Water data networks are increasingly being integrated to answer complex scientific questions that often span large geographical areas and cross political borders. Data heterogeneity is a major obstacle that impedes interoperability within and between such networks. It is resolved here for groundwater data at five levels of interoperability, within a Spatial Data Infrastructure architecture. The result is a pair of distinct national groundwater data networks for the United States and Canada, and a combined data network in which they are interoperable. This combined data network enables, for the first time, transparent public access to harmonized groundwater data from both sides of the shared international border.
","language":"English","publisher":"IWA Publishing","doi":"10.2166/hydro.2015.242","usgsCitation":"Brodaric, B., Booth, N., Boisvert, E., and Lucido, J., 2016, Groundwater data network interoperability: Journal of Hydroinformatics, v. 18, no. 1, p. 210-225, https://doi.org/10.2166/hydro.2015.242.","productDescription":"16 p.","startPage":"210","endPage":"225","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064679","costCenters":[],"links":[{"id":471140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2166/hydro.2015.242","text":"Publisher Index Page"},{"id":318936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-28","publicationStatus":"PW","scienceBaseUri":"56ebc71de4b0f59b85d99422","contributors":{"authors":[{"text":"Brodaric, Boyan","contributorId":80341,"corporation":false,"usgs":true,"family":"Brodaric","given":"Boyan","affiliations":[],"preferred":false,"id":622902,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Booth, Nathaniel 0000-0001-6040-1031 nlbooth@usgs.gov","orcid":"https://orcid.org/0000-0001-6040-1031","contributorId":140641,"corporation":false,"usgs":true,"family":"Booth","given":"Nathaniel","email":"nlbooth@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":622903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boisvert, Eric","contributorId":167613,"corporation":false,"usgs":false,"family":"Boisvert","given":"Eric","email":"","affiliations":[{"id":24780,"text":"Geological Survey of Canada, Quebec, QC, Canada","active":true,"usgs":false}],"preferred":false,"id":622904,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lucido, Jessica M. jlucido@usgs.gov","contributorId":4695,"corporation":false,"usgs":true,"family":"Lucido","given":"Jessica M.","email":"jlucido@usgs.gov","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":622901,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169063,"text":"70169063 - 2016 - Ecological resistance in urban streams: the role of natural and legacy attributes","interactions":[],"lastModifiedDate":"2016-03-17T10:45:06","indexId":"70169063","displayToPublicDate":"2016-03-17T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Ecological resistance in urban streams: the role of natural and legacy attributes","docAbstract":"Urbanization substantially changes the physicochemical and biological characteristics of streams. The trajectory of negative effect is broadly similar around the world, but the nature and magnitude of ecological responses to urban growth differ among locations. Some heterogeneity in response arises from differences in the level of urban development and attributes of urban water management. However, the heterogeneity also may arise from variation in hydrologic, biological, and physicochemical templates that shaped stream ecosystems before urban development. We present a framework to develop hypotheses that predict how natural watershed and channel attributes in the pre-urban-development state may confer ecological resistance to urbanization. We present 6 testable hypotheses that explore the expression of such attributes under our framework: 1) greater water storage capacity mitigates hydrologic regime shifts, 2) coarse substrates and a balance between erosive forces and sediment supply buffer morphological changes, 3) naturally high ionic concentrations and pH pre-adapt biota to water-quality stress, 4) metapopulation connectivity results in retention of species richness, 5) high functional redundancy buffers trophic function from species loss, and 6) landuse history mutes or reverses the expected trajectory of eutrophication. Data from past comparative analyses support these hypotheses, but rigorous testing will require targeted investigations that account for confounding or interacting factors, such as diversity in urban infrastructure attributes. Improved understanding of the susceptibility or resistance of stream ecosystems could substantially strengthen conservation, management, and monitoring efforts in urban streams. We hope that these preliminary, conceptual hypotheses will encourage others to explore these ideas further and generate additional explanations for the heterogeneity observed in urban streams.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/684839","usgsCitation":"Utz, R.M., Hopkins, K., Beesley, L., Booth, D.B., Hawley, R.J., Baker, M.E., Freeman, M., and Jones, K.L., 2016, Ecological resistance in urban streams: the role of natural and legacy attributes: Freshwater Science, v. 35, no. 1, p. 380-397, https://doi.org/10.1086/684839.","productDescription":"18 p.","startPage":"380","endPage":"397","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066391","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":318934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56ebc71be4b0f59b85d99410","contributors":{"authors":[{"text":"Utz, Ryan M.","contributorId":167572,"corporation":false,"usgs":false,"family":"Utz","given":"Ryan","email":"","middleInitial":"M.","affiliations":[{"id":24756,"text":"Chatham University, Pittsburg PA","active":true,"usgs":false}],"preferred":false,"id":622742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hopkins, Kristina G.","contributorId":167573,"corporation":false,"usgs":false,"family":"Hopkins","given":"Kristina G.","affiliations":[{"id":24757,"text":"University of Maryland, Annapolis MD","active":true,"usgs":false}],"preferred":false,"id":622743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beesley, Leah","contributorId":146250,"corporation":false,"usgs":false,"family":"Beesley","given":"Leah","email":"","affiliations":[{"id":16644,"text":"Centre of Excellence in Natural Resource Management, University of Western Australia,","active":true,"usgs":false}],"preferred":false,"id":622744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Booth, Derek B.","contributorId":100873,"corporation":false,"usgs":false,"family":"Booth","given":"Derek","email":"","middleInitial":"B.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":622745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hawley, Robert J.","contributorId":167574,"corporation":false,"usgs":false,"family":"Hawley","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":24758,"text":"Sustainable Streams, LLC, Louisville, KY","active":true,"usgs":false}],"preferred":false,"id":622746,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baker, Matthew E.","contributorId":149189,"corporation":false,"usgs":false,"family":"Baker","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":17665,"text":"Department of Geography and Environmental Systems, University of Maryland, Baltimore County, Baltimore, Maryland, US","active":true,"usgs":false}],"preferred":false,"id":622747,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":622741,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jones, Krista L. 0000-0002-0301-4497 kljones@usgs.gov","orcid":"https://orcid.org/0000-0002-0301-4497","contributorId":4550,"corporation":false,"usgs":true,"family":"Jones","given":"Krista","email":"kljones@usgs.gov","middleInitial":"L.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":622748,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70169079,"text":"70169079 - 2016 - Building the team for team science","interactions":[],"lastModifiedDate":"2016-03-17T09:55:59","indexId":"70169079","displayToPublicDate":"2016-03-17T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Building the team for team science","docAbstract":"The ability to effectively exchange information and develop trusting, collaborative relationships across disciplinary boundaries is essential for 21st century scientists charged with solving complex and large-scale societal and environmental challenges, yet these communication skills are rarely taught. Here, we describe an adaptable training program designed to increase the capacity of scientists to engage in information exchange and relationship development in team science settings. A pilot of the program, developed by a leader in ecological network science, the Global Lake Ecological Observatory Network (GLEON), indicates that the training program resulted in improvement in early career scientists’ confidence in team-based network science collaborations within and outside of the program. Fellows in the program navigated human-network challenges, expanded communication skills, and improved their ability to build professional relationships, all in the context of producing collaborative scientific outcomes. Here, we describe the rationale for key communication training elements and provide evidence that such training is effective in building essential team science skills.
","language":"English","publisher":"John Wiley & Sons, Ltd","doi":"10.1002/ecs2.1291","usgsCitation":"Read, E.K., O’Rourke, M., Hong, G.S., Hanson, P., Winslow, L., Crowley, S., Brewer, C., and Weathers, K.C., 2016, Building the team for team science: Ecosphere, v. 7, no. 3, e01291: 9 p., https://doi.org/10.1002/ecs2.1291.","productDescription":"e01291: 9 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065878","costCenters":[],"links":[{"id":471142,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1291","text":"Publisher Index Page"},{"id":318932,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-14","publicationStatus":"PW","scienceBaseUri":"56ebc719e4b0f59b85d99403","contributors":{"authors":[{"text":"Read, Emily K. 0000-0002-9617-9433 eread@usgs.gov","orcid":"https://orcid.org/0000-0002-9617-9433","contributorId":5815,"corporation":false,"usgs":true,"family":"Read","given":"Emily","email":"eread@usgs.gov","middleInitial":"K.","affiliations":[{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":false,"id":622807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Rourke, M.","contributorId":167595,"corporation":false,"usgs":false,"family":"O’Rourke","given":"M.","email":"","affiliations":[{"id":24768,"text":"Department of Philosophy, and AgBioResearch, Michigan State University, East Lansing, Michigan, 48824, USA.","active":true,"usgs":false}],"preferred":false,"id":622808,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hong, G. 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We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30 × 30 m resolution predictions for more than 38,000 km2 of diverse coastal landscape in the northeastern United States. Probabilistic SLR projections, coastal elevation and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response versus inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge. This approach is well suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/NCLIMATE2957","usgsCitation":"Lentz, E.E., Thieler, E.R., Plant, N.G., Stippa, S., Horton, R., and Gesch, D.B., 2016, Evaluation of dynamic coastal response to sea-level rise modifies inundation likelihood: Nature Climate Change, v. 6, p. 696-700, https://doi.org/10.1038/NCLIMATE2957.","productDescription":"5 p.","startPage":"696","endPage":"700","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062287","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471141,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1912/8260","text":"External Repository"},{"id":318931,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-14","publicationStatus":"PW","scienceBaseUri":"56ebc71ce4b0f59b85d99418","contributors":{"authors":[{"text":"Lentz, Erika E. elentz@usgs.gov","contributorId":167611,"corporation":false,"usgs":true,"family":"Lentz","given":"Erika","email":"elentz@usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":622852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thieler, E. 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Studies of predation by free-ranging carnivores have particularly benefited from the application of location clustering algorithms to determine when and where predation events occur. These studies have changed our understanding of large carnivore behavior, but the gains have concentrated on obligate carnivores. Facultative carnivores, such as grizzly/brown bears (Ursus arctos), exhibit a variety of behaviors that can lead to the formation of GPS clusters. We combined clustering techniques with field site investigations of grizzly bear GPS locations (n = 732 site investigations; 2004–2011) to produce 174 GPS clusters where documented behavior was partitioned into five classes (large-biomass carcass, small-biomass carcass, old carcass, non-carcass activity, and resting). We used multinomial logistic regression to predict the probability of clusters belonging to each class. Two cross-validation methods—leaving out individual clusters, or leaving out individual bears—showed that correct prediction of bear visitation to large-biomass carcasses was 78–88%, whereas the false-positive rate was 18–24%. As a case study, we applied our predictive model to a GPS data set of 266 bear-years in the Greater Yellowstone Ecosystem (2002–2011) and examined trends in carcass visitation during fall hyperphagia (September–October). We identified 1997 spatial GPS clusters, of which 347 were predicted to be large-biomass carcasses. We used the clustered data to develop a carcass visitation index, which varied annually, but more than doubled during the study period. Our study demonstrates the effectiveness and utility of identifying GPS clusters associated with carcass visitation by a facultative carnivore.
