corporation":false,"usgs":false,"family":"Mullican","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":734552,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keplinger, Brandon J.","contributorId":204644,"corporation":false,"usgs":false,"family":"Keplinger","given":"Brandon","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":734553,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":734531,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70198137,"text":"70198137 - 2018 - Taiga bean goose: Harvest assessment for the Central Management Unit: 2018","interactions":[],"lastModifiedDate":"2018-07-24T15:44:17","indexId":"70198137","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Taiga bean goose: Harvest assessment for the Central Management Unit: 2018","docAbstract":"In 2016 the European Goose Management International Working Group (EGM IWG) began development of an adaptive harvest management program for Taiga Bean Geese (TBG). In 2017, the EGM IWG adopted an interim harvest strategy consisting of a constant harvest rate (on adults) of 3% for the Central Management of Taiga Bean Geese. The interim strategy is intended to provide limited hunting opportunity while rebuilding the population. Recent efforts have involved development of a dynamic strategy in which the harvest rate can vary each year with changes in population size, and in which multiple, possibly competing, management objectives can be addressed. This report provides examples of dynamic harvest strategies and compares them with the interim, constant harvest-rate strategy. Until such time that a dynamic strategy is adopted by the EGM IWG, the annual harvest quota and its allocation among Range States is predicated on the interim strategy. Based on a January count of 38,717, the harvest quota for the 2018 hunting season is 1,610 Taiga Bean Geese (compared to 2,335 for the 2017 season). We emphasize that these quotas include both harvest during the regular season and derogation shooting. We acknowledge that the January 2018 count of Taiga Bean Geese in the Central Management Unit was likely biased low, as counts in the autumn and spring in Sweden were higher. Additionally, the size of the harvest during the fall and winter of 2017-18 is unknown, due to an inability to differentiate taiga and Tundra Bean Geese in the harvest, compilation of data too late to be used in this report, and a lack of reporting. Because of problems with both the population and harvest monitoring programs it is difficult to estimate a harvest quota for 2018 with any degree of confidence.
","language":"English","publisher":"AEWA European Goose Management","usgsCitation":"Johnson, F.A., Jensen, G.H., Alhainen, M., Fox, A.D., and Madsen, J., 2018, Taiga bean goose: Harvest assessment for the Central Management Unit: 2018, 15 p.","productDescription":"15 p.","ipdsId":"IP-098380","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":355957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":355714,"type":{"id":15,"text":"Index Page"},"url":"https://www.unep-aewa.org/sites/default/files/document/AEWA_EGMIWG_3_12_TBG_Harvest_Report.pdf"}],"publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc45de4b0f5d57878ea63","contributors":{"authors":[{"text":"Johnson, Fred A. 0000-0002-5854-3695 fjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":2773,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","email":"fjohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":740183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jensen, Gitte Hoj","contributorId":206363,"corporation":false,"usgs":false,"family":"Jensen","given":"Gitte","email":"","middleInitial":"Hoj","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":740184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alhainen, Mikko","contributorId":141140,"corporation":false,"usgs":false,"family":"Alhainen","given":"Mikko","email":"","affiliations":[{"id":13690,"text":"Finnish Wildlife Agency","active":true,"usgs":false}],"preferred":false,"id":740185,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fox, Anthony D.","contributorId":130960,"corporation":false,"usgs":false,"family":"Fox","given":"Anthony","email":"","middleInitial":"D.","affiliations":[{"id":7177,"text":"Dept of Bioscience, Aahus Univ, Denmark","active":true,"usgs":false}],"preferred":false,"id":740186,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Madsen, Jesper","contributorId":178168,"corporation":false,"usgs":false,"family":"Madsen","given":"Jesper","email":"","affiliations":[],"preferred":false,"id":740187,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196951,"text":"70196951 - 2018 - A multiscale investigation of habitat use and within-river distribution of sympatric sand darter species","interactions":[],"lastModifiedDate":"2018-05-17T15:50:26","indexId":"70196951","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5689,"text":"Journal of Geospatial Applications in Natural Resources","active":true,"publicationSubtype":{"id":10}},"title":"A multiscale investigation of habitat use and within-river distribution of sympatric sand darter species","docAbstract":"The western sand darter Ammocrypta clara, and eastern sand darter Ammocrypta pellucida, are sand-dwelling fishes of conservation concern. Past research has emphasized the importance of studying individual populations of conservation concern, while recent research has revealed the importance of incorporating landscape scale processes that structure habitat mosaics and local populations. We examined habitat use and distributions of western and eastern sand darters in the lower Elk River of West Virginia. At the sandbar habitat use scale, western sand darters were detected in sandbars with greater area, higher proportions of coarse grain sand and faster bottom current velocity, while the eastern sand darter used a wider range of sandbar habitats. The landscape scale analysis revealed that contributing drainage area was an important predictor for both species, while sinuosity, which presumably represents valley type, also contributed to the western sand darter’s habitat suitability. Sandbar quality (area, grain size, and velocity) and fluvial geomorphic variables (drainage area and valley type) are likely key driving factors structuring sand darter distributions in the Elk River. This multiscale study of within-river species distribution and habitat use is unique, given that only a few sympatric populations are known of western and eastern sand darters.
","language":"English","publisher":"SFA ScholarWorks","usgsCitation":"Thompson, P.A., Welsh, S., Strager, M.P., and Rizzo, A.A., 2018, A multiscale investigation of habitat use and within-river distribution of sympatric sand darter species: Journal of Geospatial Applications in Natural Resources, v. 2, no. 1, p. 1-22.","productDescription":"Article 1; 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-086297","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":354289,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":354109,"type":{"id":15,"text":"Index Page"},"url":"https://scholarworks.sfasu.edu/j_of_geospatial_applications_in_natural_resources/vol2/iss1/1/"}],"country":"United States","state":"West Virginia","otherGeospatial":"Elk River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.6668701171875,\n 38.3384247989913\n ],\n [\n -80.68634033203125,\n 38.3384247989913\n ],\n [\n -80.68634033203125,\n 38.68122173079789\n ],\n [\n -81.6668701171875,\n 38.68122173079789\n ],\n [\n -81.6668701171875,\n 38.3384247989913\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6c4e4b0da30c1bfbdf8","contributors":{"authors":[{"text":"Thompson, Patricia A. pathompson@usgs.gov","contributorId":139753,"corporation":false,"usgs":false,"family":"Thompson","given":"Patricia","email":"pathompson@usgs.gov","middleInitial":"A.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":735754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":152088,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart A.","email":"swelsh@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":735119,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Strager, Michael P.","contributorId":169817,"corporation":false,"usgs":false,"family":"Strager","given":"Michael","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":735755,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rizzo, Austin A.","contributorId":191439,"corporation":false,"usgs":false,"family":"Rizzo","given":"Austin","email":"","middleInitial":"A.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":735756,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197866,"text":"70197866 - 2018 - The intensity signature of induced seismicity","interactions":[],"lastModifiedDate":"2018-06-22T14:47:02","indexId":"70197866","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"The intensity signature of induced seismicity","docAbstract":"We analyze a comprehensive database of
The Pacific/North America (PA/NA) plate boundary between Vancouver Island and Alaska is similar to the PA/NA boundary in California in its kinematic history and the rate and azimuth of current relative motion, yet their deformation styles are distinct. The California plate boundary shows a broad zone of parallel strike slip and thrust faults and folds, whereas the 49‐mm/yr PA/NA relative plate motion in Canada and Alaska is centered on a single, narrow, continuous ~900‐km‐long fault, the Queen Charlotte Fault (QCF). Using gravity analysis, we propose that this plate boundary is centered on the continent/ocean boundary (COB), an unusual location for continental transform faults because plate boundaries typically localize within the continental lithosphere, which is weaker. Because the COB is a boundary between materials of contrasting elastic properties, once a fault is established there, it will probably remain stable. We propose that deformation progressively shifted to the COB in the wake of Yakutat terrane's northward motion along the margin. Minor convergence across the plate boundary is probably accommodated by fault reactivation on Pacific crust and by an eastward dipping QCF. Underthrusting of Pacific slab under Haida Gwaii occurs at convergence angles >14°–15° and may have been responsible for the emergence of the archipelago. The calculated slab entry dip (5°–8°) suggests that the slab probably does not extend into the asthenosphere. The PA/NA plate boundary at the QCF can serve as a structurally simple site to investigate the impact of rheology and composition on crustal deformation and the initiation of slab underthrusting.
