Roads are significant barriers to landscape-scale movements of individuals or populations of many wildlife taxa. The decision by an animal near a road to either cross or not cross may be influenced by characteristics of the road, environmental conditions, traits of the individual animal, and other aspects of the context within which the decision is made. We considered such factors in a mixed-effects logistic regression model describing the nightly road crossing probabilities of invasive nocturnal Brown Treesnakes (Boiga irregularis) through short-term radiotracking of 691 snakes within close proximity to 50 road segments across the island of Guam. All measures of road magnitude (traffic volume, gap width, surface type, etc.) were significantly negatively correlated with crossing probabilities. Snake body size was the only intrinsic factor associated with crossing rates, with larger snakes crossing roads more frequently. Humidity was the only environmental variable affecting crossing rate. The distance of the snake from the road at the start of nightly movement trials was the most significant predictor of crossings. The presence of snake traps with live mouse lures during a portion of the trials indicated that localized prey cues reduced the probability of a snake crossing the road away from the traps, suggesting that a snake's decision to cross roads is influenced by local foraging opportunities. Per capita road crossing rates of Brown Treesnakes were very low, and comparisons to historical records suggest that crossing rates have declined in the 60+ yr since introduction to Guam. We report a simplified model that will allow managers to predict road crossing rates based on snake, road, and contextual characteristics. Road crossing simulations based on actual snake size distributions demonstrate that populations with size distributions skewed toward larger snakes will result in a higher number of road crossings. Our method of modeling per capita road crossing probabilities as a binary response variable, influenced by contextual factors, may be useful for describing or predicting road crossings by individuals of other taxa provided that appropriate spatial and temporal resolution can be achieved and that potentially influential covariate data can be obtained.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1292","usgsCitation":"Siers, S.R., Reed, R., and Savidge, J.A., 2016, To cross or not to cross: modeling wildlife road crossings as a binary response variable with contextual predictors: Ecosphere, v. 7, no. 5, e01292; 19 p., https://doi.org/10.1002/ecs2.1292.","productDescription":"e01292; 19 p.","ipdsId":"IP-069215","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471035,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1292","text":"Publisher Index Page"},{"id":329465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-31","publicationStatus":"PW","scienceBaseUri":"57fe679ee4b0824b2d143713","contributors":{"authors":[{"text":"Siers, Shane R.","contributorId":152305,"corporation":false,"usgs":false,"family":"Siers","given":"Shane","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":650395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Robert N. reedr@usgs.gov","contributorId":1686,"corporation":false,"usgs":true,"family":"Reed","given":"Robert N.","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":650394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Savidge, Julie A.","contributorId":175196,"corporation":false,"usgs":false,"family":"Savidge","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":650396,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176210,"text":"70176210 - 2016 - Organic petrology and geochemistry of Eocene Suzak bituminous marl, north-central Afghanistan: Depositional environment and source rock potential","interactions":[],"lastModifiedDate":"2016-09-01T16:21:44","indexId":"70176210","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2682,"text":"Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Organic petrology and geochemistry of Eocene Suzak bituminous marl, north-central Afghanistan: Depositional environment and source rock potential","docAbstract":"Organic geochemistry and petrology of Eocene Suzak bituminous marl outcrop samples from Madr village in north-central Afghanistan were characterized via an integrated analytical approach to evaluate depositional environment and source rock potential. Multiple proxies suggest the organic-rich (TOC ∼6 wt.%) bituminous marls are ‘immature’ for oil generation (e.g., vitrinite Ro < 0.4%, Tmax < 425 °C, PI ≤ 0.05, C29 ααα S/S + R ≤ 0.12, C29 ββS/ββS+ααR ≤ 0.10, others), yet oil seeps are present at outcrop and live oil and abundant solid bitumen were observed via optical microscopy. Whole rock sulfur content is ∼2.3 wt.% whereas sulfur content is ∼5.0–5.6 wt.% in whole rock extracts with high polar components, consistent with extraction from S-rich Type IIs organic matter which could generate hydrocarbons at low thermal maturity. Low Fe-sulfide mineral abundance and comparison of Pr/Ph ratios between saturate and whole extracts suggest limited Fe concentration resulted in sulfurization of organic matter during early diagenesis. From these observations, we infer that a Type IIs kerogen in ‘immature’ bituminous marl at Madr could be generating high sulfur viscous oil which is seeping from outcrop. However, oil-seep samples were not collected for correlation studies. Aluminum-normalized trace element concentrations indicate enrichment of redox sensitive trace elements Mo, U and V and suggest anoxic-euxinic conditions during sediment deposition. The bulk of organic matter observed via optical microscopy is strongly fluorescent amorphous bituminite grading to lamalginite, possibly representing microbial mat facies. Short chain n-alkanes peak at C14–C16 (n-C17/n-C29 > 1) indicating organic input from marine algae and/or bacterial biomass, and sterane/hopane ratios are low (0.12–0.14). Monoaromatic steroids are dominated by C28clearly indicating a marine setting. High gammacerane index values (∼0.9) are consistent with anoxia stratification and may indicate intermittent saline-hypersaline conditions. Stable C isotope ratios also suggest a marine depositional scenario for the Suzak samples, consistent with the presence of marine foraminifera including abundant planktic globigerinida(?) and rare benthic discocyclina(?) and nummulites(?). Biomarker 2α-methylhopane for photosynthetic cyanobacteria implies shallow photic zone deposition of Madr marls and 3β-methylhopane indicates presence of methanotrophic archaea in the microbial consortium. The data presented herein are consistent with deposition of Suzak bituminous marls in shallow stratified waters of a restricted marine basin associated with the southeastern incipient or proto-Paratethys. Geochemical proxies from Suzak rock extracts (S content, high polar content, C isotopes, normal (αααR) C27–29 steranes, and C29/C30 and C26/C25 hopane ratios) are similar to extant data from Paleogene oils produced to the north in the Afghan-Tajik Basin. This observation may indicate laterally equivalent strata are effective source rocks as suggested by previous workers; however, further work is needed to strengthen oil-source correlations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2016.02.029","usgsCitation":"Hackley, P.C., and Sanfilipo, J., 2016, Organic petrology and geochemistry of Eocene Suzak bituminous marl, north-central Afghanistan: Depositional environment and source rock potential: Marine and Petroleum Geology, v. 73, p. 572-589, https://doi.org/10.1016/j.marpetgeo.2016.02.029.","productDescription":"18 p.","startPage":"572","endPage":"589","ipdsId":"IP-069387","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":328205,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"73","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57c95130e4b0f2f0cec15bfc","contributors":{"authors":[{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":647807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanfilipo, John 0000-0002-8739-5628 jsan@usgs.gov","orcid":"https://orcid.org/0000-0002-8739-5628","contributorId":140236,"corporation":false,"usgs":true,"family":"Sanfilipo","given":"John","email":"jsan@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":647808,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70161743,"text":"70161743 - 2016 - Summary of the GK15 ground‐motion prediction equation for horizontal PGA and 5% damped PSA from shallow crustal continental earthquakes","interactions":[],"lastModifiedDate":"2016-06-28T16:09:00","indexId":"70161743","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Summary of the GK15 ground‐motion prediction equation for horizontal PGA and 5% damped PSA from shallow crustal continental earthquakes","docAbstract":"We present a revised ground‐motion prediction equation (GMPE) for computing medians and standard deviations of peak ground acceleration (PGA) and 5% damped pseudospectral acceleration (PSA) response ordinates of the horizontal component of randomly oriented ground motions to be used for seismic‐hazard analyses and engineering applications. This GMPE is derived from the expanded Next Generation Attenuation (NGA)‐West 1 database (see Data and Resources; Chiou et al., 2008). The revised model includes an anelastic attenuation term as a function of quality factor (Q0) to capture regional differences in far‐source (beyond 150 km) attenuation, and a new frequency‐dependent sedimentary‐basin scaling term as a function of depth to the 1.5 km/s shear‐wave velocity isosurface to improve ground‐motion predictions at sites located on deep sedimentary basins. The new Graizer–Kalkan 2015 (GK15) model, developed to be simple, is applicable for the western United States and other similar shallow crustal continental regions in active tectonic environments for earthquakes with moment magnitudes (M) 5.0–8.0, distances 0–250 km, average shear‐wave velocities in the upper 30 m (VS30) 200–1300 m/s, and spectral periods (T) 0.01–5 s. Our aleatory variability model captures interevent (between‐event) variability, which decreases with magnitude and increases with distance. The mixed‐effect residuals analysis reveals that the GK15 has no trend with respect to the independent predictor parameters. Compared to our 2007–2009 GMPE, the PGA values are very similar, whereas spectral ordinates predicted are larger at T<0.2 s and they are smaller at longer periods.