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A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. 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2015","interactions":[],"lastModifiedDate":"2020-03-18T06:43:44","indexId":"70209112","displayToPublicDate":"2016-03-17T07:26:46","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Report A: Fish and habitat assessment in Rock Creek, Klickitat County, Washington, June 2013-December 2015","docAbstract":"The U.S. Geological Survey (USGS) and the Yakama Nation have collaborated in the Rock Creek subbasin, southeastern, Washington since 2009 to assess steelhead (Oncorynchus mykiss) populations and habitat conditions. Rock Creek, flows south to the Columbia River at river kilometer (rkm) 368. During 2015, a habitat survey was conducted, and monitoring of Passive Integrated Transponder (PIT)-tagged salmonids in the Rock Creek subbasin continued. Two multiplexing PIT-tag interrogation systems (PTISs) were installed in Rock Creek in the fall of 2009 to evaluate timing and degree of salmonid movement, smolting success, stray rates, and other life history attributes. These have been monitored every year since, during the spring, fall, and winter months. Returning adult steelhead detection histories are summarized from past Rock Creek PIT-tagging efforts. Detection histories and detection efficiencies were used to estimate a smolt-to-adult return rate (SAR) for Rock Creek PIT-tagged steelhead (tagged from 2009 to 2012) that ranged from 2.2% to 5.5%. Additionally, a SAR was also estimated for Rock Creek PIT-tagged steelhead returning to Bonneville Dam, Columbia River (rkm 235). The SAR rate to Bonneville Dam was always higher (2.4% to 10.4%), indicating straying of adults to other sites for spawning potentially further upstream or in other tributaries, or pre-spawn mortality. Twenty-two Rock Creek PIT-tagged steelhead were detected returning to Rock Creek and 35 were detected at Bonneville Dam from past tagging efforts (2009 – 2012). Monitoring of the Rock Creek PTISs [Rock Creek Lower (RCL) and Rock Creek Squaw (RCS)] provide evidence for PIT-tagged salmonid use from fish tagged outside of Rock Creek subbasin (out-of-basin) origins. A total of 82 out-of-basin PIT-tagged fish have been detected at the Rock Creek PTISs since installation.\n\nThe habitat survey was conducted in Rock Creek from September 16 to October 7, 2015. The survey started at rkm 2 and continued upstream to rkm 29.3, and included portions of the major tributaries, ranging from 1 to 9 rkm survey length upstream from their confluence. During the survey, we measured the lengths of all dry and non-pool wet sections, and for pools: the length, wetted width, average residual depth, maximum residual depth, and temperature. During the 2015 survey of Rock Creek, 38% of the river between rkm 2 and 29 was classified as dry, with a higher relative proportion (57%) of dry being in the lower river section (rkm 2-13) than the upper river section (33%, rkm 14-22). This was higher than during survey years 2010 to 2012, which ranged from 29% to 43% in the lower river. As a result of the increase in dry area the percent of non-pool wet habitat was less (22%) than previous years (range 34% to 43%), as well as the percent of pools (21%). However, more river kilometers were surveyed in the lower river section (rkm 2-13) than previous years. For the 2015 survey length (rkm 2 to 29), 19% was classified as pools and 43% was non-pool wet. This work informs potential restoration actions by identifying the persistent pools across years and in years of low water flow (i.e., 2015). This work also provides a baseline to evaluate effectiveness of future restoration actions. Potential restoration actions could include headwater and upland restoration to improve base flows and pool habitat enhancement, through increased structure and vegetation plantings for increased cover.\n\nThe Rock Creek steelhead population remains to be an important cultural resource for the Rock Creek Band of the Yakama Nation Tribe. The Rock Creek steelhead population is part of the Cascades Eastern Slope major population group (MPG), one of four MPGs contributing to the Middle Columbia steelhead distinct population segment (DPS). The National Marine Fisheries Service recovery plan for Rock Creek (NMFS 2009a) identifies an overall biological recovery goal for Rock Creek steelhead to contribute to recovery of the Mid-Columbia D","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Rock Creek Fish and Habitat Assessment for Prioritization of Restoration and Protection Actions","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Bonneville Power Administration","collaboration":"Bonneville Power Administration; Yakama Nation","usgsCitation":"Hardiman, J.M., and Harvey, E., 2016, Report A: Fish and habitat assessment in Rock Creek, Klickitat County, Washington, June 2013-December 2015, 41 p.","productDescription":"41 p.","startPage":"A-1","endPage":"A-41","ipdsId":"IP-087695","costCenters":[{"id":654,"text":"Western Fisheries Research 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PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hardiman, Jill M. 0000-0002-3661-9695 jhardiman@usgs.gov","orcid":"https://orcid.org/0000-0002-3661-9695","contributorId":2672,"corporation":false,"usgs":true,"family":"Hardiman","given":"Jill","email":"jhardiman@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":784965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Elaine","contributorId":203907,"corporation":false,"usgs":false,"family":"Harvey","given":"Elaine","email":"","affiliations":[{"id":36750,"text":"Yakama Nation Fisheries, 4 Bickleton Hwy, Goldendale, WA 98620","active":true,"usgs":false}],"preferred":false,"id":784966,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70176236,"text":"70176236 - 2016 - Hydrologic indicators of hot spots and hot moments of mercury methylation potential along river corridors","interactions":[],"lastModifiedDate":"2018-08-07T12:45:36","indexId":"70176236","displayToPublicDate":"2016-03-17T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic indicators of hot spots and hot moments of mercury methylation potential along river corridors","docAbstract":"The biogeochemical cycling of metals and other contaminants in river-floodplain corridors is controlled by microbial activity responding to dynamic redox conditions. Riverine flooding thus has the potential to affect speciation of redox-sensitive metals such as mercury (Hg). Therefore, inundation history over a period of decades potentially holds information on past production of bioavailable Hg. We investigate this within a Northern California river system with a legacy of landscape-scale 19th century hydraulic gold mining. We combine hydraulic modeling, Hg measurements in sediment and biota, and first-order calculations of mercury transformation to assess the potential role of river floodplains in producing monomethylmercury (MMHg), a neurotoxin which accumulates in local and migratory food webs. We identify frequently inundated floodplain areas, as well as floodplain areas inundated for long periods. We quantify the probability of MMHg production potential (MPP) associated with hydrology in each sector of the river system as a function of the spatial patterns of overbank inundation and drainage, which affect long-term redox history of contaminated sediments. Our findings identify river floodplains as periodic, temporary, yet potentially important, loci of biogeochemical transformation in which contaminants may undergo change during limited periods of the hydrologic record. We suggest that inundation is an important driver of MPP in river corridors and that the entire flow history must be analyzed retrospectively in terms of inundation magnitude and frequency in order to accurately assess biogeochemical risks, rather than merely highlighting the largest floods or low-flow periods. MMHg bioaccumulation within the aquatic food web in this system may pose a major risk to humans and waterfowl that eat migratory salmonids, which are being encouraged to come up these rivers to spawn. There is a long-term pattern of MPP under the current flow regime that is likely to be accentuated by increasingly common large floods with extended duration.