","language":"English","publisher":"AGU","doi":"10.1002/2017JB014770","usgsCitation":"ten Brink, U., Miller, N.C., Andrews, B.D., Brothers, D.S., and Haeussler, P.J., 2018, Deformation of the Pacific/North America plate boundary at Queen Charlotte Fault: The possible role of rheology: Journal of Geophysical Research B: Solid Earth, v. 123, no. 5, p. 4223-4242, https://doi.org/10.1002/2017JB014770.","productDescription":"20 p.","startPage":"4223","endPage":"4242","ipdsId":"IP-088657","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":460937,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1912/10462","text":"External Repository"},{"id":355347,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -140,\n 50\n ],\n [\n -128,\n 50\n ],\n [\n -128,\n 60\n ],\n [\n -140,\n 60\n ],\n [\n -140,\n 50\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"123","issue":"5","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-08","publicationStatus":"PW","scienceBaseUri":"5b46e58de4b060350a15d1d2","contributors":{"authors":[{"text":"ten Brink, Uri S. 0000-0001-6858-3001 utenbrink@usgs.gov","orcid":"https://orcid.org/0000-0001-6858-3001","contributorId":127560,"corporation":false,"usgs":true,"family":"ten Brink","given":"Uri S.","email":"utenbrink@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":739016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Nathaniel C. 0000-0003-3271-2929 ncmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3271-2929","contributorId":174592,"corporation":false,"usgs":true,"family":"Miller","given":"Nathaniel","email":"ncmiller@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739017,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andrews, Brian D. 0000-0003-1024-9400 bandrews@usgs.gov","orcid":"https://orcid.org/0000-0003-1024-9400","contributorId":201662,"corporation":false,"usgs":true,"family":"Andrews","given":"Brian","email":"bandrews@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":739018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brothers, Daniel S. 0000-0001-7702-157X dbrothers@usgs.gov","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":167089,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","email":"dbrothers@usgs.gov","middleInitial":"S.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":739019,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haeussler, Peter J. 0000-0002-1503-6247 pheuslr@usgs.gov","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":503,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter","email":"pheuslr@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":739020,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196457,"text":"sir20185050 - 2018 - Discharge, sediment, and water chemistry in Clear Creek, western Nevada, water years 2013–16","interactions":[],"lastModifiedDate":"2018-05-02T10:35:24","indexId":"sir20185050","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5050","title":"Discharge, sediment, and water chemistry in Clear Creek, western Nevada, water years 2013–16","docAbstract":"Clear Creek is a small stream that drains the eastern Carson Range near Lake Tahoe, flows roughly parallel to the Highway 50 corridor, and discharges to the Carson River near Carson City, Nevada. Historical and ongoing development in the drainage basin is thought to be affecting Clear Creek and its sediment-transport characteristics. Previous studies from water years (WYs) 2004 to 2007 and from 2010 to 2012 evaluated discharge, selected water-quality parameters, and suspended-sediment concentrations, loads, and yields at three Clear Creek sampling sites. This report serves as a continuation of the data collection and analyses of the Clear Creek discharge regime and associated water-chemistry and sediment concentrations and loads during WYs 2013–16.
Total annual sediment loads ranged from 870 to 5,300 tons during WYs 2004–07, from 320 to 1,770 tons during WYs 2010–12, and from 50 to 200 tons during WYs 2013–16. Ranges in annual loads during the three study periods were not significantly different; however, total loads were greater during 2004–07 than they were during 2013–16. Annual suspended-sediment loads in WYs 2013–16 showed no significant change since WYs 2010–12 at sites 1 (U.S. Geological Survey reference site 10310485; Clear Creek above Highway 50, near Spooner Summit, Nevada) or 2 (U.S. Geological Survey streamgage 10310500; Clear Creek above Highway 50, near Spooner Summit, Nevada), but significantly lower loads at site 3 (U.S. Geological Survey site 10310518; Clear Creek at Fuji Park, at Carson City, Nevada), supporting the theory of sediment deposition between sites 2 and 3 where the stream gradient becomes more gradual. Currently, a threshold discharge of about 3.3 cubic feet per second is required to mobilize streambed sediment (bedload) from site 2 in Clear Creek. Mean daily discharge was significantly lower in 2010–12 than in 2004–07 and also significantly lower in 2013–16 than in 2010–12. During this study, lower bedload, and therefore lower total sediment load in Clear Creek was primarily due to significantly lower discharge and cannot be directly attributed to sediment mitigation work in the basin.
Water chemistry in Clear Creek shows that the general water type of the creek under base-flow conditions in autumn is a dilute calcium bicarbonate. During winter and spring, the chemistry shifts toward a slightly more sodium and chloride character. Though the chemical characteristics show seasonal change, the water chemistries examined as part of this investigation remain within ecological criteria as adopted by the Nevada Division of Environmental Protection. There was no evidence of aqueous polynuclear aromatic hydrocarbons (PAHs) present in Clear Creek water during this study. Concentrations of PAHs, as determined in one bed-sediment sample and multiple semi-permeable membrane device extracts, were either less than quantifiable limits of analysis or were found at similar concentrations as blank samples.