","language":"English","publisher":"Seismological Society of America","publisherLocation":"El Cerito, CA","doi":"10.1785/0120150194","usgsCitation":"Graizer, V., and Kalkan, E., 2016, Summary of the GK15 ground‐motion prediction equation for horizontal PGA and 5% damped PSA from shallow crustal continental earthquakes: Bulletin of the Seismological Society of America, v. 106, no. 2, p. 687-707, https://doi.org/10.1785/0120150194.","productDescription":"21 p.","startPage":"687","endPage":"707","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065326","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":324563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Armenia, Georgia, Italy, Taiwan, Turkey, United States, Uzbekistan","state":"Alaska, California, Nevada","volume":"106","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-06","publicationStatus":"PW","scienceBaseUri":"57739fb7e4b07657d1a90d78","contributors":{"authors":[{"text":"Graizer, Vladimir;","contributorId":152040,"corporation":false,"usgs":false,"family":"Graizer","given":"Vladimir;","affiliations":[{"id":12536,"text":"U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":587625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kalkan, Erol 0000-0002-9138-9407 ekalkan@usgs.gov","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":1218,"corporation":false,"usgs":true,"family":"Kalkan","given":"Erol","email":"ekalkan@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":587624,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70185560,"text":"70185560 - 2016 - NGA-West2 equations for predicting vertical-component PGA, PGV, and 5%-damped PSA from shallow crustal earthquakes","interactions":[],"lastModifiedDate":"2017-03-24T10:35:32","indexId":"70185560","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"NGA-West2 equations for predicting vertical-component PGA, PGV, and 5%-damped PSA from shallow crustal earthquakes","docAbstract":"We present ground motion prediction equations (GMPEs) for computing natural log means and standard deviations of vertical-component intensity measures (IMs) for shallow crustal earthquakes in active tectonic regions. The equations were derived from a global database with M 3.0–7.9 events. The functions are similar to those for our horizontal GMPEs. We derive equations for the primary M- and distance-dependence of peak acceleration, peak velocity, and 5%-damped pseudo-spectral accelerations at oscillator periods between 0.01–10 s. We observe pronounced M-dependent geometric spreading and region-dependent anelastic attenuation for high-frequency IMs. We do not observe significant region-dependence in site amplification. Aleatory uncertainty is found to decrease with increasing magnitude; within-event variability is independent of distance. Compared to our horizontal-component GMPEs, attenuation rates are broadly comparable (somewhat slower geometric spreading, faster apparent anelastic attenuation), VS30-scaling is reduced, nonlinear site response is much weaker, within-event variability is comparable, and between-event variability is greater.
","language":"English","publisher":"Earthquake Engineering Research Institute","doi":"10.1193/072114EQS116M","usgsCitation":"Stewart, J.P., Boore, D.M., Seyhan, E., and Atkinson, G.M., 2016, NGA-West2 equations for predicting vertical-component PGA, PGV, and 5%-damped PSA from shallow crustal earthquakes: Earthquake Spectra, v. 32, no. 2, p. 1005-1031, https://doi.org/10.1193/072114EQS116M.","productDescription":"27 p.","startPage":"1005","endPage":"1031","ipdsId":"IP-061692","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":338265,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-01","publicationStatus":"PW","scienceBaseUri":"58d63038e4b05ec7991310eb","contributors":{"authors":[{"text":"Stewart, Jonathan P.","contributorId":100110,"corporation":false,"usgs":false,"family":"Stewart","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":685959,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boore, David M. boore@usgs.gov","contributorId":2509,"corporation":false,"usgs":true,"family":"Boore","given":"David","email":"boore@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":685958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seyhan, Emel","contributorId":51193,"corporation":false,"usgs":false,"family":"Seyhan","given":"Emel","email":"","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":685960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Atkinson, Gail M.","contributorId":60515,"corporation":false,"usgs":false,"family":"Atkinson","given":"Gail","email":"","middleInitial":"M.","affiliations":[{"id":13255,"text":"University of Western Ontario","active":true,"usgs":false}],"preferred":false,"id":685961,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191143,"text":"70191143 - 2016 - Economic impacts of a California tsunami","interactions":[],"lastModifiedDate":"2017-09-27T16:56:47","indexId":"70191143","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2823,"text":"Natural Hazards Review","active":true,"publicationSubtype":{"id":10}},"title":"Economic impacts of a California tsunami","docAbstract":"The economic consequences of a tsunami scenario for Southern California are estimated using computable general equilibrium analysis. The economy is modeled as a set of interconnected supply chains interacting through markets but with explicit constraints stemming from property damage and business downtime. Economic impacts are measured by the reduction of Gross Domestic Product for Southern California, Rest of California, and U.S. economies. For California, total economic impacts represent the general equilibrium (essentially quantity and price multiplier) effects of lost production in industries upstream and downstream in the supply-chain of sectors that are directly impacted by port cargo disruptions at Port of Los Angeles and Port of Long Beach (POLA/POLB), property damage along the coast, and evacuation of potentially inundated areas. These impacts are estimated to be $2.2 billion from port disruptions, $0.9 billion from property damages, and $2.8 billion from evacuations. Various economic-resilience tactics can potentially reduce the direct and total impacts by 80–85%.
Within the world of mapping, clouds are a pesky interference to be removed from satellite remote sensed imagery. However, to many of us, that is a waste of pixels. Cloud maps are becoming increasingly valuable in the quest to understand land cover change and surface processes. In coastal California, the dynamic summertime interactions between air masses, the ocean, and topography result in blankets of fog and low clouds flowing into low lying areas of the San Francisco Bay Area. The low clouds and fog advected from the Pacific bring moisture and shade to coastal ecosystems. This acts to reduce temperatures and evapotranspiration stress during the otherwise arid Mediterranean climate season, in turn impacting vegetation distribution, irrigation needs, and urban energy consumption.