","language":"English","publisher":"ScienceDirect","doi":"10.1016/j.scitotenv.2016.03.005","usgsCitation":"Singer, M.B., Harrison, L.R., Donovan, P.M., Blum, J.D., and Marvin-DiPasquale, M.C., 2016, Hydrologic indicators of hot spots and hot moments of mercury methylation potential along river corridors: Science of the Total Environment, v. 568, p. 697-711, https://doi.org/10.1016/j.scitotenv.2016.03.005.","productDescription":"15 p.","startPage":"697","endPage":"711","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071066","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":471145,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2016.03.005","text":"Publisher Index Page"},{"id":328234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Bear River, Feather River, Sacramento River, Yuba River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.01965332031249,\n 38.13887716726548\n ],\n [\n -122.01965332031249,\n 39.317300373271024\n ],\n [\n -121.2451171875,\n 39.317300373271024\n ],\n [\n -121.2451171875,\n 38.13887716726548\n ],\n [\n -122.01965332031249,\n 38.13887716726548\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"568","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57cd45abe4b0f2f0cec4cb4e","contributors":{"authors":[{"text":"Singer, Michael B.","contributorId":168369,"corporation":false,"usgs":false,"family":"Singer","given":"Michael","email":"","middleInitial":"B.","affiliations":[{"id":25268,"text":"University of St Andrews, UK","active":true,"usgs":false}],"preferred":false,"id":647993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Lee R.","contributorId":174322,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","email":"","middleInitial":"R.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":647994,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donovan, Patrick M.","contributorId":168368,"corporation":false,"usgs":false,"family":"Donovan","given":"Patrick","email":"","middleInitial":"M.","affiliations":[{"id":25267,"text":"Univ. of Michigan","active":true,"usgs":false}],"preferred":false,"id":647995,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blum, Joel D.","contributorId":83657,"corporation":false,"usgs":true,"family":"Blum","given":"Joel","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":647996,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Marvin-DiPasquale, Mark C. 0000-0002-8186-9167 mmarvin@usgs.gov","orcid":"https://orcid.org/0000-0002-8186-9167","contributorId":1485,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","email":"mmarvin@usgs.gov","middleInitial":"C.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":647992,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70168418,"text":"sir20165021 - 2016 - Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10","interactions":[],"lastModifiedDate":"2016-03-18T08:13:50","indexId":"sir20165021","displayToPublicDate":"2016-03-17T00:00:00","publicationYear":"2016","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":"2016-5021","title":"Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10","docAbstract":"The Albuquerque area of New Mexico has two principal sources of water: (1) groundwater from the Santa Fe Group aquifer system, and (2) surface water from the Rio Grande. From 1960 to 2002, pumping from the Santa Fe Group aquifer system caused groundwater levels to decline more than 120 feet while water-level declines along the Rio Grande in Albuquerque were generally less than 40 feet. These differences in water-level declines in the Albuquerque area have resulted in a great deal of interest in quantifying the river-aquifer interaction associated with the Rio Grande.
In 2003, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, acting as fiscal agent for the Middle Rio Grande Endangered Species Collaborative Program, and the U.S. Army Corps of Engineers, began a study to characterize the hydrogeology of the Rio Grande inner valley alluvial aquifer in the Albuquerque area of New Mexico. The study provides hydrologic data in order to enhance the understanding of rates of water leakage from the Rio Grande to the alluvial aquifer, groundwater flow through the aquifer, and discharge of water from the aquifer to riverside drains. The study area extends about 20 miles along the Rio Grande in the Albuquerque area. Piezometers and surface-water gages were installed in paired transects at eight locations. Nested piezometers, completed at various depths in the alluvial aquifer, and surface-water gages, installed in the Rio Grande and riverside drains, were instrumented with pressure transducers. Water-level and water-temperature data were collected from 2009 to 2010.
Water levels from the piezometers indicated that groundwater movement was usually away from the river towards the riverside drains. Annual mean horizontal groundwater gradients in the inner valley alluvial aquifer ranged from 0.0024 (I-25 East) to 0.0144 (Pajarito East). The median hydraulic conductivity values of the inner valley alluvial aquifer, determined from slug tests, ranged from 30 feet per day (ft/d) (Montaño) to 120 ft/d (Central) for paired transects, with a median hydraulic conductivity for all transects of 50 ft/d. Daily mean groundwater fluxes from the river through the inner valley alluvial aquifer computed using Darcy’s Law and the slug test results ranged from about 0.01 ft/d (Montaño West) to between 1.0 and 2.0 ft/d (Central East). Median annual groundwater fluxes from the river through the inner valley alluvial aquifer determined using the Suzuki-Stallman method was greatest at Alameda East (0.50 ft/d) and lowest at Alameda West (0.25 ft/d). The results from both methods agreed reasonably well.
Seepage investigations conducted by measuring discharge in the east and west riverside drains provided information for computing changes in flow within the drains and for evaluating results from Darcy’s Law and Suzuki-Stallman method flux calculations. Discharge measured in the east riverside drain between the Barelas Bridge and the I-25 bridge indicated that the flow in the east riverside drain increased by an average of 56.5 cubic feet per day per linear foot (ft3/d/ft) of drain. Discharge measured in the west riverside drain between the Central bridge and the I-25 bridge indicated that flow increased between west drain miles 0 and 4, an average of 53.8 ft3/d/ft of drain, and that flow increased between west drain miles 7 and 10, an average of 44.9 ft3/d/ft of drain. In comparison to the seepage measurement results, the groundwater fluxes from the river through the inner valley alluvial aquifer calculated from Darcy’s Law (qslug) and by the Suzuki-Stallman method (qheat) would account for 20–36 percent or 53–95 percent, respectively, of the total flow in the east riverside drain and 22–31 percent or 19–26 percent, respectively, of the total flow in the west drain. These results indicate that the drains likely also receive water from outside the inner valley.
The spatial variability of horizontal hydraulic gradients and groundwater fluxes can be primarily attributed to variability in the distances between the river and riverside drains throughout the study area and geologic heterogeneities in the alluvial aquifer. Temporal variability in the water levels, which control the horizontal hydraulic gradients and fluxes between the Rio Grande and the riverside drains, can be primarily attributed to seasonal fluctuations in river stage and irrigation practices.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165021","collaboration":"Prepared in cooperation with Bureau of Reclamation acting as fiscal agent for the Middle Rio Grande Endangered Species Collaborative Program","usgsCitation":"Rankin, D.R., Oelsner, G.P., McCoy, K.J., Moret, G.J.M., Worthington, J.A., and Bandy-Baldwin, K.M., 2016, Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10: U.S. Geological Survey Scientific Investigations Report 2016–5021, 89 p., https://dx.doi.org/10.3133/sir20165021.","productDescription":"viii, 89 p.","numberOfPages":"101","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-033038","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":318926,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5021/sir20165021.pdf","text":"Report","size":"5.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5021"},{"id":318925,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5021/coverthb.jpg"}],"country":"United States","state":"New Mexico","city":"Albuquerque","otherGeospatial":"Rio Grande","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.7266845703125,\n 34.95011635301367\n ],\n [\n -106.7266845703125,\n 35.290468565908775\n ],\n [\n -106.56051635742188,\n 35.290468565908775\n ],\n [\n -106.56051635742188,\n 34.95011635301367\n ],\n [\n -106.7266845703125,\n 34.95011635301367\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
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Albuquerque, NM 87109–1311
—The Southern Mule Deer is a mobile but non-migratory large mammal found throughout southern California and is a covered species in the San Diego Multi-Species Conservation Plan. We assessed deer movement and population connectivity across California State Route 67 and two smaller roads in eastern San Diego County using non-invasive genetic sampling. We collected deer scat pellets between April and November 2015, and genotyped pellets at 15 microsatellites and a sex determination marker. We successfully genotyped 71 unique individuals from throughout the study area and detected nine recapture events. Recaptures were generally found close to original capture locations (within 1.5 km). We did not detect recaptures across roads; however, pedigree analysis detected 21 first order relative pairs, of which approximately 20% were found across State Route 67. Exact tests comparing allele frequencies between groups of individuals in pre-defined geographic clusters detected significant genetic differentiation across State Route 67. In contrast, the assignment-based algorithm of STRUCTURE supported a single genetic cluster across the study area. Our data suggest that State Route 67 may reduce, but does not preclude, movement and gene flow of Southern Mule Deer.
","language":"English","publisher":"The Journal of the Western Section of The Wildlife Society","publisherLocation":"Albany, CA","usgsCitation":"Mitelberg, A., and Vandergast, A.G., 2016, Non-invasive genetic sampling of Southern Mule Deer (Odocoileus hemionus fuliginatus) reveals limited movement across California State Route 67 in San Diego County: Western Wildlife, v. 3, p. 8-18.","productDescription":"11 p.","startPage":"8","endPage":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-076907","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":325878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":325873,"type":{"id":15,"text":"Index Page"},"url":"https://wwjournal.org/index.php/current-volume"}],"country":"United States","state":"California","city":"San Diego","volume":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a072b9e4b060ce18fb2dda","contributors":{"authors":[{"text":"Mitelberg, Anna amitelberg@usgs.gov","contributorId":173293,"corporation":false,"usgs":true,"family":"Mitelberg","given":"Anna","email":"amitelberg@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":644117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergast, Amy G. 0000-0002-7835-6571 avandergast@usgs.gov","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":3963,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"avandergast@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":644116,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70168374,"text":"sir20165024 - 2016 - Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012","interactions":[],"lastModifiedDate":"2022-09-15T18:41:32.475293","indexId":"sir20165024","displayToPublicDate":"2016-03-16T14:00:00","publicationYear":"2016","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":"2016-5024","title":"Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012","docAbstract":"Estimates of the magnitude and frequency of floods are needed across Alaska for engineering design of transportation and water-conveyance structures, flood-insurance studies, flood-plain management, and other water-resource purposes. This report updates methods for estimating flood magnitude and frequency in Alaska and conterminous basins in Canada. Annual peak-flow data through water year 2012 were compiled from 387 streamgages on unregulated streams with at least 10 years of record. Flood-frequency estimates were computed for each streamgage using the Expected Moments Algorithm to fit a Pearson Type III distribution to the logarithms of annual peak flows. A multiple Grubbs-Beck test was used to identify potentially influential low floods in the time series of peak flows for censoring in the flood frequency analysis.