In July 2014, a 250–300-acre fire burned in the Clear Creek drainage basin. One day after the fire was extinguished, a thunderstorm washed sediment into the creek. A water chemistry sample collected as part of the post-fire storm event showed that the stormwater entering the creek had increased the concentrations of ammonium and organic nitrogen, phosphorus, manganese, and potassium; a similar finding of many other studies evaluating the effects of fires in small drainage basins. Subsequent chemical analyses of Clear Creek water in August 2014 (one month later) showed that these constituents had returned to pre-fire concentrations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185050","collaboration":"Prepared in cooperation with the Nevada Department of Transportation","usgsCitation":"Huntington, J.M., Riddle, D.J., and Paul, A.P., 2018, Discharge, sediment, and water chemistry in Clear Creek, western Nevada, water years 2013–16: U.S. Geological Survey Scientific-Investigations Report 2018–5050, 55 p., https://doi.org/10.3133/sir20185050.","productDescription":"vii, 55 p.","onlineOnly":"Y","ipdsId":"IP-067971","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":353895,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5050/sir20185050.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5050"},{"id":353894,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5050/coverthb.jpg"}],"country":"United States","state":"Nevada","city":"Carson City","otherGeospatial":"Clear Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.9,\n 39.19\n ],\n [\n -119.7,\n 39.19\n ],\n [\n -119.7,\n 39.06\n ],\n [\n -119.9,\n 39.06\n ],\n [\n -119.9,\n 39.19\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, Nevada 89701
The Lower Keys marsh rabbit (Sylvilagus palustris hefneri) is one of many endangered endemic species of the Florida Keys. The main threats are habitat loss and fragmentation from sea‐level rise, development, and habitat succession. Exotic predators such as free‐ranging domestic cats (Felis catus) pose an additional threat to these endangered small mammals. Management strategies have focused on habitat restoration and exotic predator control. However, the effectiveness of predator removal and the effects of anthropogenic habitat modifications and restoration have not been evaluated. Between 2013 and 2015, we used camera traps to survey marsh rabbits and free‐ranging cats at 84 sites in the National Key Deer Refuge, Big Pine Key, Florida, USA. We used dynamic occupancy models to determine factors associated with marsh rabbit occurrence, colonization, extinction, and the co‐occurrence of marsh rabbits and cats during a period of predator removal. Rabbit occurrence was positively related to freshwater habitat and patch size, but was negatively related to the number of individual cats detected at each site. Furthermore, marsh rabbit colonization was negatively associated with relative increases in the number of individual cats at each site between survey years. Cat occurrence was negatively associated with increasing distance from human developments. The probability of cat site extinction was positively related to a 2‐year trapping effort, indicating that predator removal reduced the cat population. Dynamic co‐occurrence models suggested that cats and marsh rabbits co‐occur less frequently than expected under random conditions, whereas co‐detections were site and survey‐specific. Rabbit site extinction and colonization were not strongly conditional on cat presence, but corresponded with a negative association. Our results suggest that while rabbits can colonize and persist at sites where cats occur, it is the number of individual cats at a site that more strongly influences rabbit occupancy and colonization. These findings indicate that continued predator management would likely benefit endangered small mammals as they recolonize restored habitats.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.3954","usgsCitation":"Cove, M., Gardner, B., Simons, T.R., and O’Connell, A.F., 2018, Co‐occurrence dynamics of endangered Lower Keys marsh rabbits and free‐ranging domestic cats: Prey responses to an exotic predator removal program: Ecology and Evolution, v. 8, no. 8, p. 4042-4052, https://doi.org/10.1002/ece3.3954.","productDescription":"11 p.","startPage":"4042","endPage":"4052","ipdsId":"IP-083926","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468795,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.3954","text":"Publisher Index Page"},{"id":353915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"National Key Deer Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.40869140625,\n 24.662306385334862\n ],\n [\n -81.33522033691405,\n 24.662306385334862\n ],\n [\n -81.33522033691405,\n 24.747454885176023\n ],\n [\n -81.40869140625,\n 24.747454885176023\n ],\n [\n -81.40869140625,\n 24.662306385334862\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"8","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-26","publicationStatus":"PW","scienceBaseUri":"5afee6c5e4b0da30c1bfbe06","contributors":{"authors":[{"text":"Cove, Michael V.","contributorId":176507,"corporation":false,"usgs":false,"family":"Cove","given":"Michael V.","affiliations":[],"preferred":false,"id":734564,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gardner, Beth","contributorId":91612,"corporation":false,"usgs":false,"family":"Gardner","given":"Beth","affiliations":[{"id":13553,"text":"University of Washington-Seattle","active":true,"usgs":false}],"preferred":false,"id":734565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Simons, Theodore R. 0000-0002-1884-6229 tsimons@usgs.gov","orcid":"https://orcid.org/0000-0002-1884-6229","contributorId":2623,"corporation":false,"usgs":true,"family":"Simons","given":"Theodore","email":"tsimons@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734521,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Connell, Allan F. 0000-0001-7032-7023 aoconnell@usgs.gov","orcid":"https://orcid.org/0000-0001-7032-7023","contributorId":471,"corporation":false,"usgs":true,"family":"O’Connell","given":"Allan","email":"aoconnell@usgs.gov","middleInitial":"F.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":734566,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196772,"text":"70196772 - 2018 - Quantifying salinity and season effects on eastern oyster clearance and oxygen consumption rates","interactions":[],"lastModifiedDate":"2018-05-01T11:37:43","indexId":"70196772","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2660,"text":"Marine Biology","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying salinity and season effects on eastern oyster clearance and oxygen consumption rates","docAbstract":"There are few data on Crassostrea virginica physiological rates across the range of salinities and temperatures to which they are regularly exposed, and this limits the applicability of growth and production models using these data. The objectives of this study were to quantify, in winter (17 °C) and summer (27 °C), the clearance and oxygen consumption rates of C. virginica from Louisiana across a range of salinities typical of the region (3, 6, 9, 15 and 25). Salinity and season (temperature and reproduction) affected C. virginica physiology differently; salinity impacted clearance rates with reduced feeding rates at low salinities, while season had a strong effect on respiration rates. Highest clearance rates were found at salinities of 9–25, with reductions ranging from 50 to 80 and 90 to 95% at salinities of 6 and 3, respectively. Oxygen consumption rates in summer were four times higher than in winter. Oxygen consumption rates were within a narrow range and similar among salinities in winter, but varied greatly among individuals and salinities in summer. This likely reflected varying stages of gonad development. Valve movements measured at the five salinities indicated oysters were open 50–60% of the time in the 6–25 salinity range and ~ 30% at a salinity of 3. Reduced opening periods, concomitant with narrower valve gap amplitudes, are in accord with the limited feeding at the lowest salinity (3). These data indicate the need for increased focus on experimental determination of optimal ranges and thresholds to better quantify oyster population responses to environmental changes.