","language":"English","publisher":"BayGeo","usgsCitation":"Torregrosa, A.A., 2016, @KarlTheFog has been mapped!: BayGEO Journal, v. 9, no. 1, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-075388","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":331423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":331424,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://journal.baygeo.org/karlthefog/"}],"volume":"9","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"584144e0e4b04fc80e5073b0","contributors":{"authors":[{"text":"Torregrosa, Alicia A. 0000-0001-7361-2241 atorregrosa@usgs.gov","orcid":"https://orcid.org/0000-0001-7361-2241","contributorId":3471,"corporation":false,"usgs":true,"family":"Torregrosa","given":"Alicia","email":"atorregrosa@usgs.gov","middleInitial":"A.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":654724,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70177750,"text":"70177750 - 2016 - Best practices in passive remote sensing VNIR hyperspectral system hardware calibrations","interactions":[],"lastModifiedDate":"2016-10-26T15:53:05","indexId":"70177750","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Best practices in passive remote sensing VNIR hyperspectral system hardware calibrations","docAbstract":"Hyperspectral imaging (HSI) is an exciting and rapidly expanding area of instruments and technology in passive remote sensing. Due to quickly changing applications, the instruments are evolving to suit new uses and there is a need for consistent definition, testing, characterization and calibration. This paper seeks to outline a broad prescription and recommendations for basic specification, testing and characterization that must be done on Visible Near Infra-Red grating-based sensors in order to provide calibrated absolute output and performance or at least relative performance that will suit the user’s task. The primary goal of this paper is to provide awareness of the issues with performance of this technology and make recommendations towards standards and protocols that could be used for further efforts in emerging procedures for national laboratory and standards groups.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proc. SPIE 9860, Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2016","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"SPIE","doi":"10.1117/12.2224022","usgsCitation":"Jablonski, J., Durell, C., Slonecker, E.T., Wong, K., Simon, B., Eichelberger, A., and Osterberg, J., 2016, Best practices in passive remote sensing VNIR hyperspectral system hardware calibrations, in Proc. SPIE 9860, Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2016, Article 986004, https://doi.org/10.1117/12.2224022.","productDescription":"Article 986004","ipdsId":"IP-075062","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":330438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5811c0f2e4b0f497e79a5a71","contributors":{"authors":[{"text":"Jablonski, Joseph","contributorId":176106,"corporation":false,"usgs":false,"family":"Jablonski","given":"Joseph","email":"","affiliations":[],"preferred":false,"id":651627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Durell, Christopher","contributorId":176107,"corporation":false,"usgs":false,"family":"Durell","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":651628,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Slonecker, E. Terrence 0000-0002-5793-0503 tslonecker@usgs.gov","orcid":"https://orcid.org/0000-0002-5793-0503","contributorId":168591,"corporation":false,"usgs":true,"family":"Slonecker","given":"E.","email":"tslonecker@usgs.gov","middleInitial":"Terrence","affiliations":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":651626,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wong, Kwok","contributorId":176109,"corporation":false,"usgs":false,"family":"Wong","given":"Kwok","email":"","affiliations":[],"preferred":false,"id":651630,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Simon, Blair","contributorId":176110,"corporation":false,"usgs":false,"family":"Simon","given":"Blair","email":"","affiliations":[],"preferred":false,"id":651631,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eichelberger, Andrew","contributorId":176111,"corporation":false,"usgs":false,"family":"Eichelberger","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":651632,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Osterberg, Jacob","contributorId":176112,"corporation":false,"usgs":false,"family":"Osterberg","given":"Jacob","email":"","affiliations":[],"preferred":false,"id":651633,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70178545,"text":"70178545 - 2016 - Assessing the role of seabirds in the ecology of influenza A viruses","interactions":[],"lastModifiedDate":"2018-07-15T18:33:20","indexId":"70178545","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":948,"text":"Avian Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the role of seabirds in the ecology of influenza A viruses","docAbstract":"Wild waterbirds, specifically waterfowl, gulls, and shorebirds, are recognized as the primordial reservoir of influenza A viruses (IAVs). However, the role of seabirds, an abundant, diverse, and globally distributed group of birds, in the perpetuation and transmission of IAVs is less clear. Here we summarize published and publicly available data for influenza viruses in seabirds, which for the purposes of this study are defined as birds that exhibit a largely or exclusively pelagic lifestyle and exclude waterfowl, gulls, and shorebirds, and we review this collective dataset to assess the role of seabirds in the influenza A ecology. Since 1961, more than 40,000 samples have been collected worldwide from the seabirds considered here and screened, using a variety of techniques, for evidence of active or past IAV infection. From these data, the overall prevalence of active infection has been estimated to be very low; however, serological data provide evidence that some seabird species are more frequently exposed to IAVs. Sequence data for viruses from seabirds are limited, except for murres (common murre, Uria aalge, and thick-billed murre, Uria lomvia; family Alcidae) for which there are full or partial genome sequences available for more than 80 viruses. Characterization of these viruses suggests that murres are infected with Group 1 hemagglutinin subtype viruses more frequently as compared to Group 2 and also indicates that these northern, circumpolar birds are frequently infected by intercontinental reassortant viruses. Greater temporal and spatial sampling and characterization of additional viruses are required to better understand the role of seabirds in global IAV dynamics.
","language":"English","publisher":"American Association of Avian Pathologists","doi":"10.1637/11135-050815-RegR","usgsCitation":"Lang, A.S., Lebarbenchon, C., Robertson, G.J., Ramey, A.M., Waldenstrom, J., and Wille, M., 2016, Assessing the role of seabirds in the ecology of influenza A viruses: Avian Diseases, v. 60, no. 1s, p. 378-386, https://doi.org/10.1637/11135-050815-RegR.","productDescription":"9 p.","startPage":"378","endPage":"386","ipdsId":"IP-065494","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":331238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"1s","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"583d5034e4b0d9329c80c59f","contributors":{"authors":[{"text":"Lang, Andrew S.","contributorId":177028,"corporation":false,"usgs":false,"family":"Lang","given":"Andrew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":654331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lebarbenchon, Camille","contributorId":140670,"corporation":false,"usgs":false,"family":"Lebarbenchon","given":"Camille","email":"","affiliations":[],"preferred":false,"id":654332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":654336,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robertson, Gregory J.","contributorId":173883,"corporation":false,"usgs":false,"family":"Robertson","given":"Gregory","email":"","middleInitial":"J.","affiliations":[{"id":27311,"text":"Wildlife Research Division, Science and Technology Branch, Environment and Climate","active":true,"usgs":false}],"preferred":false,"id":654333,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Waldenstrom, Jonas","contributorId":42891,"corporation":false,"usgs":true,"family":"Waldenstrom","given":"Jonas","email":"","affiliations":[],"preferred":false,"id":654334,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wille, Michelle","contributorId":173881,"corporation":false,"usgs":false,"family":"Wille","given":"Michelle","email":"","affiliations":[{"id":27309,"text":"Memorial University of Newfoundland, St. John’s, NL A1B 3X9, Canada","active":true,"usgs":false}],"preferred":false,"id":654335,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70177914,"text":"70177914 - 2016 - Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA","interactions":[],"lastModifiedDate":"2016-10-26T11:50:49","indexId":"70177914","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA","docAbstract":"Non-native honeybees historically have been managed for crop pollination, however, recent population declines draw attention to pollination services provided by native bees. We applied the InVEST Crop Pollination model, developed to predict native bee abundance from habitat resources, in Maine's wild blueberry crop landscape. We evaluated model performance with parameters informed by four approaches: 1) expert opinion; 2) sensitivity analysis; 3) sensitivity analysis informed model optimization; and, 4) simulated annealing (uninformed) model optimization. Uninformed optimization improved model performance by 29% compared to expert opinion-informed model, while sensitivity-analysis informed optimization improved model performance by 54%. This suggests that expert opinion may not result in the best parameter values for the InVEST model. The proportion of deciduous/mixed forest within 2000 m of a blueberry field also reliably predicted native bee abundance in blueberry fields, however, the InVEST model provides an efficient tool to estimate bee abundance beyond the field perimeter.