For two new regional skew areas, flood-frequency estimates using station skew were computed for stations with at least 25 years of record for use in a Bayesian least-squares regression analysis to determine a regional skew value. The consideration of basin characteristics as explanatory variables for regional skew resulted in improvements in precision too small to warrant the additional model complexity, and a constant model was adopted. Regional Skew Area 1 in eastern-central Alaska had a regional skew of 0.54 and an average variance of prediction of 0.45, corresponding to an effective record length of 22 years. Regional Skew Area 2, encompassing coastal areas bordering the Gulf of Alaska, had a regional skew of 0.18 and an average variance of prediction of 0.12, corresponding to an effective record length of 59 years. Station flood-frequency estimates for study sites in regional skew areas were then recomputed using a weighted skew incorporating the station skew and regional skew. In a new regional skew exclusion area outside the regional skew areas, the density of long-record streamgages was too sparse for regional analysis and station skew was used for all estimates. Final station flood frequency estimates for all study streamgages are presented for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities.
Regional multiple-regression analysis was used to produce equations for estimating flood frequency statistics from explanatory basin characteristics. Basin characteristics, including physical and climatic variables, were updated for all study streamgages using a geographical information system and geospatial source data. Screening for similar-sized nested basins eliminated hydrologically redundant sites, and screening for eligibility for analysis of explanatory variables eliminated regulated peaks, outburst peaks, and sites with indeterminate basin characteristics. An ordinary least‑squares regression used flood-frequency statistics and basin characteristics for 341 streamgages (284 in Alaska and 57 in Canada) to determine the most suitable combination of basin characteristics for a flood-frequency regression model and to explore regional grouping of streamgages for explaining variability in flood-frequency statistics across the study area. The most suitable model for explaining flood frequency used drainage area and mean annual precipitation as explanatory variables for the entire study area as a region. Final regression equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability discharge in Alaska and conterminous basins in Canada were developed using a generalized least-squares regression. The average standard error of prediction for the regression equations for the various annual exceedance probabilities ranged from 69 to 82 percent, and the pseudo-coefficient of determination (pseudo-R2) ranged from 85 to 91 percent.
The regional regression equations from this study were incorporated into the U.S. Geological Survey StreamStats program for a limited area of the State—the Cook Inlet Basin. StreamStats is a national web-based geographic information system application that facilitates retrieval of streamflow statistics and associated information. StreamStats retrieves published data for gaged sites and, for user-selected ungaged sites, delineates drainage areas from topographic and hydrographic data, computes basin characteristics, and computes flood frequency estimates using the regional regression equations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165024","collaboration":"Prepared in cooperation with the Alaska Department of Transportation and Public Facilities, Alaska Department of Natural Resources, and U.S. Army Corps of Engineers","usgsCitation":"Curran, J.H., Barth, N.A., Veilleux, A.G., and Ourso, R.T., 2016, Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012: U.S. Geological Survey Scientific Investigations Report 2016–5024, 47 p., https://dx.doi.org/10.3133/sir20165024.","productDescription":"Report: vi, 47 p.; 3 Tables; 1 Appendix; Companion File; Database","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2011-10-01","ipdsId":"IP-068358","costCenters":[{"id":114,"text":"Alaska Science 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2011-2021"},{"id":438632,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JEL0UU","text":"USGS data release","linkHelpText":"Flood Frequency Data and 2020 Observed Flood Probability for Selected Streamgages in the Fortymile River Basin, Alaska, 1911-2020"},{"id":318921,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5024/sir20165024_table09.xlsx","text":"Table 9","size":"78 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":318920,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5024/sir20165024_appendixa.xlsx","text":"Appendix A","size":"99 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":318919,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5024/sir20165024_table04.xlsx","text":"Table 4","size":"88 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U.S. Geological Survey
4210 University Drive
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http://alaska.usgs.gov
Long-term fish sampling data from the San Francisco Estuary were combined with detailed three dimensional hydrodynamic modeling to investigate the relationship between historical fish catch and hydrodynamic complexity. Delta Smelt catch data at 45 stations from the Fall Midwater Trawl (FMWT) survey in the vicinity of Suisun Bay were used to develop a quantitative catch-based station index. This index was used to rank stations based on historical Delta Smelt catch. The correlations between historical Delta Smelt catch and 35 quantitative metrics of environmental complexity were evaluated at each station. Eight metrics of environmental conditions were derived from FMWT data and 27 metrics were derived from model predictions at each FMWT station. To relate the station index to conceptual models of Delta Smelt habitat, the metrics were used to predict the station ranking based on the quantified environmental conditions. Salinity, current speed, and turbidity metrics were used to predict the relative ranking of each station for Delta Smelt catch. Including a measure of the current speed at each station improved predictions of the historical ranking for Delta Smelt catch relative to similar predictions made using only salinity and turbidity. Current speed was also found to be a better predictor of historical Delta Smelt catch than water depth. The quantitative approach developed using the FMWT data was validated using the Delta Smelt catch data from the San Francisco Bay Study. Complexity metrics in Suisun Bay were-evaluated during 2010 and 2011. This analysis indicated that a key to historical Delta Smelt catch is the overlap of low salinity, low maximum velocity, and low Secchi depth regions. This overlap occurred in Suisun Bay during 2011, and may have contributed to higher Delta Smelt abundance in 2011 than in 2010 when the favorable ranges of the metrics did not overlap in Suisun Bay.
","language":"English","publisher":"University of California at Davis John Muir Institute of the Environment and the Delta Stewardship Council","doi":"10.15447/sfews.2016v14iss1art3","usgsCitation":"Bever, A.J., MacWilliams, M.L., Herbold, B., Brown, L.R., and Feyrer, F.V., 2016, Linking hydrodynamic complexity to delta smelt (Hypomesus transpacificus) distribution in the San Francisco Estuary, USA: San Francisco Estuary and Watershed Science, v. 14, no. 1, p. 1-27, https://doi.org/10.15447/sfews.2016v14iss1art3.","productDescription":"27 p.","startPage":"1","endPage":"27","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063936","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":471146,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2016v14iss1art3","text":"Publisher Index Page"},{"id":325464,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.6845703125,\n 38.1734326790354\n ],\n [\n -121.6680908203125,\n 38.14103736644331\n ],\n [\n -121.64611816406249,\n 38.089174937729794\n ],\n [\n -121.66259765625001,\n 38.035112420612975\n ],\n [\n -121.67495727539061,\n 37.99724489645483\n ],\n [\n -121.75048828124999,\n 38.00049145082287\n ],\n [\n -121.86447143554686,\n 38.01888587738773\n ],\n [\n -121.96197509765625,\n 38.034030762875844\n ],\n [\n -122.05535888671875,\n 38.037275688165614\n ],\n [\n -122.09793090820311,\n 38.031867399480674\n ],\n [\n -122.14187622070311,\n 38.024295124443995\n ],\n [\n -122.19680786132812,\n 38.034030762875844\n ],\n [\n -122.21054077148438,\n 38.04700960141592\n ],\n [\n -122.22702026367188,\n 38.06647354576796\n ],\n [\n -122.2119140625,\n 38.0729603768343\n ],\n [\n -122.14462280273436,\n 38.052416771864834\n ],\n [\n -122.12265014648438,\n 38.064311140919\n ],\n [\n -122.08694458007812,\n 38.09782123329514\n ],\n [\n -122.07046508789062,\n 38.129155479572624\n ],\n [\n -122.05261230468751,\n 38.23386541556983\n ],\n [\n -122.02926635742188,\n 38.25004423627535\n ],\n [\n -122.00042724609374,\n 38.23170796744926\n ],\n [\n -121.99630737304688,\n 38.182068998322094\n ],\n [\n -121.97708129882812,\n 38.155077102180655\n ],\n [\n -121.87408447265625,\n 38.134556577054134\n ],\n [\n -121.761474609375,\n 38.1334763895322\n ],\n [\n -121.6845703125,\n 