","language":"English","publisher":"Springer","doi":"10.1007/s00227-018-3351-x","usgsCitation":"Casas, S., Lavaud, R., LaPeyre, M.K., Comeau, L., Filgueira, R., and LaPeyre, J.F., 2018, Quantifying salinity and season effects on eastern oyster clearance and oxygen consumption rates: Marine Biology, v. 165, p. 1-13, https://doi.org/10.1007/s00227-018-3351-x.","productDescription":"Article 90; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-092990","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":353872,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"165","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-25","publicationStatus":"PW","scienceBaseUri":"5afee6cce4b0da30c1bfbe14","contributors":{"authors":[{"text":"Casas, S.M.","contributorId":8321,"corporation":false,"usgs":true,"family":"Casas","given":"S.M.","email":"","affiliations":[],"preferred":false,"id":734390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lavaud, Romain","contributorId":200114,"corporation":false,"usgs":false,"family":"Lavaud","given":"Romain","email":"","affiliations":[],"preferred":false,"id":734391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaPeyre, Megan K. 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":585,"corporation":false,"usgs":true,"family":"LaPeyre","given":"Megan","email":"mlapeyre@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":734313,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Comeau, L. A.","contributorId":204577,"corporation":false,"usgs":false,"family":"Comeau","given":"L. A.","affiliations":[],"preferred":false,"id":734392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Filgueira, R.","contributorId":204578,"corporation":false,"usgs":false,"family":"Filgueira","given":"R.","email":"","affiliations":[],"preferred":false,"id":734393,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"LaPeyre, Jerome F.","contributorId":189466,"corporation":false,"usgs":false,"family":"LaPeyre","given":"Jerome","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":734394,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196945,"text":"70196945 - 2018 - Risky behavior and its effect on survival: snowshoe hare behavior under varying moonlight conditions","interactions":[],"lastModifiedDate":"2018-05-17T15:23:15","indexId":"70196945","displayToPublicDate":"2018-05-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2515,"text":"Journal of Zoology","active":true,"publicationSubtype":{"id":10}},"title":"Risky behavior and its effect on survival: snowshoe hare behavior under varying moonlight conditions","docAbstract":"Predation and predation risk can exert strong influences on the behavior of prey species. However, risk avoidance behaviors may vary among populations of the same species. We studied a population of snowshoe hares (Lepus americanus) near the southern edge of their range, in Pennsylvania. This population occupies different habitat types, experiences different environmental conditions, and are exposed to different predator species and densities than northern hare populations; therefore, they might exhibit differences in risk avoidance behaviors. We analyzed hare survival, movement rates, and habitat use under different levels of predation risk, as indexed by moonlight. Similar to previous work, we found snowshoe hare survival decreased with increased moon illumination during the winter, but we found differences in behavioral responses to increased predation risk. We found that snowshoe hares did not reduce movement rates during high‐risk nights, but instead found that hares selected areas with denser canopy cover, compared to low‐risk nights. We suggest that behavioral plasticity in response to predation risk allows populations of the same species to respond to localized conditions.
","language":"English","publisher":"ZSL","doi":"10.1111/jzo.12532","usgsCitation":"Gigliotti, L., and Diefenbach, D.R., 2018, Risky behavior and its effect on survival: snowshoe hare behavior under varying moonlight conditions: Journal of Zoology, v. 305, no. 1, p. 27-34, https://doi.org/10.1111/jzo.12532.","productDescription":"8 p.","startPage":"27","endPage":"34","ipdsId":"IP-083397","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":354280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"305","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-09","publicationStatus":"PW","scienceBaseUri":"5afee6c4e4b0da30c1bfbdfa","contributors":{"authors":[{"text":"Gigliotti, Laura C.","contributorId":204828,"corporation":false,"usgs":false,"family":"Gigliotti","given":"Laura C.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":735107,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diefenbach, Duane R. 0000-0001-5111-1147 drd11@usgs.gov","orcid":"https://orcid.org/0000-0001-5111-1147","contributorId":5235,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Duane","email":"drd11@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":735106,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196206,"text":"sir20185044 - 2018 - Estimation of unregulated monthly, annual, and peak streamflows in Forest City Stream and lake levels in East Grand Lake, United States-Canada border between Maine and New Brunswick","interactions":[],"lastModifiedDate":"2018-05-01T16:07:09","indexId":"sir20185044","displayToPublicDate":"2018-04-30T11:45:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5044","title":"Estimation of unregulated monthly, annual, and peak streamflows in Forest City Stream and lake levels in East Grand Lake, United States-Canada border between Maine and New Brunswick","docAbstract":"The U.S. Geological Survey, in cooperation with the International Joint Commission, compiled historical data on regulated streamflows and lake levels and estimated unregulated streamflows and lake levels on Forest City Stream at Forest City, Maine, and East Grand Lake on the United States-Canada border between Maine and New Brunswick to study the effects on streamflows and lake levels if two or all three dam gates are left open. Historical regulated monthly mean streamflows in Forest City Stream at the outlet of East Grand Lake (referred to as Grand Lake by Environment Canada) fluctuated between 114 cubic feet per second (ft3 /s) (3.23 cubic meters per second [m3 /s]) in November and 318 ft3 /s (9.01 m3 /s) in September from 1975 to 2015 according to Environment Canada streamgaging data. Unregulated monthly mean streamflows at this location estimated from regression equations for unregulated sites range from 59.2 ft3 /s (1.68 m3 /s) in September to 653 ft3 /s (18.5 m3 /s) in April. Historical lake levels in East Grand Lake fluctuated between 431.3 feet (ft) (131.5 meters [m]) in October and 434.0 ft (132.3 m) in May from 1969 to 2016 according to Environment Canada lake level data for East Grand Lake. Average monthly lake levels modeled by using the estimated hydrology for unregulated flows, and an outflow rating built from a hydraulic model with all gates at the dam open, range from 427.7 ft (130.4 m) in September to 431.1 ft (131.4 m) in April. Average monthly lake levels would likely be from 1.8 to 5.4 ft (0.55 to 1.6 m) lower with the gates at the dam opened than they have been historically. The greatest lake level changes would be from June through September.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185044","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Lombard, P.J., 2018, Estimation of unregulated monthly, annual, and peak streamflows in Forest City Stream and lake levels in East Grand Lake, United States-Canada border between Maine and New Brunswick: U.S. Geological Survey Scientific Investigations Report 2018–5044, 8 p., https://doi.org/10.3133/sir20185044.","productDescription":"Report: iv, 8 p.; Data release","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092951","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":353763,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PN94VN","text":"USGS data release","description":"USGS data release","linkHelpText":"Bathymetric data for St. Croix River at outlet to East Grand Lake and Forest City Dam Survey, United States-Canadian border between Maine and New Brunswick"},{"id":353745,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5044/sir20185044.pdf","text":"Report","size":"873 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5044"},{"id":353744,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5044/coverthb.jpg"}],"country":"Canada, United States","state":"Maine, New Brunswick","otherGeospatial":"East Grand Lake, Forest City Stream","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.884521484375,\n 45.60587170876381\n ],\n [\n -67.68951416015625,\n 45.60587170876381\n ],\n [\n -67.68951416015625,\n 45.82066487514085\n ],\n [\n -67.884521484375,\n 45.82066487514085\n ],\n [\n -67.884521484375,\n 45.60587170876381\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
196 Whitten Road
Augusta, ME 04330
Burbot (Lota lota) are an apex piscivore that were illegally introduced to the Green River drainage, Wyoming, raising concerns for the conservation and management of fishes throughout the basin. However, relatively little is known about the diet of non-native burbot. The objectives of this research were to characterize diet composition of burbot and identify differences in diet composition as a function of sampling gear. Diet composition was characterized using frequency of occurrence, percent by number, and percent by weight to identify the importance of each prey type to burbot. Diet composition was compared across gears to identify the relationship between gear and diet. Fishes were present in the stomach contents of nearly all burbot sampled and composed 62–100 percent of the stomach contents of burbot greater than 300 mm. Prey diversity was greatest in diets of burbot sampled with small-mesh hoop nets. Results from the current study provide important information on the diet of non-native burbot and highlight the potential influence of gear on diet studies.