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2016.01.003","usgsCitation":"Groff, S.C., Loftin, C., Drummond, F., Bushmann, S., and McGill, B.J., 2016, Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA: Environmental Modelling and Software, v. 79, p. 1-9, https://doi.org/10.1016/j.envsoft.2016.01.003.","productDescription":"9 p.","startPage":"1","endPage":"9","ipdsId":"IP-064763","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":488536,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2016.01.003","text":"Publisher Index Page"},{"id":330404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.95294189453125,\n 43.98491011404692\n ],\n [\n -68.95294189453125,\n 44.904523389609324\n ],\n [\n -67.1978759765625,\n 44.904523389609324\n ],\n [\n -67.1978759765625,\n 43.98491011404692\n ],\n [\n -68.95294189453125,\n 43.98491011404692\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"79","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5811c0f2e4b0f497e79a5a6f","chorus":{"doi":"10.1016/j.envsoft.2016.01.003","url":"http://dx.doi.org/10.1016/j.envsoft.2016.01.003","publisher":"Elsevier BV","authors":"Groff Shannon C., Loftin Cynthia S., Drummond Frank, Bushmann Sara, McGill Brian","journalName":"Environmental Modelling & Software","publicationDate":"5/2016","auditedOn":"2/1/2017","publiclyAccessibleDate":"1/29/2017"},"contributors":{"authors":[{"text":"Groff, Shannon C.","contributorId":176308,"corporation":false,"usgs":false,"family":"Groff","given":"Shannon","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":652153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cynthia S. 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":2167,"corporation":false,"usgs":true,"family":"Loftin","given":"Cynthia S.","email":"cyndy_loftin@usgs.gov","affiliations":[],"preferred":true,"id":652139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drummond, Frank","contributorId":176309,"corporation":false,"usgs":false,"family":"Drummond","given":"Frank","email":"","affiliations":[],"preferred":false,"id":652154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bushmann, Sara","contributorId":176310,"corporation":false,"usgs":false,"family":"Bushmann","given":"Sara","email":"","affiliations":[],"preferred":false,"id":652155,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGill, Brian J.","contributorId":146422,"corporation":false,"usgs":false,"family":"McGill","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":652156,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178062,"text":"70178062 - 2016 - Developing fish trophic interaction indicators of climate change for the Great Lakes","interactions":[],"lastModifiedDate":"2016-11-01T15:10:34","indexId":"70178062","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Developing fish trophic interaction indicators of climate change for the Great Lakes","docAbstract":"This project addressed regional climate change effects on aquatic food webs in the Great Lakes. We sought insights by examining Lake Erie as a representative system with a high level of anthropogenic impacts, strong nutrient gradients, seasonal hypoxia, and spatial overlap of cold- and cool-water fish guilds. In Lake Erie and in large embayments throughout the Great Lakes basin, this situation is a concern for fishery managers, as climate change may exacerbate hypoxia and reduce habitat volume for some species. We examined fish community composition, fine-scale distribution, prey availability, diets, and biochemical tracers for dominant fishes from study areas with medium-high nutrient levels (mesotrophic, Fairport study area), and low nutrient levels (oligotrophic, Erie study area). This multi-year database (2011-2013) provides the ability to contrast years with wide variation in rainfall, winter ice-cover, and thermal stratification. In addition, multiple indicators of dietary and distributional responses to environmental variability will allow resource managers to select the most informative approach for addressing specific climate change questions. Our results support the incorporation of some relatively simple and cost-efficient approaches into existing agency monitoring programs to track the near-term condition status of fish and fish community composition by functional groupings. Other metrics appear better suited for understanding longer-term changes, and may take more resources to implement on an ongoing basis. Although we hypothesized that dietary overlap and similarity in selected species would be sharply different during thermal stratification and hypoxic episodes, we found little evidence of this. Instead, to our surprise, this study found that fish tended to aggregate at the edges of hypoxia, highlighting potential spatial changes in catch efficiency of the fishery. This work has had several positive impacts on a wide range of resource management and stakeholder activities, most notably in Lake Erie. The results were instrumental in the development of an interim decision rule for dealing with data collected during hypoxic events to improve stock assessment of Yellow Perch. In addition, novel findings from this study regarding spatial and temporal variability in hypoxia have aided US-Environmental Protection Agency in the development of a modified sampling protocol to more accurately quantify the central basin hypoxic zone, and this directly addressed a goal of the Great Lakes Water Quality Agreement of 2012 to reduce the extent and severity of hypoxia. Finally, the study areas developed in this project formed the basis for food web sampling in the 2014 bi-national Coordinated Science and Monitoring Initiative work in Lake Erie.
","language":"English","publisher":"Northeast Climate Science Center","usgsCitation":"Kraus, R.T., Knight, C.T., Gorman, A.M., Kocovsky, P.M., Weidel, B., and Rogers, M.W., 2016, Developing fish trophic interaction indicators of climate change for the Great Lakes, 70 p.","productDescription":"70 p.","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":330639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":330638,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://necsc.umass.edu/biblio/developing-fish-trophic-interaction-indicators-climate-change-great-lakes"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5819a9c4e4b0bb36a4c91029","contributors":{"authors":[{"text":"Kraus, Richard T. 0000-0003-4494-1841 rkraus@usgs.gov","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":2609,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","email":"rkraus@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":652683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knight, Carey T.","contributorId":56529,"corporation":false,"usgs":true,"family":"Knight","given":"Carey","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":652684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorman, Ann Marie","contributorId":145525,"corporation":false,"usgs":false,"family":"Gorman","given":"Ann","email":"","middleInitial":"Marie","affiliations":[],"preferred":false,"id":652685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":652686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":652687,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rogers, Mark W. 0000-0001-7205-5623 mwrogers@usgs.gov","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":4590,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"mwrogers@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":652688,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70184218,"text":"70184218 - 2016 - Megafloods and Clovis cache at Wenatchee, Washington","interactions":[],"lastModifiedDate":"2017-03-06T11:23:52","indexId":"70184218","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Megafloods and Clovis cache at Wenatchee, Washington","docAbstract":"Immense late Wisconsin floods from glacial Lake Missoula drowned the Wenatchee reach of Washington's Columbia valley by different routes. The earliest debacles, nearly 19,000 cal yr BP, raged 335 m deep down the Columbia and built high Pangborn bar at Wenatchee. As advancing ice blocked the northwest of Columbia valley, several giant floods descended Moses Coulee and backflooded up the Columbia past Wenatchee. Ice then blocked Moses Coulee, and Grand Coulee to Quincy basin became the westmost floodway. From Quincy basin many Missoula floods backflowed 50 km upvalley to Wenatchee 18,000 to 15,500 years ago. Receding ice dammed glacial Lake Columbia centuries more—till it burst about 15,000 years ago. After Glacier Peak ashfall about 13,600 years ago, smaller great flood(s) swept down the Columbia from glacial Lake Kootenay in British Columbia. The East Wenatchee cache of huge fluted Clovis points had been laid atop Pangborn bar after the Glacier Peak ashfall, then buried by loess. Clovis people came five and a half millennia after the early gigantic Missoula floods, two and a half millennia after the last small Missoula flood, and two millennia after the glacial Lake Columbia flood. People likely saw outburst flood(s) from glacial Lake Kootenay.