38.1734326790354\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-23","publicationStatus":"PW","scienceBaseUri":"5790a183e4b030378fb4743b","contributors":{"authors":[{"text":"Bever, Aaron J.","contributorId":173009,"corporation":false,"usgs":false,"family":"Bever","given":"Aaron","email":"","middleInitial":"J.","affiliations":[{"id":27140,"text":"Delta Modeling Associates, Inc.","active":true,"usgs":false}],"preferred":false,"id":642984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"MacWilliams, Michael L.","contributorId":173010,"corporation":false,"usgs":false,"family":"MacWilliams","given":"Michael","email":"","middleInitial":"L.","affiliations":[{"id":27140,"text":"Delta Modeling Associates, Inc.","active":true,"usgs":false}],"preferred":false,"id":642985,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herbold, Bruce","contributorId":51223,"corporation":false,"usgs":false,"family":"Herbold","given":"Bruce","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":642986,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":642983,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":5901,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":642987,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70169064,"text":"70169064 - 2016 - Overestimation of marsh vulnerability to sea level rise","interactions":[],"lastModifiedDate":"2016-03-16T10:01:19","indexId":"70169064","displayToPublicDate":"2016-03-16T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2841,"text":"Nature Climate Change","onlineIssn":"1758-6798","printIssn":"1758-678X","active":true,"publicationSubtype":{"id":10}},"title":"Overestimation of marsh vulnerability to sea level rise","docAbstract":"Coastal marshes are considered to be among the most valuable and vulnerable ecosystems on Earth, where the imminent loss of ecosystem services is a feared consequence of sea level rise. However, we show with a meta-analysis that global measurements of marsh elevation change indicate that marshes are generally building at rates similar to or exceeding historical sea level rise, and that process-based models predict survival under a wide range of future sea level scenarios. We argue that marsh vulnerability tends to be overstated because assessment methods often fail to consider biophysical feedback processes known to accelerate soil building with sea level rise, and the potential for marshes to migrate inland.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/nclimate2909","usgsCitation":"Kirwan, M., Temmerman, S., Skeehan, E.E., Guntenspergen, G.R., and Fagherazzi, S., 2016, Overestimation of marsh vulnerability to sea level rise: Nature Climate Change, v. 6, p. 253-260, https://doi.org/10.1038/nclimate2909.","productDescription":"8 p.","startPage":"253","endPage":"260","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064983","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471147,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/10067/1324470151162165141","text":"External Repository"},{"id":318898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-24","publicationStatus":"PW","scienceBaseUri":"56ea759be4b0f59b85d8979b","contributors":{"authors":[{"text":"Kirwan, Matthew L. 0000-0002-0658-3038","orcid":"https://orcid.org/0000-0002-0658-3038","contributorId":84060,"corporation":false,"usgs":true,"family":"Kirwan","given":"Matthew L.","affiliations":[],"preferred":false,"id":622803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Temmerman, Stijn","contributorId":71682,"corporation":false,"usgs":true,"family":"Temmerman","given":"Stijn","affiliations":[],"preferred":false,"id":622804,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skeehan, Emily E.","contributorId":167594,"corporation":false,"usgs":false,"family":"Skeehan","given":"Emily","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":622805,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":622749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fagherazzi, Sergio","contributorId":89282,"corporation":false,"usgs":true,"family":"Fagherazzi","given":"Sergio","affiliations":[],"preferred":false,"id":622806,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70169061,"text":"70169061 - 2016 - Threshold responses of Blackside Dace (Chrosomus cumberlandensis) and Kentucky Arrow Darter (Etheostoma spilotum) to stream conductivity","interactions":[],"lastModifiedDate":"2016-03-16T10:08:20","indexId":"70169061","displayToPublicDate":"2016-03-16T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3444,"text":"Southeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Threshold responses of Blackside Dace (Chrosomus cumberlandensis) and Kentucky Arrow Darter (Etheostoma spilotum) to stream conductivity","docAbstract":"Chrosomus cumberlandensis (Blackside Dace [BSD]) and Etheostoma spilotum (Kentucky Arrow Darter [KAD]) are fish species of conservation concern due to their fragmented distributions, their low population sizes, and threats from anthropogenic stressors in the southeastern United States. We evaluated the relationship between fish abundance and stream conductivity, an index of environmental quality and potential physiological stressor. We modeled occurrence and abundance of KAD in the upper Kentucky River basin (208 samples) and BSD in the upper Cumberland River basin (294 samples) for sites sampled between 2003 and 2013. Segmented regression indicated a conductivity change-point for BSD abundance at 343 μS/cm (95% CI: 123–563 μS/cm) and for KAD abundance at 261 μS/cm (95% CI: 151–370 μS/cm). In both cases, abundances were negligible above estimated conductivity change-points. Post-hoc randomizations accounted for variance in estimated change points due to unequal sample sizes across the conductivity gradients. Boosted regression-tree analysis indicated stronger effects of conductivity than other natural and anthropogenic factors known to influence stream fishes. Boosted regression trees further indicated threshold responses of BSD and KAD occurrence to conductivity gradients in support of segmented regression results. We suggest that the observed conductivity relationship may indicate energetic limitations for insectivorous fishes due to changes in benthic macroinvertebrate community composition.
","language":"English","publisher":"Eagle Hill Institute","doi":"10.1656/058.015.0104","usgsCitation":"Hitt, N.P., Floyd, M., Compton, M., and McDonald, K., 2016, Threshold responses of Blackside Dace (Chrosomus cumberlandensis) and Kentucky Arrow Darter (Etheostoma spilotum) to stream conductivity: Southeastern Naturalist, v. 15, no. 1, p. 41-60, https://doi.org/10.1656/058.015.0104.","productDescription":"20 p.","startPage":"41","endPage":"60","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067031","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":318899,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56ea759de4b0f59b85d897a4","contributors":{"authors":[{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568 nhitt@usgs.gov","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":4435,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"nhitt@usgs.gov","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":622729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Floyd, Michael","contributorId":167568,"corporation":false,"usgs":false,"family":"Floyd","given":"Michael","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":622730,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Compton, Michael","contributorId":167569,"corporation":false,"usgs":false,"family":"Compton","given":"Michael","email":"","affiliations":[{"id":24755,"text":"Kentucky State Nature Preserves Commission","active":true,"usgs":false}],"preferred":false,"id":622731,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McDonald, Kenneth","contributorId":167570,"corporation":false,"usgs":false,"family":"McDonald","given":"Kenneth","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":622732,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169078,"text":"70169078 - 2016 - Toxoplasma gondii antibody prevalence and two new genotypes of the parasite in endangered Hawaiian Geese (nene: Branta sandvicensis)","interactions":[],"lastModifiedDate":"2016-05-05T13:05:48","indexId":"70169078","displayToPublicDate":"2016-03-16T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Toxoplasma gondii antibody prevalence and two new genotypes of the parasite in endangered Hawaiian Geese (nene: Branta sandvicensis)","docAbstract":"Toxoplasma gondii is a protozoan parasite transmitted by domestic cats (Felis catus) that has historically caused mortality in native Hawaiian birds. To estimate how widespread exposure to the parasite is in nene (Hawaiian Geese, Branta sandvicensis), we did a serologic survey for T. gondii antibody and genetically characterized parasite DNA from the tissues of dead birds that had confirmed infections by immunohistochemistry. Of 94 geese sampled, prevalence on the island of Kauai, Maui, and Molokai was 21% (n=42), 23% (n=31), and 48% (n=21), respectively. Two new T. gondii genotypes were identified by PCR-restriction fragment length polymorphism from four geese, and these appeared segregated geographically. Exposure to T. gondii in wild nene is widespread and, while the parasite is not a major cause of death, it could have sublethal or behavioral effects. How to translate such information to implement effective ways to manage feral cats in Hawaii poses challenges.