","language":"English","publisher":"Intermountain Journal of Science","usgsCitation":"McBaine, K.E., Klein, Z.B., Quist, M.C., and Rhea, D.T., 2018, Diet of burbot and implications for sampling: Intermountain Journal of Sciences, v. 24, no. 1-2, p. 1-13.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-075395","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395081,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395075,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://arc.lib.montana.edu/ojs/index.php/IJS/article/view/1350"}],"country":"United States","state":"Wyoming","otherGeospatial":"Green River drainage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.6268310546875,\n 41.15797827873605\n ],\n [\n -108.86352539062499,\n 41.15797827873605\n ],\n [\n -108.86352539062499,\n 43.27720532212024\n ],\n [\n -110.6268310546875,\n 43.27720532212024\n ],\n [\n -110.6268310546875,\n 41.15797827873605\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","issue":"1-2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McBaine, Kathryn E.","contributorId":272565,"corporation":false,"usgs":false,"family":"McBaine","given":"Kathryn","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":832152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klein, Zachary B.","contributorId":171709,"corporation":false,"usgs":false,"family":"Klein","given":"Zachary","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":832153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":831841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rhea, Darren T.","contributorId":74650,"corporation":false,"usgs":true,"family":"Rhea","given":"Darren","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":832154,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205206,"text":"70205206 - 2018 - Reproductive frequency and size-dependence of fecundity in the Giant Gartersnake (Thamnophis gigas)","interactions":[],"lastModifiedDate":"2019-09-06T10:21:06","indexId":"70205206","displayToPublicDate":"2018-04-30T10:19:21","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1894,"text":"Herpetological Conservation and Biology","onlineIssn":"2151-0733","printIssn":"1931-7603","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Reproductive frequency and size-dependence of fecundity in the Giant Gartersnake (Thamnophis gigas)","title":"Reproductive frequency and size-dependence of fecundity in the Giant Gartersnake (Thamnophis gigas)","docAbstract":"How reproductive output changes with age or size is a key life-history trait that can affect which demographic rates most influence population growth. Although many studies have investigated the reproductive ecology of gartersnakes, we know little about reproduction in the threatened Giant Gartersnake, Thamnophis gigas. We used X-radiography to determine reproductive status and estimated fecundity for 73 female T. gigas collected from several regions within the range of this species in the Sacramento Valley of California, USA, and synthesize these data with data from litters born in captivity to improve our understanding of reproduction in this species. Average total litter size determined from X-rays (15.9) and captive-born litters (15.5) are within the ranges reported from other gartersnakes, but captive-born litters had high rates of stillbirth. Only 154 of 202 neonates from captive snakes were born alive, and seven of 13 litters contained at least one stillborn neonate. We found that fecundity was positively related to maternal snout-vent length, and some evidence that larger litters contained smaller neonates. The proportion of X-rayed females that were gravid was 0.50 in 2014, 0.47 in and 2015, and 0.64 in 2016. Central California experienced an exceptional drought from 2012–2015, which may have affected the reproductive output and frequency of T. gigas. Our estimates of reproductive frequency and size-dependent fecundity in T. gigas provide valuable information that can be used in demographic models of this threatened species. Our results demonstrate that X-radiography is a useful, minimally invasive means to study fecundity in wild populations of snakes.
","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Rose, J.P., Ersan, J., Wylie, G., Casazza, M.L., and Halstead, B., 2018, Reproductive frequency and size-dependence of fecundity in the Giant Gartersnake (Thamnophis gigas): Herpetological Conservation and Biology, v. 13, no. 1, p. 80-90.","productDescription":"11 p.","startPage":"80","endPage":"90","ipdsId":"IP-087900","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":367252,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":367242,"type":{"id":15,"text":"Index Page"},"url":"https://herpconbio.org/contents_vol13_issue1.html"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.354736328125,\n 38.37611542403604\n ],\n [\n -120.69580078125001,\n 38.37611542403604\n ],\n [\n -120.69580078125001,\n 39.85072092501597\n ],\n [\n -122.354736328125,\n 39.85072092501597\n ],\n [\n -122.354736328125,\n 38.37611542403604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rose, Jonathan P. 0000-0003-0874-9166 jprose@usgs.gov","orcid":"https://orcid.org/0000-0003-0874-9166","contributorId":199339,"corporation":false,"usgs":true,"family":"Rose","given":"Jonathan","email":"jprose@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":770354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ersan, Julia 0000-0002-1549-7561","orcid":"https://orcid.org/0000-0002-1549-7561","contributorId":218034,"corporation":false,"usgs":true,"family":"Ersan","given":"Julia","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":770355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Glenn D. 0000-0002-7061-6658","orcid":"https://orcid.org/0000-0002-7061-6658","contributorId":207594,"corporation":false,"usgs":false,"family":"Wylie","given":"Glenn D.","affiliations":[],"preferred":false,"id":770357,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":770356,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":770353,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196760,"text":"70196760 - 2018 - Associations between urban sprawl and life expectancy in the United States","interactions":[],"lastModifiedDate":"2018-04-30T13:10:32","indexId":"70196760","displayToPublicDate":"2018-04-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2041,"text":"International Journal of Environmental Research and Public Health","active":true,"publicationSubtype":{"id":10}},"title":"Associations between urban sprawl and life expectancy in the United States","docAbstract":"In recent years, the United States has had a relatively poor performance with respect to life expectancy compared to the other developed nations. Urban sprawl is one of the potential causes of the high rate of mortality in the United States. This study investigated cross-sectional associations between sprawl and life expectancy for metropolitan counties in the United States in 2010. In this study, the measure of life expectancy in 2010 came from a recently released dataset of life expectancies by county. This study modeled average life expectancy with a structural equation model that included five mediators: annual vehicle miles traveled (VMT) per household, average body mass index, crime rate, and air quality index as mediators of sprawl, as well as percentage of smokers as a mediator of socioeconomic status. After controlling for sociodemographic characteristics, this study found that life expectancy was significantly higher in compact counties than in sprawling counties. Compactness affects mortality directly, but the causal mechanism is unclear. For example, it may be that sprawling areas have higher traffic speeds and longer emergency response times, lower quality and less accessible health care facilities, or less availability of healthy foods. Compactness affects mortality indirectly through vehicle miles traveled, which is a contributor to traffic fatalities, and through body mass index, which is a contributor to many chronic diseases. This study identified significant direct and indirect associations between urban sprawl and life expectancy. These findings support further research and practice aimed at identifying and implementing changes to urban planning designed to support health and healthy behaviors.