","language":"English","publisher":"Cambridge University Press","doi":"10.1016/j.yqres.2016.02.007","usgsCitation":"Waitt, R.B., 2016, Megafloods and Clovis cache at Wenatchee, Washington: Quaternary Research, v. 85, no. 3, p. 430-444, https://doi.org/10.1016/j.yqres.2016.02.007.","productDescription":"15 p.","startPage":"430","endPage":"444","ipdsId":"IP-022694","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":336869,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","volume":"85","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-20","publicationStatus":"PW","scienceBaseUri":"58be8338e4b014cc3a3a99e1","contributors":{"authors":[{"text":"Waitt, Richard B. 0000-0002-6392-5604 waitt@usgs.gov","orcid":"https://orcid.org/0000-0002-6392-5604","contributorId":2343,"corporation":false,"usgs":true,"family":"Waitt","given":"Richard","email":"waitt@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":680593,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70192021,"text":"70192021 - 2016 - Reproductive success of Horned Lark and McCown's Longspur in relation to wind energy infrastructure","interactions":[],"lastModifiedDate":"2017-10-25T15:51:49","indexId":"70192021","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Reproductive success of Horned Lark and McCown's Longspur in relation to wind energy infrastructure","docAbstract":"Wind energy is a rapidly expanding industry with potential indirect effects to wildlife populations that are largely unexplored. In 2011 and 2012, we monitored 211 nests of 2 grassland songbirds, Horned Lark (Eremophila alpestris) and McCown's Longspur (Rhynchophanes mccownii), at 3 wind farms and 2 undeveloped reference sites in Wyoming, USA. We evaluated several indices of reproductive investment and success: clutch size, size-adjusted nestling mass, daily nest survival rate, and number of fledglings. We compared reproductive success between wind farms and undeveloped sites and modeled reproductive success within wind farms as a function of wind energy infrastructure and habitat. Size-adjusted nestling mass of Horned Lark was weakly negatively related to turbine density. In 2011, nest survival of Horned Lark decreased 55% as turbine density increased from 10 to 39 within 2 km of the nest. In 2012, however, nest survival of Horned Lark was best predicted by the combination of vegetation height, distance to shrub edge, and turbine density, with survival increasing weakly with increasing vegetation height. McCown's Longspur nest survival was weakly positively related to vegetation density at the nest site when considered with the amount of grassland habitat in the neighborhood and turbine density within 1 km of the nest. Habitat and distance to infrastructure did not explain clutch size or number of fledglings for either species, or size-adjusted nestling mass for McCown's Longspur. Our results suggest that the influence of wind energy infrastructure varies temporally and by species, even among species using similar habitats. Turbine density was repeatedly the most informative measure of wind energy development. Turbine density could influence wildlife responses to wind energy production and may become increasingly important to consider as development continues in areas with high-quality wind resources.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-15-25.1","usgsCitation":"Mahoney, A., and Chalfoun, A.D., 2016, Reproductive success of Horned Lark and McCown's Longspur in relation to wind energy infrastructure: The Condor, v. 118, no. 2, p. 360-375, https://doi.org/10.1650/CONDOR-15-25.1.","productDescription":"16 p.","startPage":"360","endPage":"375","ipdsId":"IP-064787","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":482075,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-15-25.1","text":"Publisher Index Page"},{"id":347413,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"118","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a2a7e4b0220bbd9d9f7c","contributors":{"authors":[{"text":"Mahoney, Anika","contributorId":198389,"corporation":false,"usgs":false,"family":"Mahoney","given":"Anika","email":"","affiliations":[],"preferred":false,"id":715909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chalfoun, Anna D. achalfoun@usgs.gov","contributorId":3735,"corporation":false,"usgs":true,"family":"Chalfoun","given":"Anna","email":"achalfoun@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":713852,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192507,"text":"70192507 - 2016 - Trends in pesticide use on soybean, corn and cotton since the introduction of major genetically modified crops in the United States","interactions":[],"lastModifiedDate":"2017-10-26T10:27:36","indexId":"70192507","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3035,"text":"Pest Management Science","active":true,"publicationSubtype":{"id":10}},"title":"Trends in pesticide use on soybean, corn and cotton since the introduction of major genetically modified crops in the United States","docAbstract":"BACKGROUND
Genetically modified (GM) varieties of soybean, corn and cotton have largely replaced conventional varieties in the United States. The most widely used applications of GM technology have been the development of crops that are resistant to a specific broad-spectrum herbicide (primarily glyphosate) or that produce insecticidal compounds within the plant itself. With the widespread adoption of GM crops, a decline in the use of conventional pesticides was expected.
RESULTS
There has been a reduction in the annual herbicide application rate to corn since the advent of GM crops, but the herbicide application rate is mostly unchanged for cotton. Herbicide use on soybean has increased. There has been a substantial reduction in the amount of insecticides used on both corn and cotton since the introduction of GM crops.
CONCLUSIONS
The observed changes in pesticide use are likely to be the result of many factors, including the introduction of GM crops, regulatory restrictions on some conventional pesticides, introduction of new pesticide technologies and changes in farming practices. In order to help protect human and environmental health and to help agriculture plan for the future, more detailed and complete documentation on pesticide use is needed on a frequent and ongoing basis.
","language":"English","publisher":"Wiley","doi":"10.1002/ps.4082","usgsCitation":"Coupe, R.H., and Capel, P.D., 2016, Trends in pesticide use on soybean, corn and cotton since the introduction of major genetically modified crops in the United States: Pest Management Science, v. 72, no. 5, p. 1013-1022, https://doi.org/10.1002/ps.4082.","productDescription":"10 p.","startPage":"1013","endPage":"1022","ipdsId":"IP-066541","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":347436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"72","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-10","publicationStatus":"PW","scienceBaseUri":"5a07ea42e4b09af898c8cc70","contributors":{"authors":[{"text":"Coupe, Richard H. 0000-0001-8679-1015 rhcoupe@usgs.gov","orcid":"https://orcid.org/0000-0001-8679-1015","contributorId":551,"corporation":false,"usgs":true,"family":"Coupe","given":"Richard","email":"rhcoupe@usgs.gov","middleInitial":"H.","affiliations":[{"id":394,"text":"Mississippi Water Science Center","active":true,"usgs":true}],"preferred":true,"id":716096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Capel, Paul D. 0000-0003-1620-5185 capel@usgs.gov","orcid":"https://orcid.org/0000-0003-1620-5185","contributorId":1002,"corporation":false,"usgs":true,"family":"Capel","given":"Paul","email":"capel@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":716095,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70170937,"text":"70170937 - 2016 - Assessing predation risks for small fish in a large river ecosystem between contrasting habitats and turbidity conditions","interactions":[],"lastModifiedDate":"2016-05-11T14:06:47","indexId":"70170937","displayToPublicDate":"2016-04-30T15:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":737,"text":"American Midland Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Assessing predation risks for small fish in a large river ecosystem between contrasting habitats and turbidity conditions","docAbstract":"This study examined predation risk for juvenile native fish between two riverine shoreline habitats, backwater and debris fan, across three discrete turbidity levels (low, intermediate, high) to understand environmental risks associated with habitat use in a section of the Colorado River in Grand Canyon, AZ. Inferences are particularly important to juvenile native fish, including the federally endangered humpback chub Gila cypha. This species uses a variety of habitats including backwaters which are often considered important rearing areas. Densities of two likely predators, adult rainbow trout Oncorhynchus mykiss and adult humpback chub, were estimated between habitats using binomial mixture models to examine whether higher predator density was associated with patterns of predation risk. Tethering experiments were used to quantify relative predation risk between habitats and turbidity conditions. Under low and intermediate turbidity conditions, debris fan habitat showed higher relative predation risk compared to backwaters. In both habitats the highest predation risk was observed during intermediate turbidity conditions. Density of likely predators did not significantly differ between these habitats. This information can help managers in Grand Canyon weigh flow policy options designed to increase backwater availability or extant turbidity conditions.