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20705-2350","active":true,"usgs":false}],"preferred":false,"id":622797,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Su, Chunlei","contributorId":167590,"corporation":false,"usgs":false,"family":"Su","given":"Chunlei","email":"","affiliations":[{"id":24765,"text":"University of Tennessee, Department of Microbiology, Knoxville, TN 37996-0845","active":true,"usgs":false}],"preferred":false,"id":622798,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Medeiros, John","contributorId":167591,"corporation":false,"usgs":false,"family":"Medeiros","given":"John","email":"","affiliations":[{"id":24766,"text":"4. State of Hawaii, Division of Forestry and Wildlife-Maui, 54 South High Street # 101, Wailuku, HI 96793.","active":true,"usgs":false}],"preferred":false,"id":622799,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kaiakapu, Thomas","contributorId":167592,"corporation":false,"usgs":false,"family":"Kaiakapu","given":"Thomas","email":"","affiliations":[{"id":24767,"text":"State of Hawaii, Division of Forestry and Wildlife-Kauai, 3060 Eiwa Street # 306, Lihue, HI 96766","active":true,"usgs":false}],"preferred":false,"id":622800,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kwok, Oliver C.","contributorId":167593,"corporation":false,"usgs":false,"family":"Kwok","given":"Oliver","email":"","middleInitial":"C.","affiliations":[{"id":24764,"text":"US Department of Agriculture, Agricultural Research Service, Animal Parasitic Diseases Laboratory, Beltsville, MD, 20705-2350","active":true,"usgs":false}],"preferred":false,"id":622801,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dubey, Jitender P.","contributorId":41707,"corporation":false,"usgs":true,"family":"Dubey","given":"Jitender","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":622802,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70169069,"text":"70169069 - 2016 - Seasonal response of ghrelin, growth hormone, and insulin-like growth factor I in the free-ranging Florida manatee (Trichechus manatus latirostris)","interactions":[],"lastModifiedDate":"2016-07-17T23:41:24","indexId":"70169069","displayToPublicDate":"2016-03-16T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2653,"text":"Mammalian Biology","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal response of ghrelin, growth hormone, and insulin-like growth factor I in the free-ranging Florida manatee (Trichechus manatus latirostris)","docAbstract":"Seasonal changes in light, temperature, and food availability stimulate a physiological response in an animal. Seasonal adaptations are well studied in Arctic, Sub-Arctic, and hibernating mammals; however, limited studies have been conducted in sub-tropical species. The Florida manatee (Trichechus manatus latirostris), a sub-tropical marine mammal, forages less during colder temperatures and may rely on adipose stores for maintenance energy requirements. Metabolic hormones, growth hormone (GH), insulin-like growth factor (IGF)-I, and ghrelin influence growth rate, accretion of lean and adipose tissue. They have been shown to regulate seasonal changes in body composition. The objective of this research was to investigate manatee metabolic hormones in two seasons to determine if manatees exhibit seasonality and if these hormones are associated with seasonal changes in body composition. In addition, age related differences in these metabolic hormones were assessed in multiple age classes. Concentrations of GH, IGF-I, and ghrelin were quantified in adult manatee serum using heterologous radioimmunoassays. Samples were compared between short (winter) and long (summer) photoperiods (n = 22 male, 20 female) and by age class (adult, juvenile, and calf) in long photoperiods (n = 37). Short photoperiods tended to have reduced GH (p = 0.08), greater IGF-I (p = 0.01), and greater blubber depth (p = 0.03) compared with long photoperiods. No differences were observed in ghrelin (p = 0.66). Surprisingly, no age related differences were observed in IGF-I or ghrelin concentrations (p > 0.05). However, serum concentrations of GH tended (p = 0.07) to be greater in calves and juveniles compared with adults. Increased IGF-I, greater blubber thickness, and reduced GH during short photoperiod suggest a prioritization for adipose deposition. Whereas, increased GH, reduced blubber thickness, and decreased IGF-I in long photoperiod suggest prioritization of lean tissue accretion. Hormone profiles in conjunction with difference in body composition between photoperiods indicate seasonal adjustments in manatee nutrient partitioning priorities.
\nThe presence of low-lying stratocumulus clouds and fog has been known to modify biophysical and ecological properties in coastal California where forests are frequently shaded by low-lying clouds or immersed in fog during otherwise warm and dry summer months. Summer fog and stratus can ameliorate summer drought stress and enhance soil water budgets, and often have different spatial and temporal patterns. Here we use remote sensing datasets to characterize the spatial and temporal patterns of cloud cover over California’s northern Channel Islands. We found marine stratus to be persistent from May through September across the years 2001-2012. Stratus clouds were both most frequent and had the greatest spatial extent in July. Clouds typically formed in the evening, and dissipated by the following early afternoon. We present a novel method to downscale satellite imagery using atmospheric observations and discriminate patterns of fog from those of stratus and help explain patterns of fog deposition previously studied on the islands. The outcomes of this study contribute significantly to our ability to quantify the occurrence of coastal fog at biologically meaningful spatial and temporal scales that can improve our understanding of cloud-ecosystem interactions, species distributions and coastal ecohydrology.
","language":"English","publisher":"American Meterorological Society","doi":"10.1175/EI-D-15-0033.1","usgsCitation":"Rastogi, B., Williams, A.P., Fischer, D.T., Iacobellis, S.F., McEachern, K., Carvalho, L., Jones, C.L., Baguskas, S.A., and Still, C.J., 2016, Spatial and temporal patterns of cloud cover and fog inundation in coastal California: Ecological implications: Earth Interactions, v. 20, Paper 15; 19 p., https://doi.org/10.1175/EI-D-15-0033.1.","productDescription":"Paper 15; 19 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073097","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471148,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/ei-d-15-0033.1","text":"Publisher Index Page"},{"id":318897,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.45959472656249,\n 33.880677127838844\n ],\n [\n -120.45959472656249,\n 34.08564930273551\n ],\n [\n -119.32662963867188,\n 34.08564930273551\n ],\n [\n -119.32662963867188,\n 33.880677127838844\n ],\n [\n -120.45959472656249,\n 33.880677127838844\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-24","publicationStatus":"PW","scienceBaseUri":"56ea759ce4b0f59b85d8979f","contributors":{"authors":[{"text":"Rastogi, Bharat","contributorId":167577,"corporation":false,"usgs":false,"family":"Rastogi","given":"Bharat","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":622755,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, A. Park","contributorId":88456,"corporation":false,"usgs":true,"family":"Williams","given":"A.","email":"","middleInitial":"Park","affiliations":[],"preferred":false,"id":622756,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fischer, Douglas T.","contributorId":167578,"corporation":false,"usgs":false,"family":"Fischer","given":"Douglas","email":"","middleInitial":"T.","affiliations":[{"id":24759,"text":"Strategic Environmental Consulting; University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":622758,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iacobellis, Sam F.","contributorId":11502,"corporation":false,"usgs":true,"family":"Iacobellis","given":"Sam","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":622757,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McEachern, Kathryn 0000-0003-2631-8247 kathryn_mceachern@usgs.gov","orcid":"https://orcid.org/0000-0003-2631-8247","contributorId":146324,"corporation":false,"usgs":true,"family":"McEachern","given":"Kathryn","email":"kathryn_mceachern@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":622754,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carvalho, Leila","contributorId":167579,"corporation":false,"usgs":false,"family":"Carvalho","given":"Leila","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":622759,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Charles Leslie","contributorId":27790,"corporation":false,"usgs":true,"family":"Jones","given":"Charles","email":"","middleInitial":"Leslie","affiliations":[],"preferred":false,"id":622760,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Baguskas, Sara A.","contributorId":167580,"corporation":false,"usgs":false,"family":"Baguskas","given":"Sara","email":"","middleInitial":"A.","affiliations":[{"id":24760,"text":"University of California, Santa Barbara and Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":622761,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Still, Christopher J.","contributorId":167581,"corporation":false,"usgs":false,"family":"Still","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":24761,"text":"University of California, Santa Barbara; Oregon State University","active":true,"usgs":false}],"preferred":false,"id":622762,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70171335,"text":"70171335 - 2016 - Abundance, distribution, and removals of feral pigs at Big Island National Wildlife Refuge Complex 2010–2015","interactions":[],"lastModifiedDate":"2018-01-04T12:39:30","indexId":"70171335","displayToPublicDate":"2016-03-16T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":414,"text":"Technical Report","active":false,"publicationSubtype":{"id":9}},"seriesNumber":"HCSU-075","title":"Abundance, distribution, and removals of feral pigs at Big Island National Wildlife Refuge Complex 2010–2015","docAbstract":"The Hakalau Forest Unit (HFU) of Big Island National Wildlife Refuge Complex (BINWRC) has intensively monitored non-native ungulate presence and distribution during surveys of all managed areas since 1988. In this report we: 1) provide results from recent ungulate surveys and the number of removals at HFU to determine the distribution, abundance, and efficacy of removals of feral pigs, the dominant ungulate, from 2010 to 2015; 2) present results of surveys of the presence and distribution of several ungulate species at the Kona Forest Unit (KFU) of BINWRC from November of 2012 to April of 2015; 3) present results of surveys of weed presence and cover at both refuge units; and 4) present comparative analyses of forest canopy cover at KFU from visual estimates and geospatial imagery. Removals of feral pigs at HFU appear to have significantly decreased pig abundance over the study period from 2010–2015. A grand total of 1,660 feral pigs were removed from managed areas of HFU from 2010 until September of 2015. Management units 2 and 4 contained the majority of pigs at HFU. Recent surveys recorded high densities of pigs in the unenclosed, unmanaged area of Lower Maulua, reaching 14.9 ± (3.2) pigs/km2 in March of 2015. The total amount of ungulate sign ranged from 22.2 to 54.3 percent of plots surveyed at KFU from November of 2012 to April of 2015. The ability to differentiate sign of ungulate species remains problematic at KFU; although there appears to have been a significant decline in feral cattle sign at KFU, this result is likely to be unreliable because cattle and pig sign were not differentiated consistently during later surveys. Spatial distributions in weed cover are distinctive; however, some weed species may not be reliably represented due to observers’ inconsistencies in recording data and abilities to recognize less common weeds.