","language":"English","publisher":"MDPI","doi":"10.3390/ijerph15050861","usgsCitation":"Hamidi, S., Ewing, R., Tatalovich, Z., Grace, J.B., and Berrigan, D., 2018, Associations between urban sprawl and life expectancy in the United States: International Journal of Environmental Research and Public Health, v. 15, no. 5, p. 1-11, https://doi.org/10.3390/ijerph15050861.","productDescription":"Article 861; 11 p.","startPage":"1","endPage":"11","ipdsId":"IP-056461","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468802,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/ijerph15050861","text":"Publisher Index Page"},{"id":353857,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"5","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-26","publicationStatus":"PW","scienceBaseUri":"5afee6cde4b0da30c1bfbe24","contributors":{"authors":[{"text":"Hamidi, Shima","contributorId":204538,"corporation":false,"usgs":false,"family":"Hamidi","given":"Shima","email":"","affiliations":[{"id":12734,"text":"University of Texas at Arlington","active":true,"usgs":false}],"preferred":false,"id":734278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ewing, Reid","contributorId":204537,"corporation":false,"usgs":false,"family":"Ewing","given":"Reid","email":"","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":734277,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tatalovich, Zaria","contributorId":204539,"corporation":false,"usgs":false,"family":"Tatalovich","given":"Zaria","email":"","affiliations":[{"id":36952,"text":"National Cancer Institute, NIH","active":true,"usgs":false}],"preferred":false,"id":734279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":734276,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berrigan, David","contributorId":204540,"corporation":false,"usgs":false,"family":"Berrigan","given":"David","email":"","affiliations":[{"id":36952,"text":"National Cancer Institute, NIH","active":true,"usgs":false}],"preferred":false,"id":734280,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197481,"text":"70197481 - 2018 - Biology and impacts of Pacific Islands invasive species. 14. Sus scrofa the feral pig (Artiodactyla: Suidae)","interactions":[],"lastModifiedDate":"2018-06-06T16:21:37","indexId":"70197481","displayToPublicDate":"2018-04-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2990,"text":"Pacific Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Biology and impacts of Pacific Islands invasive species. 14. Sus scrofa the feral pig (Artiodactyla: Suidae)","title":"Biology and impacts of Pacific Islands invasive species. 14. Sus scrofa the feral pig (Artiodactyla: Suidae)","docAbstract":"Feral pigs (Sus scrofa L.) are perhaps the most abundant, widespread, and economically significant large introduced vertebrate across the Pacific island region. Unlike many other nonnative invasive species, feral pigs have both cultural and recreational importance in the region, complicating their management. Today, Pacific island feral pigs are a mixture of several strains of domestic swine, Asiatic wild boar, and European wild boar. Due to their generalist diet and rooting behavior, feral pigs alter soils and watersheds and negatively impact native and nonnative flora and fauna. As a result, feral pigs have played a role in the extinction of several species of plants and animals on Pacific islands and have negative effects on both ecotourism and agricultural industries in the region. Despite numerous published studies on feral pigs in the Pacific island region, of which the majority include systematic analyses of original empirical data, some fundamental aspects of feral pig ecology remain poorly characterized, at least partly due to the remote and inaccessible environments that they often inhabit. To address these knowledge gaps, effort should be made to integrate research conducted outside the Pacific island region into local management strategies. This review summarizes the origins, history, ecology, environmental effects, and current management of feral pigs in the Pacific island region; integrates regional scientific findings with those of other insular and continental systems; and identifies current knowledge gaps requiring further research to inform the ecology and management of this impactful invasive species.
","language":"English ","publisher":"University of Hawai'i Press","doi":"10.2984/72.2.1","usgsCitation":"Wehr, N., Hess, S.C., and Litton, C.M., 2018, Biology and impacts of Pacific Islands invasive species. 14. Sus scrofa the feral pig (Artiodactyla: Suidae): Pacific Science, v. 72, no. 2, p. 177-198, https://doi.org/10.2984/72.2.1.","productDescription":"22 p.","startPage":"177","endPage":"198","numberOfPages":"22","ipdsId":"IP-080121","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":354773,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Pacific Islands","volume":"72","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b46e58ee4b060350a15d1da","contributors":{"authors":[{"text":"Wehr, Nathaniel H. ","contributorId":205455,"corporation":false,"usgs":false,"family":"Wehr","given":"Nathaniel H. ","affiliations":[{"id":33542,"text":"Department of Natural Resources and Environmental Management, University of Hawai‘i at Mānoa, Honolulu, Hawaii","active":true,"usgs":false}],"preferred":false,"id":737354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hess, Steven C. 0000-0001-6403-9922 shess@usgs.gov","orcid":"https://orcid.org/0000-0001-6403-9922","contributorId":3156,"corporation":false,"usgs":true,"family":"Hess","given":"Steven","email":"shess@usgs.gov","middleInitial":"C.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":false,"id":737352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Litton, Creighton M.","contributorId":58534,"corporation":false,"usgs":true,"family":"Litton","given":"Creighton","email":"","middleInitial":"M.","affiliations":[{"id":34391,"text":"Department of Natural Resources and Environmental Management, University of Hawai‘i at Mānoa, Honolulu, Hawaii 96822","active":true,"usgs":false}],"preferred":false,"id":737353,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197755,"text":"70197755 - 2018 - The widespread influence of Great Lakes microseisms across the United States revealed by the 2014 polar vortex","interactions":[],"lastModifiedDate":"2018-06-19T16:38:07","indexId":"70197755","displayToPublicDate":"2018-04-28T00:00:00","publicationYear":"2018","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 widespread influence of Great Lakes microseisms across the United States revealed by the 2014 polar vortex","docAbstract":"During the winter of 2014, a weak polar vortex brought record cold temperatures to the north‐central (“Midwest”) United States, and the Great Lakes reached the highest extent of ice coverage (92.5%) since 1979. This event shut down the generation of seismic signals caused by wind‐driven wave action within the lakes (termed “lake microseisms”), giving an unprecedented opportunity to isolate and characterize these novel signals through comparison with nonfrozen time periods. Using seismic records at 72 broadband stations, we observe Great Lakes microseism signals at distances >300 km from the lakes. In contrast to conventional oceanic microseisms, there is no clear relationship between the frequency content of the seismic signals (observed from ~0.5–5‐s period) and the dominant swell period or resonance periods of the lakes based on their bathymetric profiles. Thus, the exact generation mechanism is not readily explained by conventional microseism theory and warrants further investigation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017GL076690","usgsCitation":"Anthony, R.E., Ringler, A.T., and Wilson, D.C., 2018, The widespread influence of Great Lakes microseisms across the United States revealed by the 2014 polar vortex: Geophysical Research Letters, v. 45, no. 8, p. 3436-3444, https://doi.org/10.1002/2017GL076690.","productDescription":"9 p.","startPage":"3436","endPage":"3444","ipdsId":"IP-095199","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":468803,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017gl076690","text":"Publisher Index Page"},{"id":355175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Great Lakes","volume":"45","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-20","publicationStatus":"PW","scienceBaseUri":"5b46e58fe4b060350a15d1dc","contributors":{"authors":[{"text":"Anthony, Robert 0000-0001-7089-8846 reanthony@usgs.gov","orcid":"https://orcid.org/0000-0001-7089-8846","contributorId":202829,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert","email":"reanthony@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":738393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":145576,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":738394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, David C. 0000-0003-2582-5159 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-5159","contributorId":145580,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":738395,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195291,"text":"ofr20181010 - 2018 - Laboratory observations of artificial sand and oil agglomerates","interactions":[],"lastModifiedDate":"2018-04-30T10:54:20","indexId":"ofr20181010","displayToPublicDate":"2018-04-27T15:45:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1010","title":"Laboratory observations of artificial sand and oil agglomerates","docAbstract":"Sand and oil agglomerates (SOAs) form when weathered oil reaches the surf zone and combines with suspended sediments. The presence of large SOAs in the form of thick mats (up to 10 centimeters [cm] in height and up to 10 square meters [m2] in area) and smaller SOAs, sometimes referred to as surface residual balls (SRBs), may lead to the re-oiling of beaches previously affected by an oil spill. A limited number of numerical modeling and field studies exist on the transport and dynamics of centimeter-scale SOAs and their interaction with the sea floor. Numerical models used to study SOAs have relied on shear-stress formulations to predict incipient motion. However, uncertainty exists as to the accuracy of applying these formulations, originally developed for sand grains in a uniformly sorted sediment bed, to larger, nonspherical SOAs. In the current effort, artificial sand and oil agglomerates (aSOAs) created with the size, density, and shape characteristics of SOAs were studied in a small-oscillatory flow tunnel. These experiments expanded the available data on SOA motion and interaction with the sea floor and were used to examine the applicability of shear-stress formulations to predict SOA mobility. Data collected during these two sets of experiments, including photographs, video, and flow velocity, are presented in this report, along with an analysis of shear-stress-based formulations for incipient motion. The results showed that shear-stress thresholds for typical quartz sand predicted the incipient motion of aSOAs with 0.5–1.0-cm diameters, but were inaccurate for aSOAs with larger diameters (>2.5 cm). This finding implies that modified parameterizations of incipient motion may be necessary under certain combinations of aSOA characteristics and environmental conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181010","usgsCitation":"Jenkins, R.L., Dalyander, P.S., Penko, Allison, and Long, J.W., 2018, Laboratory observations of artificial sand and oil agglomerates: U.S. Geological Survey Open-File Report 2018–1010, https://doi.org/10.3133/ofr20181010.","productDescription":"HTML","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079703","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":353721,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1010","text":"Report HTML"},{"id":353720,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1010/coverthb2.jpg"}],"contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
The objective of this chapter is to create a methodology to model the emergent behavior during a disruption in the transportation system and that calculates economic losses due to such a disruption, and to understand how an extreme event affects the road transportation network. The chapter discusses a system dynamics approach which is used to model the transportation road infrastructure system to evaluate the different factors that render road segments inoperable and calculate economic consequences of such inoperability. System dynamics models have been integrated with business process simulation model to evaluate, design, and optimize the business process. The chapter also explains how different factors affect the road capacity. After identifying the various factors affecting the available road capacity, a causal loop diagram (CLD) is created to visually represent the causes leading to a change in the available road capacity and the effects on travel costs when the available road capacity changes.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Emergent behavior in complex systems engineering: A modeling and simulation approach","language":"English","publisher":"Wiley","doi":"10.1002/9781119378952.ch15","usgsCitation":"Ojha, A., Corns, S., Shoberg, T.G., Qin, R., and Long, S.K., 2018, Modeling and simulation of emergent behavior in transportation infrastructure restoration, chap. of Emergent behavior in complex systems engineering: A modeling and simulation approach, p. 249-368, https://doi.org/10.1002/9781119378952.ch15.","productDescription":"120 p.","startPage":"249","endPage":"368","ipdsId":"IP-087912","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":353780,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-16","publicationStatus":"PW","scienceBaseUri":"5afee6cde4b0da30c1bfbe2a","contributors":{"authors":[{"text":"Ojha, Akhilesh","contributorId":204482,"corporation":false,"usgs":false,"family":"Ojha","given":"Akhilesh","email":"","affiliations":[{"id":36947,"text":"Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO, 65401","active":true,"usgs":false}],"preferred":false,"id":734135,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corns, Steven","contributorId":146271,"corporation":false,"usgs":false,"family":"Corns","given":"Steven","affiliations":[{"id":16655,"text":"Dept. of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO","active":true,"usgs":false}],"preferred":false,"id":734136,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shoberg, Thomas G. 0000-0003-0173-1246 tshoberg@usgs.gov","orcid":"https://orcid.org/0000-0003-0173-1246","contributorId":3764,"corporation":false,"usgs":true,"family":"Shoberg","given":"Thomas","email":"tshoberg@usgs.gov","middleInitial":"G.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":734134,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qin, Ruwen","contributorId":204483,"corporation":false,"usgs":false,"family":"Qin","given":"Ruwen","email":"","affiliations":[{"id":36947,"text":"Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO, 65401","active":true,"usgs":false}],"preferred":false,"id":734137,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Long, Suzanna K.","contributorId":146270,"corporation":false,"usgs":false,"family":"Long","given":"Suzanna","email":"","middleInitial":"K.","affiliations":[{"id":16655,"text":"Dept. of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO","active":true,"usgs":false}],"preferred":false,"id":734138,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196725,"text":"70196725 - 2018 - Factors influencing elk recruitment across ecotypes in the Western United States","interactions":[],"lastModifiedDate":"2018-09-10T15:10:53","indexId":"70196725","displayToPublicDate":"2018-04-27T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Factors influencing elk recruitment across ecotypes in the Western United States","docAbstract":"Ungulates are key components in ecosystems and economically important for sport and subsistence harvest. Yet the relative importance of the effects of weather conditions, forage productivity, and carnivores on ungulates are not well understood. We examined changes in elk (Cervus canadensis) recruitment (indexed as age ratios) across 7 states and 3 ecotypes in the northwestern United States during 1989–2010, while considering the effects of predator richness, forage productivity, and precipitation. We found a broad‐scale, long‐term decrease in elk recruitment of 0.48 juveniles/100 adult females/year. Weather conditions (indexed as summer and winter precipitation) showed small, but measurable, influences on recruitment. Forage productivity on summer and winter ranges (indexed by normalized difference vegetation index [NDVI] metrics) had the strongest effect on elk recruitment relative to other factors. Relationships between forage productivity and recruitment varied seasonally and regionally. The productivity of winter habitat was more important in southern parts of the study area, whereas annual variation in productivity of summer habitat had more influence on recruitment in northern areas. Elk recruitment varied by up to 15 juveniles/100 adult females across the range of variation in forage productivity. Areas with more species of large carnivores had relatively low elk recruitment, presumably because of increased predation. Wolves (Canis lupus) were associated with a decrease of 5 juveniles/100 adult females, whereas grizzly bears (Ursus arctos) were associated with an additional decrease of 7 juveniles/100 adult females. Carnivore species can have a critical influence on ungulate recruitment because their influence rivals large ranges of variation in environmental conditions. A more pressing concern, however, stems from persistent broad‐scale decreases in recruitment across the distribution of elk in the northwestern United States, irrespective of carnivore richness. Our results suggest that wildlife managers interested in improving recruitment of elk consider the combined effects of habitat and predators. Efforts to manage summer and winter ranges to increase forage productivity may have a positive effect on recruitment.