","language":"English","publisher":"University of Notre Dame","publisherLocation":"Notre Dame, IN","doi":"10.1674/0003-0031-175.2.206","usgsCitation":"Dodrill, M.J., Yard, M.D., and Pine, W.E., 2016, Assessing predation risks for small fish in a large river ecosystem between contrasting habitats and turbidity conditions: American Midland Naturalist, v. 175, no. 2, p. 206-221, https://doi.org/10.1674/0003-0031-175.2.206.","productDescription":"16 p.","startPage":"206","endPage":"221","numberOfPages":"16","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-044798","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":321128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.04907226562499,\n 35.545635932499415\n ],\n [\n -114.04907226562499,\n 36.99377838872517\n ],\n [\n -111.2860107421875,\n 36.99377838872517\n ],\n [\n -111.2860107421875,\n 35.545635932499415\n ],\n [\n -114.04907226562499,\n 35.545635932499415\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"175","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"573457abe4b0dae0d5ddd2e4","contributors":{"authors":[{"text":"Dodrill, Michael J. 0000-0002-7038-7170 mdodrill@usgs.gov","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":5468,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","email":"mdodrill@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":629170,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":169281,"corporation":false,"usgs":true,"family":"Yard","given":"Michael","email":"myard@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":629169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pine, William E. III","contributorId":139959,"corporation":false,"usgs":false,"family":"Pine","given":"William","suffix":"III","email":"","middleInitial":"E.","affiliations":[{"id":13332,"text":"Uni. of Florida Department of Wildlife Ecology and Conservation","active":true,"usgs":false}],"preferred":false,"id":629171,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70170957,"text":"70170957 - 2016 - Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria","interactions":[],"lastModifiedDate":"2018-08-09T12:12:53","indexId":"70170957","displayToPublicDate":"2016-04-30T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria","docAbstract":"Using satellite imagery to quantify the spatial patterns of cyanobacterial toxins has several challenges. These challenges include the need for surrogate pigments – since cyanotoxins cannot be directly detected by remote sensing, the variability in the relationship between the pigments and cyanotoxins – especially microcystins (MC), and the lack of standardization of the various measurement methods. A dual-model strategy can provide an approach to address these challenges. One model uses either chlorophyll-a (Chl-a) or phycocyanin (PC) collected in situ as a surrogate to estimate the MC concentration. The other uses a remote sensing algorithm to estimate the concentration of the surrogate pigment. Where blooms are mixtures of cyanobacteria and eukaryotic algae, PC should be the preferred surrogate to Chl-a. Where cyanobacteria dominate, Chl-a is a better surrogate than PC for remote sensing. Phycocyanin is less sensitive to detection by optical remote sensing, it is less frequently measured, PC laboratory methods are still not standardized, and PC has greater intracellular variability. Either pigment should not be presumed to have a fixed relationship with MC for any water body. The MC-pigment relationship can be valid over weeks, but have considerable intra- and inter-annual variability due to changes in the amount of MC produced relative to cyanobacterial biomass. To detect pigments by satellite, three classes of algorithms (analytic, semi-analytic, and derivative) have been used. Analytical and semi-analytical algorithms are more sensitive but less robust than derivatives because they depend on accurate atmospheric correction; as a result derivatives are more commonly used. Derivatives can estimate Chl-a concentration, and research suggests they can detect and possibly quantify PC. Derivative algorithms, however, need to be standardized in order to evaluate the reproducibility of parameterizations between lakes. A strategy for producing useful estimates of microcystins from cyanobacterial biomass is described, provided cyanotoxin variability is addressed.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.hal.2016.01.005","usgsCitation":"Stumpf, R.P., Davis, T.W., Wynne, T.T., Graham, J., Loftin, K.A., Johengen, T., Gossiaux, D., Palladino, D., and Burtner, A., 2016, Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria: Harmful Algae, v. 54, p. 160-173, https://doi.org/10.1016/j.hal.2016.01.005.","productDescription":"14 p.","startPage":"160","endPage":"173","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070534","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":471038,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hal.2016.01.005","text":"Publisher Index Page"},{"id":321204,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5736faade4b0dae0d5e03bf4","contributors":{"authors":[{"text":"Stumpf, Rick P","contributorId":169288,"corporation":false,"usgs":false,"family":"Stumpf","given":"Rick","email":"","middleInitial":"P","affiliations":[{"id":6637,"text":"National Oceanic and Atmospheric Administration, Northwest Fisheries Science Center, 2725 Montlake Blvd E, Seattle, WA 98112","active":true,"usgs":false}],"preferred":false,"id":629218,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Timothy W.","contributorId":169289,"corporation":false,"usgs":false,"family":"Davis","given":"Timothy","email":"","middleInitial":"W.","affiliations":[{"id":6637,"text":"National Oceanic and Atmospheric Administration, Northwest Fisheries Science Center, 2725 Montlake Blvd E, Seattle, WA 98112","active":true,"usgs":false}],"preferred":false,"id":629219,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wynne, Timothy T.","contributorId":169290,"corporation":false,"usgs":false,"family":"Wynne","given":"Timothy","email":"","middleInitial":"T.","affiliations":[{"id":6637,"text":"National Oceanic and Atmospheric Administration, Northwest Fisheries Science Center, 2725 Montlake Blvd E, Seattle, WA 98112","active":true,"usgs":false}],"preferred":false,"id":629220,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":150737,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer L.","email":"jlgraham@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":629217,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loftin, Keith A. 0000-0001-5291-876X kloftin@usgs.gov","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":868,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","email":"kloftin@usgs.gov","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":629221,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johengen, T.H.","contributorId":169291,"corporation":false,"usgs":false,"family":"Johengen","given":"T.H.","affiliations":[{"id":25465,"text":"Cooperative Institute for Limnology and Ecosystem Research","active":true,"usgs":false}],"preferred":false,"id":629222,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gossiaux, D.","contributorId":169292,"corporation":false,"usgs":false,"family":"Gossiaux","given":"D.","affiliations":[{"id":25466,"text":"National Oceanic and Atmostpheric Administration","active":true,"usgs":false}],"preferred":false,"id":629223,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Palladino, D.","contributorId":169293,"corporation":false,"usgs":false,"family":"Palladino","given":"D.","email":"","affiliations":[{"id":25465,"text":"Cooperative Institute for Limnology and Ecosystem Research","active":true,"usgs":false}],"preferred":false,"id":629224,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Burtner, A.","contributorId":169294,"corporation":false,"usgs":false,"family":"Burtner","given":"A.","email":"","affiliations":[{"id":25465,"text":"Cooperative Institute for Limnology and Ecosystem Research","active":true,"usgs":false}],"preferred":false,"id":629225,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70169062,"text":"sim3351 - 2016 - Geologic map of the Valdez D-1 and D-2 quadrangles (Mount Wrangell Volcano), Alaska","interactions":[],"lastModifiedDate":"2018-06-20T19:49:59","indexId":"sim3351","displayToPublicDate":"2016-04-29T15:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3351","title":"Geologic map of the Valdez D-1 and D-2 quadrangles (Mount Wrangell Volcano), Alaska","docAbstract":"Mount Wrangell (elev. 4,317 m) is the youngest and only active volcano in the Oligocene to Holocene-aged Wrangell volcanic field that extends from beyond the Alaska-Yukon border northwest through the Wrangell Mountains to the Copper River Basin. The volcano is a very large (900 km3) broad shield containing an ice-filled, nonexplosive, collapse caldera measuring 3.2 by 5.6 kilometers. Three known craters, the West, North, and East occur along the north and west margins of the caldera; the caldera is open to the southeast. The volcano is best exposed on its southwest flank (this map area) where a number of deep glaciated canyons cut through hundreds of meters of shield lava flows creating routes for younger, valley-filling lava flows. The shield extends north into the Gulkana A-1 quadrangle, northeast into the Nabesna A-6 quadrangle, and east into the McCarthy quadrangle where it is almost entirely covered by ice. The present extent of the Mount Wrangell shield showing the entire caldera and locations of the three summit craters is depicted in figure 1.
\nMount Wrangell was built rapidly beginning about 650 ka by the outpourings of hundreds of voluminous lava flows from a vent, or vents, apparently in the present summit area. By 200 ka to 300 ka, activity waned and only an occasional lava flow coursed down the glacially carved valleys radiating from the summit or flowed over the upper summit area above the heads of the glacial valleys. The youngest dated valley-fill lava flow is approximately 25,000 years old; one or two undated flows may be younger.
\nIn historical times there have been several reports of lava flows issuing from the summit area. The most reliable and convincing of these were two independent observations from Copper Center, Alaska on September 3, 1899 that described great earth movements (the 1899 Yakutat Bay earthquake) followed by an eruption at Mount Wrangell’s summit, consisting of vigorous ash emission and flowing lava on the volcano’s northwest flank. This eruptive activity apparently continued for several years after the earthquake, as a photo taken around 1901–02 shows a large part of Mount Wrangell’s summit blanketed by ash. During this study, no evidence of young lava flows in the region were found, although it is very possible that a small-volume flow could be entirely hidden by snow and ice in the 100 years since the event. However, abundant juvenile andesitic pumice was found on the upper Chetaslina Glacier, strongly supporting a very young pyroclastic eruption.
\nIn addition to the 1899–1902 eruptions there have been accounts of strong ash-producing activity on at least four different occasions: 1912, July 3, 1921, April 6, 1930, and February 20, 1982. Of these, the 1921 activity was the most spectacular, and possibly erupted from the northeast side of the summit caldera.
\nPresent activity is limited to fumaroles in North and West Crater at the summit, at the summit ridge near East Crater, and at two localities at an elevation of 3,657 m on the southwest flank. The summit fumaroles frequently give rise to visible steam plumes, and occasionally sporadic explosive phreatic activity in North and West Crater will put a thin dusting of ash on the summit ice.