","language":"English","publisher":"University of Hawaii at Hilo","publisherLocation":"Hilo, HI","collaboration":"This product was prepared under Cooperative Agreement G13AC00097 for the Pacific Island Ecosystems Research Center of the U.S. Geological Survey.","usgsCitation":"Leopold, C.R., Hess, S.C., Kendall, S.J., and Judge, S.W., 2016, Abundance, distribution, and removals of feral pigs at Big Island National Wildlife Refuge Complex 2010–2015: Technical Report HCSU-075, iii, 44 p.","productDescription":"iii, 44 p.","startPage":"1","endPage":"44","numberOfPages":"49","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073412","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":326265,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":328012,"type":{"id":15,"text":"Index Page"},"url":"https://dspace.lib.hawaii.edu/handle/10790/2684"}],"country":"United States","state":"Hawaii","otherGeospatial":"Hakalau Forest Unit (HFU) of Big Island National Wildlife Refuge Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.36041259765625,\n 19.751194318940865\n ],\n [\n -155.36041259765625,\n 19.89911667339186\n ],\n [\n -155.21141052246094,\n 19.89911667339186\n ],\n [\n -155.21141052246094,\n 19.751194318940865\n ],\n [\n -155.36041259765625,\n 19.751194318940865\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a9ad2de4b05e859bdfb7c3","contributors":{"authors":[{"text":"Leopold, Christina R.","contributorId":46817,"corporation":false,"usgs":true,"family":"Leopold","given":"Christina","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":630613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hess, Steve C. 0000-0001-6403-9922 shess@usgs.gov","orcid":"https://orcid.org/0000-0001-6403-9922","contributorId":150366,"corporation":false,"usgs":true,"family":"Hess","given":"Steve","email":"shess@usgs.gov","middleInitial":"C.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":630612,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kendall, Steve J. 0000-0002-9290-5629","orcid":"https://orcid.org/0000-0002-9290-5629","contributorId":169663,"corporation":false,"usgs":false,"family":"Kendall","given":"Steve","email":"","middleInitial":"J.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":630614,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Judge, Seth W.","contributorId":8718,"corporation":false,"usgs":true,"family":"Judge","given":"Seth","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":630615,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70174957,"text":"70174957 - 2016 - The Pliocene Model Intercomparison Project (PlioMIP) Phase 2: Scientific objectives and experimental design","interactions":[],"lastModifiedDate":"2016-07-22T16:03:58","indexId":"70174957","displayToPublicDate":"2016-03-16T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1250,"text":"Climate of the Past","active":true,"publicationSubtype":{"id":10}},"title":"The Pliocene Model Intercomparison Project (PlioMIP) Phase 2: Scientific objectives and experimental design","docAbstract":"The Pliocene Model Intercomparison Project (PlioMIP) is a co-ordinated international climate modelling initiative to study and understand climate and environments of the Late Pliocene, as well as their potential relevance in the context of future climate change. PlioMIP examines the consistency of model predictions in simulating Pliocene climate and their ability to reproduce climate signals preserved by geological climate archives. Here we provide a description of the aim and objectives of the next phase of the model intercomparison project (PlioMIP Phase 2), and we present the experimental design and boundary conditions that will be utilized for climate model experiments in Phase 2.
\nFollowing on from PlioMIP Phase 1, Phase 2 will continue to be a mechanism for sampling structural uncertainty within climate models. However, Phase 1 demonstrated the requirement to better understand boundary condition uncertainties as well as uncertainty in the methodologies used for data–model comparison. Therefore, our strategy for Phase 2 is to utilize state-of-the-art boundary conditions that have emerged over the last 5 years. These include a new palaeogeographic reconstruction, detailing ocean bathymetry and land–ice surface topography. The ice surface topography is built upon the lessons learned from offline ice sheet modelling studies. Land surface cover has been enhanced by recent additions of Pliocene soils and lakes. Atmospheric reconstructions of palaeo-CO2 are emerging on orbital timescales, and these are also incorporated into PlioMIP Phase 2. New records of surface and sea surface temperature change are being produced that will be more temporally consistent with the boundary conditions and forcings used within models.
\nFinally we have designed a suite of prioritized experiments that tackle issues surrounding the basic understanding of the Pliocene and its relevance in the context of future climate change in a discrete way.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/cp-12-663-2016","usgsCitation":"Haywood, A.M., Dowsett, H.J., Dolan, A.M., Rowley, D., Abe-Ouchi, A., Otto-Bliesner, B., Chandler, M.A., Hunter, S.J., Lunt, D.J., Pound, M., and Salzmann, U., 2016, The Pliocene Model Intercomparison Project (PlioMIP) Phase 2: Scientific objectives and experimental design: Climate of the Past, v. 12, no. 3, p. 663-675, https://doi.org/10.5194/cp-12-663-2016.","productDescription":"13 p.","startPage":"663","endPage":"675","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060058","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":471149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/cp-12-663-2016","text":"Publisher Index Page"},{"id":325565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-16","publicationStatus":"PW","scienceBaseUri":"5793444de4b0eb1ce79e8c1d","contributors":{"authors":[{"text":"Haywood, Alan M.","contributorId":86663,"corporation":false,"usgs":true,"family":"Haywood","given":"Alan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":643340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dowsett, Harry J. 0000-0003-1983-7524 hdowsett@usgs.gov","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":949,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"hdowsett@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":643339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dolan, Aisling M.","contributorId":30117,"corporation":false,"usgs":true,"family":"Dolan","given":"Aisling","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":643341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rowley, David","contributorId":173099,"corporation":false,"usgs":false,"family":"Rowley","given":"David","email":"","affiliations":[{"id":12621,"text":"University of Chicago and University of South Florida","active":true,"usgs":false}],"preferred":false,"id":643342,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Abe-Ouchi, Ayako","contributorId":94942,"corporation":false,"usgs":true,"family":"Abe-Ouchi","given":"Ayako","email":"","affiliations":[],"preferred":false,"id":643343,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Otto-Bliesner, Bette","contributorId":58171,"corporation":false,"usgs":true,"family":"Otto-Bliesner","given":"Bette","affiliations":[],"preferred":false,"id":643344,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chandler, Mark A.","contributorId":101768,"corporation":false,"usgs":true,"family":"Chandler","given":"Mark","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":643345,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hunter, Stephen J.","contributorId":55711,"corporation":false,"usgs":true,"family":"Hunter","given":"Stephen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":643346,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lunt, Daniel J.","contributorId":101168,"corporation":false,"usgs":true,"family":"Lunt","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":643347,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pound, Matthew","contributorId":173100,"corporation":false,"usgs":false,"family":"Pound","given":"Matthew","email":"","affiliations":[{"id":18103,"text":"Northumbria University, Newcastle Upon Tyne, UK","active":true,"usgs":false}],"preferred":false,"id":643357,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Salzmann, Ulrich","contributorId":173101,"corporation":false,"usgs":false,"family":"Salzmann","given":"Ulrich","email":"","affiliations":[{"id":18103,"text":"Northumbria University, Newcastle Upon Tyne, UK","active":true,"usgs":false}],"preferred":false,"id":643358,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70170130,"text":"70170130 - 2016 - Estimating abundance in the presence of species uncertainty","interactions":[],"lastModifiedDate":"2016-09-19T15:52:34","indexId":"70170130","displayToPublicDate":"2016-03-16T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Estimating abundance in the presence of species uncertainty","docAbstract":"1.N-mixture models have become a popular method for estimating abundance of free-ranging animals that are not marked or identified individually. These models have been used on count data for single species that can be identified with certainty. However, co-occurring species often look similar during one or more life stages, making it difficult to assign species for all recorded captures. This uncertainty creates problems for estimating species-specific abundance and it can often limit life stages to which we can make inference.
\n2.We present a new extension of N-mixture models that accounts for species uncertainty. In addition to estimating site-specific abundances and detection probabilities, this model allows estimating probability of correct assignment of species identity. We implement this hierarchical model in a Bayesian framework and provide all code for running the model in BUGS-language programs.
\n3.We present an application of the model on count data from two sympatric freshwater fishes, the brook stickleback (Culaea inconstans) and the ninespine stickleback (Pungitius pungitius), ad illustrate implementation of covariate effects (habitat characteristics). In addition, we used a simulation study to validate the model and illustrate potential sample size issues. We also compared, for both real and simulated data, estimates provided by our model to those obtained by a simple N-mixture model when captures of unknown species identification were discarded. In the latter case, abundance estimates appeared highly biased and very imprecise, while our new model provided unbiased estimates with higher precision.
\n4.This extension of the N-mixture model should be useful for a wide variety of studies and taxa, as species uncertainty is a common issue. It should notably help improve investigation of abundance and vital rate characteristics of organisms’ early life stages, which are sometimes more difficult to identify than adults.