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21438","usgsCitation":"Lukacs, P.M., Mitchell, M.S., Hebblewhite, M., Johnson, B.K., Johnson, H.E., Kauffman, M., Proffitt, K.M., Zager, P., Brodie, J., Hersey, K., Holland, A.A., Hurley, M., McCorquodale, S., Middleton, A., Nordhagen, M., Nowak, J.J., Walsh, D.P., and White, P., 2018, Factors influencing elk recruitment across ecotypes in the Western United States: Journal of Wildlife Management, v. 82, no. 4, p. 698-710, https://doi.org/10.1002/jwmg.21438.","productDescription":"13 p.","startPage":"698","endPage":"710","ipdsId":"IP-063346","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":353779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"82","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-08","publicationStatus":"PW","scienceBaseUri":"5afee6cde4b0da30c1bfbe28","contributors":{"authors":[{"text":"Lukacs, Paul M.","contributorId":101240,"corporation":false,"usgs":true,"family":"Lukacs","given":"Paul","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":734180,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mitchell, Michael S. 0000-0002-0773-6905 mmitchel@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-6905","contributorId":3716,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"mmitchel@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":734181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hebblewhite, Mark","contributorId":190188,"corporation":false,"usgs":false,"family":"Hebblewhite","given":"Mark","email":"","affiliations":[],"preferred":false,"id":734182,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Bruce K.","contributorId":204502,"corporation":false,"usgs":false,"family":"Johnson","given":"Bruce","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":734183,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Heather E. 0000-0001-5392-7676 hejohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5392-7676","contributorId":205919,"corporation":false,"usgs":true,"family":"Johnson","given":"Heather","email":"hejohnson@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":734184,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900 mkauffman@usgs.gov","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":189179,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew J.","email":"mkauffman@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":false,"id":734185,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Proffitt, Kelly M.","contributorId":106783,"corporation":false,"usgs":true,"family":"Proffitt","given":"Kelly","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":734186,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zager, Peter","contributorId":16737,"corporation":false,"usgs":true,"family":"Zager","given":"Peter","email":"","affiliations":[],"preferred":false,"id":734187,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brodie, Jedediah","contributorId":63706,"corporation":false,"usgs":true,"family":"Brodie","given":"Jedediah","affiliations":[],"preferred":false,"id":734188,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hersey, Kent","contributorId":99873,"corporation":false,"usgs":false,"family":"Hersey","given":"Kent","affiliations":[{"id":6763,"text":"Utah Division of Wildlife Resources, Salt Lake City, Utah","active":true,"usgs":false}],"preferred":false,"id":734189,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Holland, A. Andrew","contributorId":171542,"corporation":false,"usgs":false,"family":"Holland","given":"A.","email":"","middleInitial":"Andrew","affiliations":[],"preferred":false,"id":734190,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hurley, Mark","contributorId":58174,"corporation":false,"usgs":true,"family":"Hurley","given":"Mark","affiliations":[],"preferred":false,"id":734191,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"McCorquodale, Scott","contributorId":28515,"corporation":false,"usgs":true,"family":"McCorquodale","given":"Scott","affiliations":[],"preferred":false,"id":734192,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Middleton, Arthur","contributorId":39274,"corporation":false,"usgs":true,"family":"Middleton","given":"Arthur","affiliations":[],"preferred":false,"id":734193,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Nordhagen, Matthew","contributorId":189127,"corporation":false,"usgs":false,"family":"Nordhagen","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":734194,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Nowak, J. Joshua","contributorId":171707,"corporation":false,"usgs":false,"family":"Nowak","given":"J.","email":"","middleInitial":"Joshua","affiliations":[],"preferred":false,"id":734195,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Walsh, Daniel P. 0000-0002-7772-2445 dwalsh@usgs.gov","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":4758,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"dwalsh@usgs.gov","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":734196,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"White, P.J.","contributorId":91436,"corporation":false,"usgs":true,"family":"White","given":"P.J.","affiliations":[],"preferred":false,"id":734197,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70196038,"text":"sir20185042 - 2018 - A metabolism-based whole lake eutrophication model to estimate the magnitude and time scales of the effects of restoration in Upper Klamath Lake, south-central Oregon","interactions":[],"lastModifiedDate":"2018-04-30T11:11:57","indexId":"sir20185042","displayToPublicDate":"2018-04-27T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5042","title":"A metabolism-based whole lake eutrophication model to estimate the magnitude and time scales of the effects of restoration in Upper Klamath Lake, south-central Oregon","docAbstract":"A whole lake eutrophication (WLE) model approach for phosphorus and cyanobacterial biomass in Upper Klamath Lake, south-central Oregon, is presented here. The model is a successor to a previous model developed to inform a Total Maximum Daily Load (TMDL) for phosphorus in the lake, but is based on net primary production (NPP), which can be calculated from dissolved oxygen, rather than scaling up a small-scale description of cyanobacterial growth and respiration rates. This phase 3 WLE model is a refinement of the proof-of-concept developed in phase 2, which was the first attempt to use NPP to simulate cyanobacteria in the TMDL model. The calibration of the calculated NPP WLE model was successful, with performance metrics indicating a good fit to calibration data, and the calculated NPP WLE model was able to simulate mid-season bloom decreases, a feature that previous models could not reproduce.
In order to use the model to simulate future scenarios based on phosphorus load reduction, a multivariate regression model was created to simulate NPP as a function of the model state variables (phosphorus and chlorophyll a) and measured meteorological and temperature model inputs. The NPP time series was split into a low- and high-frequency component using wavelet analysis, and regression models were fit to the components separately, with moderate success.
The regression models for NPP were incorporated in the WLE model, referred to as the “scenario” WLE (SWLE), and the fit statistics for phosphorus during the calibration period were mostly unchanged. The fit statistics for chlorophyll a, however, were degraded. These statistics are still an improvement over prior models, and indicate that the SWLE is appropriate for long-term predictions even though it misses some of the seasonal variations in chlorophyll a.
The complete whole lake SWLE model, with multivariate regression to predict NPP, was used to make long-term simulations of the response to 10-, 20-, and 40-percent reductions in tributary nutrient loads. The long-term mean water column concentration of total phosphorus was reduced by 9, 18, and 36 percent, respectively, in response to these load reductions. The long-term water column chlorophyll a concentration was reduced by 4, 13, and 44 percent, respectively. The adjustment to a new equilibrium between the water column and sediments occurred over about 30 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185042","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Wherry, S.A., and Wood, T.M., 2018, A metabolism-based whole lake eutrophication model to estimate the magnitude and time scales of the effects of restoration in Upper Klamath Lake, south-central Oregon: U.S. Geological Survey Scientific Investigations Report 2018–5042, 43 p., https:/doi.org/10.3133/sir20185042.","productDescription":"vii, 43 p.","onlineOnly":"Y","ipdsId":"IP-081297","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":353789,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5042/coverthb.jpg"},{"id":353790,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5042/sir20185042.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5042"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.10273742675781,\n 42.22750046697999\n ],\n [\n -121.79374694824219,\n 42.22750046697999\n ],\n [\n -121.79374694824219,\n 42.595554553719204\n ],\n [\n -122.10273742675781,\n 42.595554553719204\n ],\n [\n -122.10273742675781,\n 42.22750046697999\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819