\nThis study was directed toward Mount Wrangell volcano and the older Wrangell volcanic field rocks that underlie the volcano. These older lavas include the Chetaslina lavas (867 ka–1,650 ka) and a basaltic andesite–dacite center (1,590 ka–1,640 ka) whose source areas are not well defined. Older Paleozoic and Mesozoic sedimentary, igneous, and metamorphic rocks of the Wrangellia terrane underlie the entire Wrangell volcanic field.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3351","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Richter, D.H., McGimsey, R.G., Labay, K.A., Lanphere, M.A., Moore, R.B., Nye, C.J., Rosenkrans, D.S., and Winkler, G.R., 2016, Geologic map of the Valdez D-1 and D-2 quadrangles (Mount Wrangell Volcano), Alaska: U.S. Geological Survey Scientific Investigations Map 3351, 20 p., scale 1:63,360, https://dx.doi.org/10.3133/sim3351.","productDescription":"Pamphlet: iii, 20 p.; Sheet: 45.54 x 28.53 inches; Metadata: FAQ, html, txt, xml; Read Me; Databases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-061459","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":318874,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3351/sim3351_readme.pdf","text":"Read Me","size":"22 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3351 Read Me"},{"id":318866,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3351/coverthb.jpg"},{"id":318873,"rank":4,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3351/sim3351_databases.zip","text":"Databases","size":"5.2 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3351 Databases"},{"id":318867,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3351/sim3351_sheet1.pdf","text":"Sheet 1","size":"49.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3351 Sheet 1"},{"id":318868,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3351/sim3351_pamphlet.pdf","text":"Pamphlet","size":"880 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3351 Pamphlet"},{"id":318869,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3351/sim3351_meta.html","text":"Metadata (html)","size":"58 KB","linkFileType":{"id":5,"text":"html"},"description":"SIM 3351 Metadata (html)"},{"id":318870,"rank":8,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3351/sim3351_meta.txt","text":"Metadata (txt)","size":"41 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3351 Metadata (txt)"},{"id":318871,"rank":9,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3351/sim3351_meta.xml","text":"Metadata (xml)","size":"38 KB","description":"SIM 3351 Metadata (xml)"},{"id":318872,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3351/sim3351_meta.faq.html","text":"Metadata FAQ","size":"33 KB","linkFileType":{"id":5,"text":"html"},"description":"SIM 3351 Metadata FAQ"}],"country":"United States","state":"Alaska","otherGeospatial":"Mount Wrangell Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -144.75,\n 62\n ],\n [\n -144.75,\n 61.75\n ],\n [\n -144,\n 61.75\n ],\n [\n -144,\n 62\n ],\n [\n -144.75,\n 62\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Staff, Alaska Science Center
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Science Center
Alaska Mineral Resources
Continuing coral-reef degradation in the western Atlantic is resulting in loss of ecological and geologic functions of reefs. With the goal of assisting resource managers and stewards of reefs in setting and measuring progress toward realistic goals for coral-reef conservation and restoration, we examined reef degradation in this region from a geological perspective. The importance of ecosystem services provided by coral reefs—as breakwaters that dissipate wave energy and protect shorelines and as providers of habitat for innumerable species—cannot be overstated. However, the few coral species responsible for reef building in the western Atlantic during the last approximately 1.5 million years are not thriving in the 21st century. These species are highly sensitive to abrupt temperature extremes, prone to disease infection, and have low sexual reproductive potential. Their vulnerability and the low functional redundancy of branching corals have led to the low resilience of western Atlantic reef ecosystems. The decrease in live coral cover over the last 50 years highlights the need for study of relict (senescent) reefs, which, from the perspective of coastline protection and habitat structure, may be just as important to conserve as the living coral veneer. Research is needed to characterize the geological processes of bioerosion, reef cementation, and sediment transport as they relate to modern-day changes in reef elevation. For example, although parrotfish remove nuisance macroalgae, possibly promoting coral recruitment, they will not save Atlantic reefs from geological degradation. In fact, these fish are quickly nibbling away significant quantities of Holocene reef framework. The question of how different biota covering dead reefs affect framework resistance to biological and physical erosion needs to be addressed. Monitoring and managing reefs with respect to physical resilience, in addition to ecological resilience, could optimize the expenditure of resources in conserving Atlantic reefs and the services they provide.
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Sanitary and combined sanitary and storm sewer networks convey wastewater to centralized treatment plants. Impervious surfaces, which include roads, parking lots, and buildings, increase stormwater runoff and the efficiency by which runoff is conveyed to nearby stream channels; therefore, impervious surfaces increase the risk of urban flooding and alteration of natural ecosystems. These challenges will increase with the expansion of urban centers and the probable effects of climate change on precipitation patterns. Understanding the urban-water cycle is critical to effectively manage water resources and to protect people, infrastructure, and urban-stream ecosystems. As a leader in water-supply, wastewater, and stormwater assessments, the U.S. Geological Survey has the expertise and resources needed to monitor, model, and interpret data related to the urban-water cycle and thereby enable water-resources managers to make informed decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163025","collaboration":"Northeast Region Urban Landscape Capabilities Team","usgsCitation":"U.S. Geological Survey, 2016, Urban infrastructure and water management—Science capabilities of the U.S. Geological Survey: U.S. Geological Survey Fact Sheet 2016–3025, 2 p., https://dx.doi.org/10.3133/fs20163025.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074177","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":320439,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3025/fs20163025.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3025"},{"id":320438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3025/coverthb.jpg"},{"id":320440,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163023","text":"Fact Sheet 2016-3023"},{"id":320441,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163024","text":"Fact Sheet 2016-3024"},{"id":320442,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163026","text":"Fact Sheet 2016-3026"}],"contact":"U.S. Geological Survey
Northeast Region Urban Landscapes Capability Team
Email: GS-NE_ULCT@usgs.gov
Urban development creates multiple stressors that can degrade stream ecosystems by changing stream hydrology, water quality, and physical habitat. Contaminants, habitat destruction, and increasing streamflow variability resulting from urban development have been associated with the disruption of biological communities, particularly the loss of sensitive aquatic biota. Understanding how algal, invertebrate, and fish communities respond to these physical and chemical stressors can provide important clues as to how streams should be managed to protect stream ecosystems as a watershed becomes increasingly urbanized. The U.S. Geological Survey continues to lead monitoring efforts and scientific studies on the effects of urban development on stream ecosystems in metropolitan areas across the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163026","collaboration":"Northeast Region Urban Landscape Capabilities Team","usgsCitation":"U.S. Geological Survey, 2016, Urban development and stream ecosystem health—Science capabilities of the U.S. Geological Survey: U.S. Geological Survey Fact Sheet 2016–3026, 2 p., https://dx.doi.org/10.3133/fs20163026.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074179","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":320444,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3026/fs20163026.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3026"},{"id":320445,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163023","text":"Fact Sheet 2016-3023"},{"id":320443,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3026/coverthb.jpg"},{"id":320446,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163024","text":"Fact Sheet 2016-3024"},{"id":320447,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163025","text":"Fact Sheet 2016-3025"}],"contact":"U.S. Geological Survey
Northeast Region Urban Landscapes Capability Team
Email: GS-NE_ULCT@usgs.gov
Urbanization affects streamflow characteristics, coastal flooding, and groundwater recharge. Increasing impervious areas, streamflow diversions, and groundwater pumpage are some of the ways that the natural water cycle is affected by urbanization. Assessment of the relations among these factors and changes in land use helps water-resource managers with issues such as stormwater management and vulnerability to flood and drought. Scientists with the U.S. Geological Survey (USGS) have the expertise to monitor and model urban hydrologic systems. Streamflow and groundwater data are available in national databases, and analyses of these data, including identification of long-term streamflow trends and the efficacy of management practices, are published in USGS reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163023","collaboration":"Northeast Region Urban Landscape Capabilities Team ","usgsCitation":"U.S. Geological Survey, 2016, Urban hydrology—Science capabilities of the U.S. Geological Survey: U.S. Geological Survey Fact Sheet 2016–3023, 2 p., https://dx.doi.org/10.3133/fs20163023.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074174","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":320426,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163025","text":"Fact Sheet 2016-3025","description":"FS 2016-3023"},{"id":320425,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163024","text":"Fact Sheet 2016-3024","description":"FS 2016-3023"},{"id":320423,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3023/coverthb.jpg"},{"id":320427,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163026","text":"Fact Sheet 2016-3026","description":"FS 2016-3023"},{"id":320424,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3023/fs20163023.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3023"}],"contact":"U.S. Geological Survey