\nWest Nile virus (WNV) spread to the US western plains states in 2003, when a significant mortality event attributed to WNV occurred in Greater Sage-grouse ( Centrocercus urophasianus ). The role of avian species inhabiting sagebrush in the amplification of WNV in arid and semiarid regions of the North America is unknown. We conducted an experimental WNV challenge study in Vesper Sparrows ( Pooecetes gramineus ), a species common to sagebrush and grassland habitats found throughout much of North America. We found Vesper Sparrows to be moderately susceptible to WNV, developing viremia considered sufficient to transmit WNV to feeding mosquitoes, but the majority of birds were capable of surviving infection and developing a humoral immune response to the WNV nonstructural 1 and envelope proteins. Despite clearance of viremia, after 6 mo, WNV was detected molecularly in three birds and cultured from one bird. Surviving Vesper Sparrows were resistant to reinfection 6 mo after the initial challenge. Vesper sparrows could play a role in the amplification of WNV in sagebrush habitat and other areas of their range, but rapid clearance of WNV may limit their importance as competent amplification hosts of WNV.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Wildlife Diseases","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wildlife Disease Association","publisherLocation":"Lawrence, KS","doi":"10.7589/2015-06-148","usgsCitation":"Hofmeister, E.K., Dusek, R.J., Fassbinder-Orth, C., Owen, B., and Franson, J., 2016, Susceptibility and antibody response of Vesper Sparrows (Pooecetes gramineus) to West Nile virus: A potential amplification host in sagebrush-grassland habitat: Journal of Wildlife Diseases, v. 52, no. 2, 9 p., https://doi.org/10.7589/2015-06-148.","productDescription":"9 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068995","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":318951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.075,\n 41.475\n ],\n [\n -118.075,\n 41.465\n ],\n [\n -118.065,\n 41.465\n ],\n [\n -118.065,\n 41.475\n ],\n [\n -118.075,\n 41.475\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56ed26b6e4b0f59b85db0a33","contributors":{"authors":[{"text":"Hofmeister, Erik K. 0000-0002-6360-3912 ehofmeister@usgs.gov","orcid":"https://orcid.org/0000-0002-6360-3912","contributorId":3230,"corporation":false,"usgs":true,"family":"Hofmeister","given":"Erik","email":"ehofmeister@usgs.gov","middleInitial":"K.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":622930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dusek, Robert J. 0000-0001-6177-7479 rdusek@usgs.gov","orcid":"https://orcid.org/0000-0001-6177-7479","contributorId":152316,"corporation":false,"usgs":true,"family":"Dusek","given":"Robert","email":"rdusek@usgs.gov","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":622931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fassbinder-Orth, Carol","contributorId":167623,"corporation":false,"usgs":false,"family":"Fassbinder-Orth","given":"Carol","email":"","affiliations":[{"id":24786,"text":"Creighton University, Omaha, NE, 68178, USA","active":true,"usgs":false}],"preferred":false,"id":622932,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Owen, Benjamin","contributorId":167624,"corporation":false,"usgs":false,"family":"Owen","given":"Benjamin","email":"","affiliations":[{"id":24786,"text":"Creighton University, Omaha, NE, 68178, USA","active":true,"usgs":false}],"preferred":false,"id":622933,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Franson, J. Christian jfranson@usgs.gov","contributorId":149318,"corporation":false,"usgs":true,"family":"Franson","given":"J. Christian","email":"jfranson@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":false,"id":622934,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70162692,"text":"ofr20161013 - 2016 - Ecological requirements for pallid sturgeon reproduction and recruitment in the Missouri River—Annual report 2014","interactions":[],"lastModifiedDate":"2016-03-17T09:14:13","indexId":"ofr20161013","displayToPublicDate":"2016-03-16T00:00:00","publicationYear":"2016","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":"2016-1013","title":"Ecological requirements for pallid sturgeon reproduction and recruitment in the Missouri River—Annual report 2014","docAbstract":"The Comprehensive Sturgeon Research Project is a multiyear, multiagency collaborative research framework developed to provide information to support pallid sturgeon recovery and Missouri River management decisions. The project strategy integrates field and laboratory studies of sturgeon reproductive ecology, early life history, habitat requirements, and physiology. The project scope of work is developed annually with collaborating research partners and in cooperation with the U.S. Army Corps of Engineers, Missouri River Recovery Program–Integrated Science Program. The project research consists of several interdependent and complementary tasks that involve multiple disciplines.
The project research tasks in the 2014 scope of work emphasized understanding of reproductive migrations and spawning of adult pallid sturgeon and hatch and drift of larvae. These tasks were addressed in three hydrologically and geomorphologically distinct parts of the Missouri River Basin: the Lower Missouri River downstream from Gavins Point Dam, the Upper Missouri River downstream from Fort Peck Dam and downstream reaches of the Milk River, and the Lower Yellowstone River. The project research is designed to inform management decisions related to channel re-engineering, flow modification, and pallid sturgeon population augmentation on the Missouri River and throughout the range of the species. Research and progress made through this project are reported to the U.S. Army Corps of Engineers annually. This annual report details the research effort and progress made by the Comprehensive Sturgeon Research Project during 2014.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161013","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Missouri River Recovery—Integrated Science Program","usgsCitation":"DeLonay, A.J., Chojnacki, K.A., Jacobson, R.B., Braaten, P.J., Buhl, K.J., Elliott, C.M., Erwin, S.O., Faulkner, J.D.A., Candrl, J.S., Fuller, D.B., Backes, K.M., Haddix, T.M., Rugg, M.L., Wesolek, C.J., Eder, B.L., and Mestl, G.E., 2016,\nEcological requirements for pallid sturgeon reproduction and recruitment in the Missouri River—Annual report 2014: U.S. Geological Survey Open-File Report 2016–1013, 131 p., https://dx.doi.org/10.3133/ofr20161013.","productDescription":"xv, 131 p.","numberOfPages":"152","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-065464","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":318883,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1013/coverthb.jpg"},{"id":318884,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1013/ofr20161013.pdf","text":"Report","size":"24.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1013"}],"country":"United States","state":"Missouri, Montana, Nebraska","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.57812499999999,\n 47.956823800497475\n ],\n [\n -107.57812499999999,\n 48.629278127969414\n ],\n [\n -104.029541015625,\n 48.629278127969414\n ],\n [\n -104.029541015625,\n 47.956823800497475\n ],\n [\n -107.57812499999999,\n 47.956823800497475\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.84423828125,\n 41.054501963290505\n ],\n [\n -97.84423828125,\n 42.956422511073335\n ],\n [\n -95.78979492187499,\n 42.956422511073335\n ],\n [\n -95.78979492187499,\n 41.054501963290505\n ],\n [\n -97.84423828125,\n 41.054501963290505\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.08441162109374,\n 38.52668162061619\n ],\n [\n -93.08441162109374,\n 39.3024245604149\n ],\n [\n -91.67816162109375,\n 39.3024245604149\n ],\n [\n -91.67816162109375,\n 38.52668162061619\n ],\n [\n -93.08441162109374,\n 38.52668162061619\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
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4200 New Haven Road
Columbia, MO 65201
To address concerns about the effects of water-resource management practices and rising sea level on saltwater intrusion, the U.S. Geological Survey in cooperation with the Broward County Environmental Planning and Community Resilience Division, initiated a study to examine causes of saltwater intrusion and predict the effects of future alterations to the hydrologic system on salinity distribution in eastern Broward County, Florida. A three-dimensional, variable-density solute-transport model was calibrated to conditions from 1970 to 2012, the period for which data are most complete and reliable, and was used to simulate historical conditions from 1950 to 2012. These types of models are typically difficult to calibrate by matching to observed groundwater salinities because of spatial variability in aquifer properties that are unknown, and natural and anthropogenic processes that are complex and unknown; therefore, the primary goal was to reproduce major trends and locally generalized distributions of salinity in the Biscayne aquifer. The methods used in this study are relatively new, and results will provide transferable techniques for protecting groundwater resources and maximizing groundwater availability in coastal areas. The model was used to (1) evaluate the sensitivity of the salinity distribution in groundwater to sea-level rise and groundwater pumping, and (2) simulate the potential effects of increases in pumping, variable rates of sea-level rise, movement of a salinity control structure, and use of drainage recharge wells on the future distribution of salinity in the aquifer.
\nResults from the simulation of historical conditions indicate that the model generally represents the observed greater westward extent of elevated salinity in the central part of the intruded area relative to the northern and southernmost parts of the intruded area. Results of sensitivity testing indicate that the extent of elevated salinity is most sensitive to pumping in areas where the source of saltwater is largely offshore, from the Atlantic Ocean, and is most sensitive to sea-level rise in areas where the source of salinity is downward leakage of brackish water from canals.
\nSimulations of future scenarios indicate that increases in pumping near the existing interface may cause the interface to advance and decreases in pumping may cause it to retreat. Climatic effects, such as periods of prolonged drought or high precipitation, may augment or counteract long-term effects of changes in pumping on aquifer salinity at well fields. With increasing rates of sea-level rise, the freshwater-saltwater interface advances progressively inland, and flow-averaged salinities at well fields near the existing interface increase commensurately. Hypothetical southeastward (downstream) re-positioning of the existing G–54 salinity-control structure may prevent the interface from moving northwestward along and near the North New River canal, but beneficial effects are localized. Implementation of freshwater recharge wells in the city of Hallandale Beach may also have only a localized freshening effect in the aquifer and little appreciable effect on the freshwater-saltwater interface or on concentrations of salinity at well fields.
\nModel accuracy and use are limited by uncertainty in the physical properties and boundary conditions of the system, uncertainty in historical and future conditions, and generalizations made in the mathematical relationships used to describe the physical processes of groundwater flow and transport. Because of these limitations, model results should be considered in relative rather than absolute terms. Nonetheless, model results do provide useful information on the relative scale of response of the system to changes in pumping distribution, sea-level rise, and mitigation activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165022","collaboration":"Prepared in cooperation with the Broward County Environmental Planning and Community Resilience Division","usgsCitation":"Hughes, J.D., Sifuentes, D.F., and White, J.T., 2016, Potential effects of alterations to the hydrologic system on the distribution of salinity in the Biscayne aquifer in Broward County, Florida: U.S. Geological Survey Scientific Investigations Report 2016–5022, 114 p., https://dx.doi.org/10.3133/sir20165022.","productDescription":"Report: x, 114 p.; Data Release","numberOfPages":"128","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-056536","costCenters":[{"id":269,"text":"FLWSC-Ft. 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Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
http://fl.water.usgs.gov/