Northeast Region Urban Landscapes Capability Team
Email: GS-NE_ULCT@usgs.gov
Streams and estuaries with urban watersheds commonly exhibit increased streamflow and decreased base flow; diminished stream-channel stability; excessive amounts of contaminants such as pesticides, metals, industrial and municipal waste, and combustion products; and alterations to biotic community structure. Collectively, these detrimental effects have been termed the “urban-stream syndrome.” Water-resource managers seek to lessen the effects on receiving water bodies of new urban development and remediate the effects in areas of existing urbanization. Similarly, the scientific community has produced extensive research on these topics, with researchers from the U.S. Geological Survey (USGS) leading many studies of urban streams and the processes responsible for the urban-stream syndrome. Increasingly, USGS studies are evaluating the effects of management and restoration activities to better understand how urban waters respond to the implementation of management practices. The USGS has expertise in collecting and interpreting data for many physical, chemical, and ecological processes in urban waters and, thus, provides holistic assessments to inform managers of urban water resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163024","collaboration":"Northeast Region Urban Landscape Capabilities Team","usgsCitation":"U.S. Geological Survey, 2016, Contaminants in urban waters—Science capabilities of the U.S. Geological Survey: U.S. Geological Survey Fact Sheet 2016–3024, 2 p., https://dx.doi.org/10.3133/fs20163024.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074176","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":320432,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3024/coverthb.jpg"},{"id":320433,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3024/fs20163024.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3024"},{"id":320434,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163023","text":"Fact Sheet 2016-3023","description":"FS 2016-3024"},{"id":320435,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163025","text":"Fact Sheet 2016-3025","description":"FS 2016-3024"},{"id":320436,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163026","text":"Fact Sheet 2016-3026","description":"FS 2016-3024"}],"contact":"U.S. Geological Survey
Northeast Region Urban Landscapes Capability Team
Email: GS-NE_ULCT@usgs.gov
Evidence suggests that lakes are important sites for atmospheric CO2 exchange and so play a substantial role in the global carbon budget. Previous research has 2 weaknesses: (1) most data have been collected only during the open-water or summer seasons, and (2) data are concentrated principally on natural lakes in northern latitudes. Here, we report on the full annual cycle of atmospheric CO2 exchanges of 15 oligotrophic to eutrophic reservoirs in the Glacial Till Plains of the United States. With one exception, these reservoirs showed an overall loss of CO2 during the year, with most values within the lower range reported for temperate lakes. There was a strong cross-system seasonal pattern: an average of 70% of total annual CO2 efflux occurred by the end of spring mixis; some 20% of annual flux was reabsorbed during summer stratification; and the remaining 50% of efflux was lost during autumnal mixing. Net annual flux was negatively correlated with depth and positively correlated with both water residence time and DOC, with the smallest annual CO2 efflux measured in shallow fertile impoundments. Strong correlations yield relationships allowing regional up-scaling of CO2 evasion. Understanding lacustrine CO2 uptake and evasion requires seasonal analyses across the full range of lake trophic states and morphometric attributes.
","language":"English","publisher":"International Society of Limnology","doi":"10.5268/IW-6.2.982","usgsCitation":"Jones, J., Obrecht, D.V., Graham, J., Balmer, M.B., Filstrup, C.T., and Downing, J.A., 2016, Seasonal patterns in carbon dioxide in 15 mid-continent (USA) reservoirs: Inland Waters, v. 2, no. 6, p. 265-272, https://doi.org/10.5268/IW-6.2.982.","productDescription":"8 p.","startPage":"265","endPage":"272","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074423","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":471040,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5268/iw-6.2.982","text":"Publisher Index Page"},{"id":320803,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","county":"Adair County, Chariton County, Harrison County, Linn County, Mercer County, Nodaway 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2008","interactions":[],"lastModifiedDate":"2016-04-28T13:28:53","indexId":"ds991","displayToPublicDate":"2016-04-28T13:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"991","title":"Baseline coastal oblique aerial photographs collected from Calcasieu Lake, Louisiana, to Brownsville, Texas, September 9-10, 2008","docAbstract":"The U.S. Geological Survey (USGS), as part of the National Assessment of Coastal Change Hazards project, conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms (Morgan, 2009). On September 9-10, 2008, the USGS conducted an oblique aerial photographic survey from Calcasieu Lake, Louisiana, to Brownsville, Texas, aboard a Cessna C-210 (aircraft) at an altitude of 500 feet (ft) and approximately 1,000 ft offshore. This mission was flown to collect baseline data for assessing incremental changes of the beach and nearshore area, and the data can be used in the assessment of future coastal change.
The photographs provided in this report are Joint Photographic Experts Group (JPEG) images. ExifTool was used to add the following to the header of each photo: time of collection, Global Positioning System (GPS) latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. The photograph locations are an estimate of the position of the aircraft at the time the photograph was taken and do not indicate the location of any feature in the images (see the Navigation Data page). These photographs document the state of the barrier islands and other coastal features at the time of the survey. Pages containing thumbnail images of the photographs, referred to as contact sheets, were created in 5-minute segments of flight time. These segments can be found on the Photos and Maps page. Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet.
In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML file was created using the photographic navigation files. The KML file can be found in the kml folder.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds991","usgsCitation":"Morgan, K.L.M., and Westphal, K.A., 2016, Baseline coastal oblique aerial photographs collected from Calcasieu Lake, Louisiana, to Brownsville, Texas, September 9-10, 2008: U.S. Geological Survey Data Series 991, https://dx.doi.org/10.3133/ds991.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2008-09-09","ipdsId":"IP-072328","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":320621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":320527,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0991"}],"country":"United States","state":"Louisiana, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.657470703125,\n 25.849336891707605\n ],\n [\n -97.657470703125,\n 30.221101852485987\n ],\n [\n -92.63671875,\n 30.221101852485987\n ],\n [\n -92.63671875,\n 25.849336891707605\n ],\n [\n -97.657470703125,\n 25.849336891707605\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
(727) 502–8000
http://coastal.er.usgs.gov
The U.S. Geological Survey (USGS), as part of the National Assessment of Coastal Change Hazards project, conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms (Morgan, 2009). On September 14-15, 2008, the USGS conducted an oblique aerial photographic survey along the Alabama, Mississippi, and Louisiana barrier islands and the north Texas coast, aboard a Beechcraft Super King Air 200 (aircraft) at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was flown to collect post-Hurricane Ike data for assessing incremental changes in the beach and nearshore area since the last survey, flown on September 9-10, 2008, and the data can be used in the assessment of future coastal change.
The photographs provided in this report are Joint Photographic Experts Group (JPEG) images. ExifTool was used to add the following to the header of each photo: time of collection, Global Positioning System (GPS) latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. The photograph locations are an estimate of the position of the aircraft at the time the photograph was taken and do not indicate the location of any feature in the images (see the Navigation Data page). These photographs document the state of the barrier islands and other coastal features at the time of the survey. Pages containing thumbnail images of the photographs, referred to as contact sheets, were created in 5-minute segments of flight time. These segments can be found on the Photos and Maps page. Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet.
In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML file was created using the photographic navigation files. The KML file can be found in the kml folder.
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600 4th Street South
St. Petersburg, FL 33701
(727) 502–8000
http://coastal.er.usgs.gov
No abstract available.
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